Pllaninc transport in Egypt ne MATLAB 2012.pdf

Tanta UniversityFaculty of Engineering

Public Works Engineering Department

Transportation Planning Process forUrban Areas - Case Study Tanta City .

By

Ahmed Mohamed Abd Elhamed Alkafoury .

Demonstrator in Public Works Engineering Department . Faculty of Engineering, Tanta University , Tanta, Egypt.

B.Sc. of Structural Engineering. (2004)

A ThesisSubmitted for the Fulfillment of the Requirements for

the Degree of the Master of Science in Civil Engineering (Public Works Engineering) .

Under the supervision of

Prof. Dr. Mohamed Hafez Fahmy

Prof. of Railway and Transportation Engineering.Head of Transportation Eng. Dept.

Faculty of Engineering, Alexandria University,Alexandria, Egypt.

Prof. Dr. Mohamed El-ShabrawyMohamed Ali

Prof. of Highway and Traffic Engineering. Public works Eng. Dept.

Faculty of Engineering, El-MansouraUniversity, El-Mansoura, Egypt.

Ass. Prof. Dr. Hafez Abbas Afify

Public Works Eng. Dept.Faculty of Engineering, Tanta University, Tanta, Egypt.

2012

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AbstractThe high growth rate of population, income and vehicle ownership in

developing countries urban areas, accompanied with problems of

congestion, pollution and accidents, raise s the essentiality for efficient

planning of transport systems. The need is not to provide bigger roads to

cope with more vehicles, but to re -plan of the study area according to

specified transportation models.

The subject of transport planning, urban transport planning in particular, is

the understanding of these problems, formulating safe and sustainable

efficient solutions and managing the whole transportation system to provide

an adequate system and involve broad interaction with many other

disciplines. Achieving long-term, medium and short-term solutions to the

overall problems of traffic and transport, requires applying scientific

methods of transportation planning and traffic engineering supported by

voluminous amount of information and data about the urban transportation

system and its interrelationships, and aided by computers to deal with this

complex data to be processed and analyzed.

In most developing countries like Egypt, general lack of adequately current

or relevant demographic and socio-economic data and sometime inaccurate

statistics, makes the transportation planning using known planning software

difficult and may lead to inaccurate results. In these cases, the planner must

develop his own transportation model , which describes the transportation

system in the region.

The main aim of this research is to define the urban transport planning

process in a developing area, namely Tanta city (Egypt) as a case study. A

computer program (UTPP-TC: Urban Transport Planning Program for Tanta

City) has been built using MATLAB programming process. The program

uses the four steps transportation models; Trip generation and attraction, trip

distribution, modal split and trip assignment. The program also can evaluate

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the operational and environmental situation of the road network in the study

area.

Different scenarios to improve urban transport systems in Tanta city have

been investigated. This includes: a do nothing scenario, a public transport

scenario and an LRT (Light Rail Transit) scenario. Also, the research

determines how efficiency each scenario performed on the study area.

The result of this research indicated that, the LRT scenario is the most

acceptable solution to solve traffic congestion and problems in the study

area. It leads to improve the level of service of main roads in Tanta city.

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ACKNOWLEDGEMENT

First, praise and thanks be to Allah for giving me the potential to do this

work, and ask him to increase His Grace and Generosity.

There is no way to convey the magnitude of my debt to my advisor

professor Dr. Mohamed Hafez Fahmy , who, since I knew him, has been my

inspiration and role model for how to do a good research. He has always

given me good advice, ideas and direction while still allowing me to go my

own way. Professor Dr. Mohamed Elshabrawy Ali, who always gave me the

support with information and knowledge in the field of the research I am

incredibly fortunate to have had him as an advisor all these years.

I would like to express my sincere gratitude to Dr. Hafez Abbas Afify who

always stands beside me, and gives me the su pport at the time I really in

need, since I were an under graduate student , till now.

Next, I would like to thank my family, especially m y parents who have

offered loving support and thought me the joy of finding things out. They

have always supported me in whatever strange place I find my self, and have

given me the confidence to muddle my way out knowing that I am not alone.

I would like to thank my kind wife, who played a k ey role in performing

this work she has always encouraged me to do a good re search. Thanks

forever and ever for my wife, who probably does not even know how much

her support has meant to me during my study.

This work is dedicated to my son Zyad, who really represents all my life.

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Finally, this work is also dedicated to all Egyptian martyrs of 25 January

2011 Revolution.

Ahmed Mohamed Abd Elhamed Alkafoury .

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CONTENTS

Page

ABSTRACT……………………………………………………….. i

ACKNOWLEDGMENTS.………………………………………... iii

CONTENTS……………………………………………………….. v

LIST OF TABLES………………………………………………… xi

LIST OF FIGURES ……………………………….………............ xv

CHAPTER 1: OBJECTIVES AND METHODOLOGY OF

THE RESEARCH

1.1 INTRODUCTION…………………………………………… 1

1.2 OBJECTIVES OF THE RESEARC H…….………...……….. 2

1.3 METHODOLOGY OF THE RESEARCH…..…….. ……….. 2

1.4 OUTLINES OF THE RESEARCH………………………….. 4

DEFINITION OF URBAN TRANSPORTATION:2CHAPTERPROCESSPLANNING

2.1 INTRODUCTION…………………… ……………………… 5

2.2 URBAN TRANSPORTATION PLANNING ……….………. 5

2.3 TRAVEL DEMAND MODELING …………………………. 8

2.3.1 Four-stages travel demand mode …………………. 8

2.3.2 Simultaneous or direct demand formulation ………. 16

2.4SUSTAINABILITY IN URBAN TRA NSPORTATION

PLANNING …………….........................................................17

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CHAPTER 3: SOFTWARE USED FOR URBANTRANSPORTATION PLANNING PROCESS3.1 INTRODUCTION…………………………………………. . 18

3.2 TRANSPORTATION PLANNING SOFTWARE …………. 18

3.2.1 EMME/2………………………….………………... 19

3.2.2 QRS II ……………………………….……… ……. 20

3.2.3 TRANPLAN ……………...………………………. 21

3.2.4 HCS ……………………………………………….. 22

3.2.5 VISUM AND VISSIM ………...………………….. 22

3.2.6 TransCAD………………….……………………… 25

3.3 COMPARISON OF SOFTWARE USED FOR URBAN

TRANSPORTATION PLANNING 25

3.4 URBAN TRANSPORTATION PLANNING SOFTWARE

FOR DEVELOPING COUNTRIES 31

CHAPTER 4: PROPOSED PROGRAM FOR URBANTRANSPORTATION PLANNING PROCESS IN DEVELOPINGAREAS4.1 INTRODUCTION……………………………………….…... ........ 32

4.2 STRUCTURE AND COMPONENTS OF THE PROPOSED

PROGRAM………………………………………………… ……... 32

4.3 MODELS USED IN THE PROPOSED PROGRAM….... .............. 33

4.3.1 Models for forecasting socio-economic data................... 34

4.3.2 Models for trip generation / attraction… ………………. 36

4.3.3 Model for trip distribution……………………………... 41

4.3.4 Model for modal split ……………………… …………. 43

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4.3.5 Model for trip assignment …………… ………………. 45

4.3.6 Model for operational evaluation of road network ……. 47

4.3.7 (HCM2000 - two lane highway) method………………. 49

4.3.7.1 Determination of the free flow speed ( FFS)… 51

4.3.7.2Determination of the average travel speed

(ATS)............................................................... 53

4.3.7.3 Determination of model parameters ………… 54

4.3.7.4 Determination of percent time spentfollowing (PTSF)............................................. 55

4.3.7.5 Determination of level of service (LOS)......... 56

4.3.8 Operational evaluation of urban streets…………........... 56

4.3.8.1 The average travel speed (HCM2000 – ATS)method………………………………………. 56

4.3.8.1.1 Calculating of uniform delay d1 andincremental delay d2…….................... 60

4.3.8.1.2 Calculating of control delay (d)…….. 61

4.3.8.1.3 Calculating of average travel speed(ATS)………………………………... 63

4.3.8.2 The probability (NCHRP 3-70 – HCM2010)method……………………………………… 64

4.3.8.2.1Determine the probability that anindividual will response with LOS“J”or worse……………………………... 65

4.3.8.2.2 Determine the probability that driverwill perceive LOS “J”……................. 66

4.3.8.2.3 Determine the LOS model…………... 67

4.3.8.2.4 Determine LOS grade…………...…... 67

4.3.9 Models for environmental assessment …………………. 68

4.3.9.1 Air pollution model……………………..…… 70

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4.3.9.2 Model for noise pollution……………..…….. 73

4.3.10 Modules of the program………………………………... 74

ECONOMIC DATA OF THE-ANALYSIS OF SOCIO:5CHAPTERSTUDY AREA AND ZONING SYSTEM5.1 INTRODUCTION……………………………………….…... ........ 77

5.2 STUDY AREA AND ZONING SYSTEM ……………………….. 77

5.3 ANALYSIS OF SOCIO-ECONOMIC DATA IN THE STUDY

AREA ………….…………………………………………….. 83

5.3.1 Population and household................... ............................. 84

5.3.2 Education ……………………………………………... . 88

5.3.3 Employment ……………………………... ..................... 90

5.3.4 Income …………………………… ……………………. 91

5.3.5 Car ownership …………………………… ……………. 93

5.4 ANALYSIS OF THE PRESENT SITUATION OF

TRANSPORTATION SYSTEM IN THE STUDY AREA ……… 96

5.4.1 Transport system in the study area................................... 96

5.4.2 Mode choice of the study area ………………………… 100

5.5 ROAD NETWORK ………………………………………………. 100

5.6 TRANSPORTATION DEMAND ………………………………… 104

CHAPTER 6: APPLICATION OF THE PROPOSED PROGRAMON STUDY AREA6.1 INTRODUCTION…………………………………………… 106

6.2 MAIN MENU OF THE PROGRAM …………………………….. 106

6.3 FORECASTING OF SOCIO -ECONOMIC DATA ……………. 106

6.4 FORECASTING OF FUTURE TRIP PRODUCED ANDATTRACTED…………………………………………………….. 114

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6.4.1 Model Calibration ………………………...…………… 114

6.4.2 Forecasting of Trip Generated and Attracted ………….. 118

6.5 TRIP DISTRIBUTION STAGE …………… …..………………… 121

6.6 MODAL SPLIT STAGE………………….………………………. 123

6.7 TRIP ASSIGNMENT STAGE ……………………………………. 128

6.8 OPERATIONAL EVALUATION OF THE ROADNETWORK……………………………………………………….. 172

6.9 ENVIRONMENTAL ASSESSMENT ……………………………. 185

6.9.1 Air Pollution Assessment ………………………...……. 185

6.9.2 Noise Assessment …………........................................... 190

CHAPTER 7: MEASURES TO IMPROVE TRANSPORTATIONSYSTEM IN STUDY AREA7.1 INTRODUCTION…………………………………………… 200

7.2 (DO-NOTHING) SCENARIO......................................................... 200

7.3 LIGHT RAIL TRANSIT (LRT) SCENARIO.................................. 201

7.4 PUBIC TRANSPORT SCENARIO ................................................. 209

7.5 THE OPTIMAL SCENARIO........................................................... 216

CHAPTER 8: SUMMARY, CONCLUSIONS ANDRECOMMENDATIONS8.1 SUMMARY …………………………………………… …………. 218

8.2 CONCLUSIONS AND RECOMMENDATIONS ………………... 219

REFERENCES ……………………………………………………. 223

OMIC DATA OF MAINECON-SOCIO):A(APPENDIX.................................................................TRANSPORTATION ZONES 228

ECONOMIC DATA AND TRAVEL-SOCIO):B(APPENDIX..……………………………………………ZONES-DEMAND OF SUB 230

APPENDIX (C): APPLICATION OF THE PROGRAM ON TANTACITY AS CASE STUDY……………………………………….… …….. 236

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APPENDIX (D): LIGHT RAIL TRANSIT (LRT) SCENARIO …….. 252

APPENDIX (E): PUBLIC TRANSPORT SCENARIO ………………. 275

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LIST OF TABLES

Table Page

Table (2-1) Cross Classification Table Giving Trip Rate perHousehold per Day………………………………… 10

Table (3-1) Comparison of EMME/2 and TransCAD…………. 27

Table (3-2) Comparison of VISUM and TRANSPLAN………. 29

Table (4-1) Annual Growth Factors of Socio-Economc Data ofStudy Area ………………………………………… 35

Table (4-2) Forecasted Independent Variables that Affect TripGeneration / Attraction for Tanta city for Year2030……………………………………………… ... 36

Table (4-3) Model Parameters of the Independent Variables ofthe Proposed Trip Production Model ……………… 39

Table (4-4) Model Parameters of the Independent Variables ofthe Proposed Trip Attraction Model ......................... 40

Table (4-5) Primary Measures of Effectiveness for LOSDefinition………………………………………….. 48

Table (4-6) LOS Criteria for Two-Lane Highways Class I…….. 50

Table (4-7) LOS Criteria for Two-Lane Highways Class II…… 51

Table (4-8) Adjustment for Lane Width and Shoulder Width…. 52

Table (4-9) Adjustment for Access Points……………………... 52

Table (4-10) Adjustment factor for Percentage of Non -passingZones……………………………………………… . 53

Table (4-11) Grade Adjustment Factor (f G) to Determine Speedson Two-Way and Directional Segments …………... 54

Table (4-12) Passenger-Car Equivalents on Two-Lane Highway.. 55

Table (4-13) Classification Of Urban Street According ToFunctional And Design Criteria…………………… 57

Table (4-14) Functional and Design Categories of Urban Streets. 59

Table (4-15) LOS Criteria for Urban Streets According toHCM2000………………………………………… .. 60

Table (4-16) Incremental Delay Adjustment Factor I…………… 61

Table (4-17) Adjustment Factor for Platoon Arrival During theGreen……………………………………………… 62

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Table (4-18) Arrival Types Occurrence Conditions …………….. 62

Table (4-19) Segment Running Time (sec/km)…………………. 63

Table (4-20) Alpha and Beta Parameters for Recommended LOSModel……………………………………………… 65

Table (4-21) Parameters for Stops Per Km Equation …………….. 66

Table (4-22) LOS Letter Grade Numerical Equivalents ………… 67

Table (4-23) Parameter to Estimate Air Pollution FromTransportation Sector………………………............ 72

Table (5-1) The Administrative divisions of Tanta city ……..… 80

Table (5-2) Transportation Zones in Study Area ………………. 80

Table (5-3) Population Numbers of Transportation Zones Year2006……………………………………………….. 85

Table (5-4) Classification of Population of TransportationZones Year 2000………………………………… .. 85

Table (5-5) Change in Population Density of TransportationZones in Study Area Between Year 2000 and Year2006………………………………………………. 87

Table (5-6) Number of Educates in the Transportation Zones ofStudy Area in Year 2000………………………….. 89

Table (5-7) The Number of Employees AccordingTransportation Zones in Year 2006……………….. 90

Table (5-8) The Number of Employees AccordingTransportation Zones in Year 2000……………….. 90

Table (5-9) Annual Income of Population Numbers ofTransportation Zones in Year 2000………………... 92

Table (5-10) Number of Cars in Study Area Between Year 1990and Year1997…………………………………………. 93

Table (5-11) Number of Private Cars in The TransportationZones of The Study Area in Year 2000……………. 95

Table (5-12) Occupancy and Equivalent Passenger Car Unit ofTransportation Modes in Study Area ……………… 98

Table (5-13) Characteristics of road network of the study area inyear 2000…………………………………………... 102

Table (5-14) (O/D) Matrix of Transportation Zones Year 2000(trip/day)…………………………………………… 105

Table (6-1) Forecasted Socio-economic Data Affect TripProduction of Tanta city in year 2030……………... 109

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Table (6-2) Forecasted Socio-economic Data Affect TripAttraction of Tanta city in year 2030……………… 109

Table (6-3) Comparison Between Trip Generation/ AttractionCalculated by Program and Trip generation Data ofDifferent Transportation Zones in year 2000……… 116

Table (6-4) Trip Production and Trip Attraction of Tanta CityTransportation Sub-zones in Year 2030…………… 118

Table (6-5) Origin Destination Matrix of Tanta CityTransportation Sub-Zones for year 2030 (Trip/day)………………………………………………... 122

Table (6-6) Forecasted Origin Destination Matrix of Public Busin year 2030 in Tanta City (Trip/day)……………... 126

Table (6-7) Forecasted Origin Destination Matrix of CollectionTaxi in year 2030 in Tanta City (Trip/day)……….. 126

Table (6-8) Forecasted Origin Destination Matrix of Taxi inyear 2030 in Tanta City (Trip/day)………………... 127

Table (6-9) Forecasted Origin Destination Matrix ofMotorcycle in year 2030 in Tanta City (Trip/day). 127

Table (6-10) Forecasted Origin Destination Matrix of PrivateCars in year 2030 in Tanta City (Trip/day)……….. 128

Table (6-11) Reduction Factor of Roadway Capacity Due ToEffect Of Lateral Clearance and Lane Width……… 131

Table (6-12) Geometrical and Operational Characteristics of theCoded Road Links of the Network………………… 132

Table (6-13) The Input Data for the Trip Assignment Stage(Year 2000)………………………………………... 144

Table (6-14) Peak Hourly (O/D) Matrix for the Year 2000(pcu/hr)…………………………………………….. 156

Table (6-15) Trip Assignment Results on Road Network in Year2030 (Do- nothing Scenario)………………………. 157

Table (6-16) LOS of Road Links in the Study Area in 2030(Output of 6th Stage – Do-nothing Scenario)……… 175

Table (6-17) Comparison between Percentage Level of Serviceof Road Network Links Determined by (HCM2010–NCHRP 3-70) Method and (HCM2000 – ATS)Method…………………………………………….. 182

Table (6-18) Total Co2 and Co2 Equivalent Emission Producedfrom Transportation Systems in Year 2030 (kg/day)– (Do-nothing Scenario)…………………………. 188

Table (6-19) Mean Noise Level of the Road Network Links inYear 2030 in dB(A) (Do-nothing Scenario)………. 193

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Table (7-1) Comparison between Time Delay Percentage ofStudy Area Road Network Links for Do-nothingScenario and LRT Scenario……………………….. 204

Table (7-2) Comparison between Level of Service Percentageof Study Area Road Network Links for Do-nothingScenario and LRT Scenario……………………….. 205

Table (7-3) Comparison Between Percentage of Noise Level(%) Produced from Do-nothing Scenario and LRTScenario………………………………………….. 206

Table (7-4) Total Co2 and Co2 Equivalent Emission Producedin LRT Scenario for Target Year 2030 (kg/day)…... 208

Table (7-5) Comparison between Time Delay Percentage ofStudy Area Road Network Links for Do-nothingScenario and Public Transport Scenario…………... 211

Table (7-6) Comparison Between Level of Service Percentageof Road Network Links for Do-nothing Scenarioand Public Transport Scenario…………………….. 212

Table (7-7) Comparison Between Percentage of Noise Level(%) Produced from Do-nothing Scenario and PublicTransport Scenario………………………………… 213

Table (7-8) Total CO2 and CO2 Equivalent Emission ofTransportation Systems in Public TransportScenario for Target Year 2030 (kg/day)…………... 215

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LIST OF FIGURES

Figure Page

Fig (1-1) Framework of the Research………………………... 3

Fig (2-1) Stages Flowchart of Urban Transportation PlanningProcess.…………………………………………… .. 7

Fig (2-2) Four-Stage Travel Demand Model………………… 9

Fig (4-1) LOS graphical criteria for two -lane highways inclass I …………………………………… ..……….. 51

Fig (4-2) Functional Classifications of UrbanStreets.…………………………………………… .. 58

Fig (4-3) Major sources of carbon monoxi de ………..……… 69

Fig (4-4) Major sources of NOx……………………………... 69

Fig (4-5) Modules of Transportation PlanningProgram.…………………………………………… 76

Fig (5-1) Map of Egypt……………………………………… . 78

Fig (5-2) Location of Gharbia Governorate in Egypt ……….. 79

Fig (5-3) The Administrative divis ions of Tanta city………... 81

Fig (5-4) Transportation Zones in Study Area ………………. 82

Fig (5-5) Difference between the Population Numbers inStudy Area in the year 2000 and 2006……………. 86

Fig (5-6) Trip Generation Rate for Age Groups of StudyArea……………………………………………….. 88

Fig (5-7) Age Characteristics of Study Area ………………… 88

Fig (5-8) Number of Educates in The Transportation Zonesof Study Area in year 2000………………………... 89

Fig (5-9) Comparison of the Number of Employmentbetween year2000 and 2006 in the Study Area…… 91

Fig (5-10) Comparison between Population Number in EveryAnnual Income Category of DifferentTransportation Zones for Year 2000……………… 92

Fig (5-11) Distribution of the Average Annual Income in theStudy Area Year2000…………………………….. 93

Fig (5-12) Development of Car Ownership in Study Area …… 94

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Fig (5-13) Percentage of Private Cars According to theTransportation Zones in Study Area in Year 2000 95

Fig (5-14) The Routes of the Existing Transportation Systemin Study Area……………………………………… 99

Fig (5-15) Distribution of Demand by Mode in Study Area ….. 100

Fig (5-16) The Road Network of the Study Area…………… 101

Fig (6-1) Forecasting Socioeconomic Data of Tanta city inyear 2030 using the Program – First Stage………... 108

Fig (6-2) Forecasted Population Number in TheTransportation Zones of the Study Area in year2030……………………………………………….. 111

Fig (6-3) Forecasted Number of Educates in TheTransportation Zones of the Study Area in year2030……………………………………………..... 111

Fig (6-4) Forecasted Number of Employees in TheTransportation Zones of the Study Area in year2030……………………………………………….. 112

Fig (6-5) Forecasted Number of Cars in The TransportationZones of the Study Area in year 2030…………….. 112

Fig (6-6) Forecasted Population Number with DifferentAnnual Income Categories for the TransportationZones of the Study Area in year 2030…………….. 113

Fig (6-7) Forecasted Area (km2) of Transportation Zones ofthe Study Area in year 2030………………………. 114

Fig (6-8) Forecasted Number of Educational Places in TheTransportation Zones of the Study Area in year2030…………………………………………….…. 114

Fig (6-9) Calibration of Trip generation Model (year 2000)… 117

Fig (6-10) Calibration of Trip Attraction Model (year 2000)… 117

Fig (6-11) Forecasting of the Trip Produced /Attracted Usingthe Proposed Program – Second Stage……………. 119

Fig (6-12) Trip Production of Transportation Zones in theStudy Area (Trip/day) in year 2030……………….. 120

Fig (6-13) Trip Attraction of Transportation Zones of in theStudy Area (Trip/day) in year 2030……………….. 121

Fig (6-14) Distribution of Trips Using the Proposed Program –Third Stage………………………………………… 124

Fig (6-15) Modal Split Using the Proposed Program – FourthStage………………………………… …………………. 125

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Fig (6-16) Trip Assignment Using the Proposed Program – 5th

Stage……………………………………………….. 130

Fig (6-17) Study Area Road Network Coding System………... 155

Fig (6-18) (V/C) Percentage for Road Network (in number) ofStudy Area –( Target year 2030) – Do-nothingScenario…………………………………………… . 168

Fig (6-19) (V/C) Percentage of the Road Links of the StudyArea (in %) in Target year 2030 (Do -nothingScenario)…………………………………………… 169

Fig (6-20) Number of Road Network Links in different TimeDelay Ranges for year 2030 (Do-nothing Scenario). 171

Fig (6-21) Time Delay Percentage of Road Links in differentTime Delay Ranges (Year2030 – Do-notingScenario)…………………………………………… 171

Fig (6-22) Operational Evaluation Using the ProposedProgram – 6th Stage……………………………..…

174

Fig (6-23) Level of Service Percentage of Road NetworkLinks for Year 2030 (HCM2010 –NCHRP 3-70 –Do-nothing Scenario)................................................

173

Fig (6-24) Level of Service Percentage of Road NetworkLinks in Year2030 (HCM2000 – ATS method –Do-nothing Scenario)……………………………… 181

Fig (6-25) Comparison between LOS Percentage LOSCalculated by (HCM2010 –NCHRP 3-70) Methodand (HCM2000 – ATS) Method in Study Area forYear 2030………………………………………… .

183

Fig (6-26) Air Pollution Assessment Using the ProposedProgram – 7th Stage………………………………...

187

Fig (6-27) Emissions According to Transport Mode for StudyArea in 2030 (Do-nothing Scenario).........................

189

Fig (6-28) Percentage of Co2 Equivalent Emissions ofDifferent Transport Systems for Target Year 2030(Do-nothing scenario).............. .................................

189

Fig (6-29) Noise Pollution Assessment Using the ProposedProgram – 8th Stage................................................... 192

Fig (6-30) Mean Noise Level Percentage of Road NetworkLinks for Year 2030 – Do-nothing Scenario………. 199

Fig (7-1) Layout of Proposed LRT Path in LRT Scenario…. 202

Fig (7-2) The Proposed Modal Split in the LRT Scenario…... 203

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Fig (7-3) Comparison Between LOS Percentage of RoadNetwork for Do-nothing Scenario and LRTScenario……………………………………………. 205

Fig (7-4) Comparison Between Percentages of Noise LevelProduced from Do-nothing Scenario and LRTScenario…………………………………………… . 207

Fig (7-5) Comparison Between Co2 Equivalent EmissionsProduced from Transport Systems in Do -nothingScenario and LRT Scenario for Target Year 2030… 209

Fig (7-6) Modal Split for Public Transport Scenario ………... 210

Fig (7-7) Comparison between LOS Percentage of RoadNetwork for Do-nothing Scenario and PublicTransport Scenario.................................... ................ 212

Fig (7-8) Comparison Between Percentage of Noise LevelProduced from Do-nothing Scenario and PublicTransport Scenario………………………………… 214

Fig (7-9) Comparison Between Co2 Equivalent EmissionsProduced from Transport Systems in Do -nothingScenario and Public Transport Scenario for TargetYear 2030………………………………………… .. 215

Fig (7-10) Comparison between LRT Scenario and PublicTransport Scenario for Target Year 2030…………. 217

Chapter 1

Objectives and Methodology of the Research

1

1.1 Introduction

Due to the rapid growth of population and economy in urban areas of thedeveloping world, the demand for efficient transport is increasing.Population is growing rapidly in many cities, often through uncontrolledimmigration that allows city growth to outstrip the pace at whichinfrastructure can be adjusted.

It is a known fact that towns determine the axis along which traffic mustmove, and that transportation gives any town its form and confuses thelayout. So, urban transportation is identified as a functional element in thebroader context of urban facilities and services. Urban transport plays a keyrole in the dynamic development and economy of the city, without whichthe city could barely operate.

Moreover, vehicle ownership and use is growing even faster than thepopulation. Problems of congestion, pollution and accidents result fromvehicles moving on road networks, makes the value of doing planning hasbeen called into question by transportation planners and decision -makers.There is a wide range of suggested solutions to these problems, frombuilding new roads to banning cars, and from improving bus services to theuse of telecommunications and alternative to travel. Many of these solutionsare expensive, and may not be e ffective; moreover they may introduce newproblems. New roads, for example consume precious land; bans of c ars mayresult in loss of trade [5].

The need is not to provide bigger roads to cope with more vehicles. Thesubject of transport planning, urban transport planning in particular , is theunderstanding of these problems, formulating safe and sustainable efficientsolutions and managing the whole transportation system to provide anadequate system and involve broad interaction with many other discipli nes.Here the importance of Urban Transport Planning (UTP) , especially fordeveloping countries appears since the planning process is more than merelylisting highway and transit capital investments; it requires developingstrategies for operating, managin g, maintaining, and financing the area’stransportation system in such a way as to advance the area’s long -termgoals.

In case of Tanta city as developing city where motor vehicle andpopulation are growing at high rate and the road network can not pace thetransportation needs in the city, Tanta needs to develop a sustainable

2

transport system not only to reduce the urban road traffic, congestion, airpollution and noise emissions, but also due to achieving sustainabledevelopment of the 21st century. In such a situation there is an urgent needto apply scientific methods of transportation planning and trafficengineering duly aided by computers, to achieve both long term and shortterm solutions to the overall problems of traffic and transport in Tanta city.

Finally, the transportation planning process attempts to deal with very largeand complex problems. This is difficult not only because of the size of theurban transportation system and its interrelationships, but also because ofthe voluminous amount of information and data that must be comprehendedand processed. Computers have added a new dimension to the field oftransportation planning, a tool by which the planner can realistically analyzehuge volumes of available data [41].

1.2 Objectives of The Research

The main objective of this research is to define an urban transport planningprocess for developing countries. A Four steps computer model usingMATLAB programming system has been developed. The model analyzessocio-economic data, gets the relationship between them and the traveldemand in the study area. It forecasts the future demand, assigns it at theroad network, and finally evaluates the traffic operation and environmen talimpacts on the study area. The program has been applied on Tanta city(Egypt). Different improving scenarios were also examined.

1.3 Methodology of The Research

The research methodological framework is depicted in Fig (1-1). Itcontains the following steps:

Step (1) : Definition and scope of the urban transportation planning processare introduced

Step (2) : Software used for urban transport planning process have beenrepresented

Step (3) : A proposed program for urban transport planning process indeveloping countries has been created.

3

Fig (1-1): Framework of the Research.

Definition and Scope of Urban TransportationPlanning Process

Software used for Urban TransportationPlanning Process

Proposed program for Urban TransportationPlanning Process in Developing Countries

Analysis of Socio-economic Data of the Study Area

Application of the Proposed Program on theStudy Area

Measures to improve Transport system in UrbanAreas

Scenario 1Do-nothing

Scenario

Scenario 2Public Transport

Scenario

Scenario 3LRT

Scenario

Optimum Scenario

4

Step (4) : Analysis of socio -economic data of the study area has beenperformed

Step (5) : Application of the proposed program on study area has beeninvestigated

Step (6) : Measures to improve transport system in urban areas has beenstudied.

Step (7) : Finally, three scenarios are proposed to improve transportperformance in study area. (Do-nothing scenario –Public transport scenario– Light Rail Transit scenario)

1.4 Outlines of The Research

The research comprises 8 chapters:

Chapter (1) presents an introduction and includes the objective andmethodology of this thesis.

Chapter (2) deals with definition and scope of urban transport planningprocess.

Chapter (3) presents different software used for urban transport planningprocess

Chapter (4) introduces a proposed computer program for urban transportplanning in developing countries.

Chapter (5) analyzes the socio-economic data in Study Area.

Chapter (6) represents an application of the proposed computer program onthe Study Area.

Chapter (7) different measures to improve the transport system in StudyArea in 3 scenarios have been investigated.

Chapter (8) comprises the main conclusions drawn from the dissertation.

Chapter 2

Urban Transportation Planning Process

5

2.1 Introduction Transportation is a process of a transporting or being transported goods and people from a place to a place in a specific time with a mean of transport. The basic function of an urban transport system is to permit the efficient movement of goods and people in order to support the diverse needs of a dynamic urban economy Transportation planning is developed as a complete package of projects and policies, conceived as a unified whole. It should be implemented in accordance with a carefully conceived, financially realistic, annual program, derived in turn from a longer program. [35]. In the developing countries, urban transportation is a pressing concern in its big cities. Rapid population growth and spatial expansion has led to a sharp increase in demand for urban transportation facilities and services in these cities [12]. The main aim of this chapter is to define the urban transportation planning process. 2.2 Urban Transportation Planning The urban transportation planning is a part of the overall urban planning of a zone. It aims to build bases and roles to ensure that the transport system is keeping with the continues urban development and meets the needs of people of safe and comfort transport. Urban Transportation Planning Process (UTPP) is identified as conditional prediction of travel demand in order to estimate the likely transportation consequences of several transportation alternatives [6]. The Urban Transportation Planning Process concerns providing information to decide on the fate of the transportation projects and to put the transportation polices. The primary objective of the urban transportation planning process is to ensure that there should be a balance between land-use activities, urban environment elements and transportation demand. Moreover, the urban transportation planning includes forecasting future land-use and future travel demand, to ensure that there should be a connection between all future land-use activities. This communication is represented in traffic, and the objective of transportation planning is plane facilities to accommodate this traffic and the meeting of total transportation needs at minimum cost.

6

Also, much of the professional literature about urban transportation planning regard it as a rational process involves sequence steps from gathering information to decision-making [26]. These steps include: 1-Goal definition: that is a determination and statement of the goals of the transportation systems based on community value, as identified by the planner. 2-Identification of needs: this involves comparison of the actual performance of the transportation system with its goals, objectives and measures of effectiveness. 3-Developing of alternative solutions to address each need identified. 4-Evaluation of alternative solutions in terms of physical, economic, financial feasibility, environmental impact. A decision process in which particular alternatives are selected for implementation. A broad sequence of operations inside the urban transportation planning process is proposed to identify greater detailed stages. These stages include: 1- Survey and data collection: surveying the present-day travel habits of people living or working in the study area and collecting socio-economic and land-use data. 2- Developing models: developing mathematical formulas and parameter calibrations, which present the relationship between socio-economic data and travel demand of the study area in present days. These models includes:

· Trip generation models (how many travel movements are made?)

· Trip distribution (where do they go?) · Modal split (by what modes are they travel?) · Trip assignment (at what route it is taken?) · Network evaluation (what level of service is on the network

links?) · Environmental assessment (How the transport system impacts

the environment?) 3- Predict future travel demand: uses future socio-economic data together with the models to predict future demand. 4- Future situation of the transport systems and networks: determining the situation after distributing the travel demand on the transport system and transport network. 5- Evaluation of transport networks: to identify problem places on the transport networks.

7

6- Proposing alternative solution scenarios. 7- Evaluation of different alternative scenarios: depending on economic, operational and environmental issues, as well as political aspects, evaluation of scenarios can be implemented. 8- Finally, Selecting and implementing the optimum alternative: solving urban transportation problems and selecting the suitable alternative depends on the time-scale of the solution. Some transportation problems can be solved by immediate action plans, some need short-term (1-3 years), or long term plans (up to 20+years). Fig (2-1) shows a stages flowchart of urban transportation planning process.

Selecting and implementing the optimum alternative

Proposing alternative solution scenarios

Evaluation of different alternatives scenarios

Future situation of the transport systems and networks

Evaluation of transport networks

Survey & Data Collection

Developing Transportation Models

Future Transport Demand

8

Fig (2-1): Stages Flowchart of Urban Transportation Planning Process.

On the other hand, a constant problem in urban areas is the collection and analysis of data that describe changes in urban activities.. Just as urban transportation planners need socio-economic data by traffic zones, urban management requires socio-economic data by small areas to estimate the demand for public services [30]. There for, it is important to collect as much as possible socio-economic, environmental, present transport system and urban land use data of the study area, as this information is the base of the transportation planning study, although collecting and analyzing of data that describe urban activities is a constant problem in urban areas of developing countries. This stage is more difficult in developing countries; the rare of such information in microscopic stage makes it difficult to build accurate transport models. 2.3 Travel Demand Modeling A model is an abstraction of reality, formulated in either conceptual, physical or mathematical terms and used as a mechanism for reproducing the operation of a real world system for analytical purpose [2]. The transport demand models aim to estimate the travel demand and travel behavior, which will take place under a given set of assumptions (for example, population, income, car ownership and land-use) in order to estimate the likely transportation consequence of several transport alternatives. Development efforts of the transportation demand modeling have followed two main paths. The first represents a move toward a model grounded on a theoretical understand of travel behavior. The second is toward the discovery of simple models that can facilitate decision making by providing useful information quickly and inexpensively [7]. Besides, the development of travel demand models involves the construction of mathematical models of current travel behaviors, determination of regression coefficients through analysis of present travel demand data. Many transportation demand aspects have been used in the transportation planning process.

9

2.3.1 Four-Stages Travel Demand Model One of the well known travel demand models is the four-stage model. This model was originally developed during the 1960s. The model may appear to have remained unchanged since that time, but, the powerful personal computers have made these models undergo significant modifications in response to improve the modelers understanding of the travel behavior. This model consists of four sequential steps. The output of each step is the input of the following step beside other inputs as socio-economic, land-use inputs and the target-planning year. The four – stage model consists of four sub-models. They are:

· Trip generation stage. · Trip distribution stage. · Mode choice stage. · Trip assignment stage.

Fig (2-2) shows components of the four-stage travel demand model.

Fig (2-2): Four-Stage Travel Demand Model. The objective of the trip generation model is to forecast the number of person-trips that will begin from or end in each travel analysis zone within the study area for a typical day of the target year. Trip generation regression models (trip production model and trip

Trip Generation Model

Trip Distribution Model

Mode Choice

Trip Assignment Model

10

attraction model) must have been estimated and calibrated using observations taken during the base year, and the total number of trip generation represents the dependant variable of the model and the independent variables are the land-use or socio-economic factors. The relationships expressed by the model are assumed to remain unchanged over time until other regression coefficient is interpolated. The most common form of the trip generation regression model is in a linear function of the form: Qi = a0 + a1 x1i + a2 x2i + a3 x3i +.....+ an xni

Zj = b0 + b1 y1j + b2 y2j + b3 y3j +.....+ bn ynj

Where: Qi : Number of trips produced from zone i. Zj : Number of trips attracted to zone j. x :Independent variable affects trip production. y : Independent variable affects trip attraction. a , b : Regression parameters of independent variables. x represents factors which have commonly been used to predict trip

production include population, number of workers in households, car

ownership and income. On the other hand, y represents factors that

have commonly been used to predict trip attraction includes total employment in zone and land development.

An alternative trip generation technique is the cross classification

analysis. In this technique the population is broken down into a set of classes known to be correlated with trip making behavior. For

example, house holds may be broken down according to number of

persons in the household and number vehicles available to the household. Each combination of these two categories results in a rate

of trip making per household. Table (2-1) illustrates an example of

cross classification trip rate.

11

Table (2-1): Cross Classification Table Giving Trip Rate per Household per Day. [26].

Trip/Household/Day Vehicles/Household Person/

Household 0 1 2 or more 1 1.02 1.90 2.10 2 2.12 3.25 3.70 3 2.15 3.75 3.90

4 or more 3.96 5.00 6.54 A third trip generation technique is the trip rate technique. In this

trip generation analysis, the Trip rate refers to several models that are based on the determination of the average trip-production or trip

attraction rates associated with the important trip generators within

the region. This technique is a little complicated as the trip rate is

given for a very specific categorization, for instance, the trip rate can be given for a person per thousand square feet of each land use of the

study area.

In comparing different trip generation technique, A comparison of trip generation models done by Daniel A. Badoe [8] on to Greater

Toronto Area using data for a base year 1986 and a target year 1996

led to the conclusion that for urban areas the simple linear model yields the best performance in prediction of travel in the base year

and in the forecast year using error measures evaluated.

The next step of the sequential four-step forecasting model system is intended to predict zone-zone trip interchanges. The final product is a

n origin-destination matrix representing trip interchange between all

the study area transportation zones. The rational used in trip

distribution is that all trips attraction are in a competition to each other to attract trips produced by production transportation zones, and

more trips will be attracted to the zone of more attractiveness. This

attractiveness could be presented in the travel time between zones,

the travel economic cost between zones or the distance between zones.

12

Over the years, modelers have used several different formulations of trip distribution. The first was the Fratar or Growth model. This

structure extrapolated a base year trip table to the future based on

growth, but took no account of changing spatial accessibility due to

increased supply or changes in travel patterns and congestion. The next models developed were the gravity model and the intervening

opportunities model. Evaluation of several model forms in the 1960's

concluded that "the gravity model and intervening opportunity model

proved of about equal reliability and utility in simulating the 1948 and 1955 trip distribution for Washington, D.C.". [9]

The basic idea of Fratar method is the assumption of a constant ratio

of design base year number of trips, and design target year number of trips. This model takes the following formulation:

Where: Fij (forecasted) :Forecasted number of trips generated from zone i to zone j in the target year. Fij(current) :Current number of trips generated from zone i to zone j in the base year. Qi(t) :Trips generated from zone i in target year. One of the limitations of Fratar method is that it breaks down if one new transportation zone is created after the base year, besides, the

model is not sensitive to the impedance between transportation zones.

In addition to the previous, and because of computational ease,

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13

gravity models became more widely spread than intervening

opportunities models. The Gravity model gets its name from the fact that it is conceptually based on Newton's gravity law which states the

force. The gravity model of trip distribution takes the following

formulation:

Where:

Fij :Number of trips generated from zone i to zone j in the target year. Qi :Trips generated from zone i in target year. Zj :Trips attracted to zone j in target year. Kij : Socio-economic balance factor. Wij : Impedance between zoon i and j (travel time, travel cost or travel distance). γ : Sensitivity factor of travel resistance. Assumed to be 2.0, by the analogy to the inverse square law of gravity. Urban transportation planning may involve single transportation

mode or combination of different modes. The third step of the sequential four-step forecasting model system is used to show the

travel modes selection behavior of trip maker. The reasons

underlying the modal split vary among trip type, cost and level of

service associated with available transport modes. Factors affect the transport mode choice can be classified into 3 branches: [43]

1- Household characteristics.

2- Zonal characteristics.

3- Traffic facility characteristics.

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Household characteristics affect the mode choice by the in many

aspects. For instance, increase in income may switch the trip maker from a low cost travel mode to more expensive and comfortable

mode of transport. Also, Car ownership also affects the mode choice.

Family composition has also a direct influence on the mode choice;

students are less ready for driving car to school and will chose public transport. Workers of high income and employment may own a

private car.

On the other hand, zone accessibility affects the modal split. Residential zones with high density will be served by large number of

public transport modes. Moreover, Traffic facility characteristics such

as travel time, waiting time to access, availability to park and cost of

travel influence the choice behavior of the trip maker. Wilson A. G. [49] developed a trip distribution and modal-split model for the journey to work in the form:

Where : Fij

kn : Number of work trips estimated by the model between origin

zone i and destination zone j, by mode k and persons of type n. Oi

n : Number of work trips originating in zone i by persons of

type n.. Dj : Total number of work trips destinations in zone j. n :Person type (usually taken as a car-ownership index). exp(-βn Cij

n) : Exponential of generalized cost-decay function. Ai

n and Bj : Function of Dj and Oi

n.

Contemporary mode choice models are almost always disaggregate

probability models based on a utility function .The most common assumption is a logit model that calculates the probability of

choosing mode m. this model takes the following formula:

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ni

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15

Um = a0 + a1 F1m + a1 F1m +……+ an Fnm

Where : P(m) : Probability of travelers that use mode m.

Um : Utility of mode m.

a : Model parameter of mode m.

F : Factor affecting the utility of mode m (trip cost or trip time or

safety). In the assignment stage, the model is intended to predict the number

of traveler that choice specific route between two transportation

zones and, hence, the traffic on the links of the transportation

network. The basic of trip assignment is that all the trip makers are rationally thinking, and will try to find the route of least cost. The

term cost represents many aspects including journey time, length,

financial cost, comfort, safety and convenience.

A network assignment processed requires a way of coding the

network, an understanding of the factors affects assignment (travel

cost of the link). The network coding includes that the network is

divided into a system of links and nodes. The nodes are interconnected by links to establish the traffic network. Traffic is

assigned to each of these links depending on the loading between

each pair of nodes. The representation of the nodes is by giving

number to each node, and the link is represented by its start node and end node number.

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All-or-Nothing (AON) assignment technique assumes that all trip

makers traveling between a specific pair of zones will select the same minimum path calculated on the free flow link impedance. As a

result, too many vehicles will be assigned to a particular link and will

cause congestion in it. That is not done in reality as the traffic

volumes is divided between many paths. The defects in the All-or-Nothing technique have been covered in

the Capacity-Restrained technique. Capacity-Restrained is based on

the principal that as the link flow increases, the travel time increases.

This means that the shortest path in the first assignment stage may not be the shortest in the second assignment stage because of new

traffic congestion on the link. Several assignment procedures are

done. And at the end of each assignment, the traffic volume on each

link is calculated and compared to its capacity, and the new travel time of all the routes is calculated according to the following

formulation:

Where: tn : New trip time after assignment phase i. t0 : Trip time before assignment phase i (free-flow time). V :Assigned traffic volume (pcu/hr). C :Practical road capacity (pcu/hr). 2.3.2 Simultaneous or Direct Demand Formulation Another travel demand theory states the individual makes the travel

choice decisions simultaneously rather than in sequence. And for this, the demand model should be calibrated to match this behavior. The

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Quandt and Baumol formulation of intercity travel demand is an

example of the Simultaneous demand. This model takes the form:

Where: Fijk :Travel flow between zone i to zone j by mode k. Pi , PJ :Population of zone i and zone j . Cijk :Cost of mode k. Hijk : Travel time of mode k. Dijk : Departure frequency of mode k. Yij : Weighted average income of zone i and zone j

a0…….. a8 : Calibration parameters * : Refers to least value This model is a simultaneously trip generation- trip distribution

mode choice model. The equation uses the land-use variables and socio-economic characteristics data, besides the impedance between

zones to estimate the zonal demand by mode.

In urban situation, the application and calibration of such a large model is cumbersome. However, they may be useful for rather coarse

estimates at the regional level if the number of zones and degree of

details in specifying the transportation network are kept to a minimum. [7]

2.4 Sustainability in Urban Transportation Planning Transportation is a primary factor behind environmental problems in

the cities of developing countries [19]. With the ultimate goal of

achieving sustainable urban transportation, it is clear that the attention of transportation planners cannot be so narrowly focused.

Apart from the absolute number of trips, attention has to be placed on

the quality or nature of these trips as well [3]. For this reason, the step

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of the evaluation of the optimum future transportation scenario has an

environmental dimension. The optimum future transportation scenario is not only that leads to less traffic volumes and less

congestion, but also the scenario with minimum negative

environmental impact. This is the concept of the sustainable

transportation planning.

Finally, Continuous reviews and revisions of plans lead to improved

confidence levels [14]. There can not be any finality about the plan

since it is a continuing dynamic process. So, urban transportation planning process always contains a feedback path.

Chapter 3

Software Used for Urban Transportation PlanningProcess.

18

3.1 Introduction The process of carrying out computer-aided transport planning with status

analysis and design process work is shared between the user and the

computer. While the planner successively improves his design (suggested

solution) based on the current state, the computer determines the impact of

the current solution. In computer-aided transport planning, the transportation

system is represented in a transport model which, like all models, is an

abstraction of the real world. The aim of the modeling process is model-

based preparation for decisions taken in the real world. [45]. Many of

computer packages have been developed to aid transport planner in his

transportation planning process, examples of these software are EMME/2

(Equilibrium Multimodal-Multimodal Equilibrium), QRSII (Quick

Response System), TRANPLAN (TRANsport PLANning), HCS (Highway

Capacity Software), VISUM (Verkehr In Städten – Umlegung) and

VISSIM (Verkehr In Städten – SIMulationsmodel), and TransCAD

(TRANSport Computer Aided Design). The aim of this chapter is to analyze

the input and output of these programs and to introduce a comparison

between them, which may help the transport planner to choice the system

which is appropriate to the specific goal of his transport planning process.

Another aim is to describe the ability of using such software in urban areas

of the developing countries.

3.2 Transportation Planning Software Many computer software involves the transportation planning process, for

instance:

• EMME/2 (Equilibrium Multimodal-Multimodal Equilibrium).

• QRSII (Quick Response System).

• TRANPLAN (TRANsport PLANning).

• HCS (Highway Capacity Software).

• VISUM (Verkehr In Städten – Umlegung) and VISSIM (Verkehr In

Städten – SIMulationsmodel).

19

• TRANSCAD (TRANSport Computer Aided Design).

3.2.1 EMME/2

The Equilibrium Multimodal-Multimodal Equilibrium (EMME) is a

research model that resulted from a project that lasted from 1976 to 1979. It

was programmed by FORTRAN. The algorithm for solving the network

assignment was based on TRAFIC and the transit assignment was based on

TRANSCOM. Later, the software was updated by INOR Consultant, the

developer of EMME to be graphical interface and named EMME/2.

EMME/2 does not include default settings for any procedure, reflecting a

philosophy that the user should be able to specify an appropriate model

form, understand what the model represents, and be aware of its potential

limitations. EMME/2, not designed for beginners, is appreciated by more

advanced Urban Transport Planning (UTP) modelers. The input requires a

network representation by coordinate system or by a digital map. On each

node and link the pertinent mode, transit line, turns and volumes are input.

Network or zone data such as accident statistics, traffic surveys pavement

characteristics and other custom information can be incorporated with user

definition attributes.

Traffic demand software, like EMME/2, has a powerful ability to automate

the four-step model for traffic analysis. However, often they have a poor

graphical interface and the network maps for this software are very difficult

to find [36]. The major feature of this software package is the incorporation

of multimodal analysis. In all applications, both automobiles and transit

related characteristics can be incorporated simultaneously, which

approximate real world conditions. Up to 30 modes can be handled by the

software. EMME/2 provides framework of implementing wide variety of

travel demand forecasting. Also, the user can specify unlimited models

representing demand, volume delay relationships, turn penalties and mode

choice behavior.

20

EMME/2 support ASCII text files, shape files and dBase files for

importing data. The software is only capable of exporting to ASCII files.

The software is limited to 6,000 zones, 150,000 links, 60,000 nodes and

24,000 transit lines. The main outputs of the software are the overall

equilibrium of the road network and a graphical presentation of the results.

Besides, the updated network characteristics can be fed back to EMME/2 for

derivation of optimum network assignment. Another output is the

economical evaluation and traffic impact analysis.

3.2.2 QRS II

The Quick Response System (QRSII) was developed in the 1970s as an

upgrade of QRSI to provide a quick analysis of transportation policies,

particularly in a small area level. It provides good interface and a power

interactive graphics general network which can be used to draw quickly a

modified highway and transit networks on the computer screen. All data

needed by the system are entered using the graphics general network.

Because QRSII includes defaults for all model settings based on accepted

industry standards, it is perhaps the easiest package to learn and use.

Algorithms for trip generation, distribution, modal split and trip assignment

besides algorithms to find transit and highway path are a part of the

software. Default equations and parameters are provided.

The trip production and attraction stage calculates the trips as a person trip

per day. the trip production number is calculated based on average

household trip rates, so the software requires inputs about average

household trip rate, trip purpose. Then, the total production is split into 3

purpose trips (home-based work, home-based non-work, non-home- based).

On the other hand, the trip attraction is calculated based on multiple linear

regression equation [7].

21

The gravity model is the model used for the trip distribution with an option

of choosing the time as impedance. The basic modal choice used in the

software splits the travel demand between highway and transit based on the

difference in disutility of the two modes. The form used in modal splitting is

the logit binomial equation.

The assignment is done by the (All or Nothing) technique or by the

Capacity restrained technique. QRSII requires the specification of the

network for each model.

3.2.3 TRANPLAN

One of the most commonly urban transportation planning used software

programs in the United States is (TRANsport PLANning) TRANPLAN.

This software was first written as 16-bit DOS application, then it was

developed to be 32 bit Windows application. It may not be the fanciest

program, in terms of offering multiple versions of advanced route

assignment procedures, but it does provide all of the options normally

associated with the traditional four-step UTP process.

TRANPLAN is a toolbox with more than 40 functions. The Network

Information System (NIS) is available for the development maintenance

display of highway and transit networks. TRANPLAN is Geographic

Information System (GIS) supporting, which can update up to 15 types of

polygon boundaries. However, TRANPLAN is a batch rather than an

interactive system, so the user may need to develop certain parts of an

application (e.g., trip generation) by another program and interface it with

TRANPLAN.

TRANPLAN model of trip production and trip attraction is the regression

model. In trip distribution, TRANPLAN support both Frater model and the

gravity model while the software uses the diversion curve for trip modal

split. The assignment is done by the All or Nothing technique, by the

22

Capacity restrained technique, or by the incremental loading technique.

While transit trips may be split among competing transit lines in one of three

ways: in proportion to the frequencies of transit lines, equally among them,

or only on the selected line Also, the software allows the user to use network

functions to update the highway and transit network information

TRANPLAN is often favored for areas in which trip estimation and

assignment have legal implications or when state planning agencies require

standardized model outputs [15].

3.2.4 HCS

HCS 2000 (Highway Capacity Software) was developed by the Mc Trans

Center at the university of Florida as a typical Windows installation. HCS

2000 is a program based on the Highway Capacity Manual. Its primary

function is to analyze capacity and provide level of service for isolated

intersections. Each intersection required the following traffic inputs: number

of lanes per approach, volumes per lane, lane width, % grade, % heavy

vehicles, parking, bus stop per hour, conflicting pedestrian crossing per

hour, pedestrian button and minimum pedestrian green time, arrival type,

right turns on red and lost time. The required timing inputs for each

intersection include phasing diagrams, whether the signal is actuated, and

the green, yellow and red times for each phase. HCS output the following

information: adjusted saturation flows for each approach, volume

adjustments, capacity analysis, delays and level of service [47].

3.2.5 VISUM and VISSIM VISUM and VISSIM are a program for computer-aided transport planning

which serves to analyze, modeling and plan a transportation system. It were

developed by (PTV) Planung Transport Verkehr AG in Karlsruhe, Germany

A transportation system includes private and public transport supply (Private

Transport systems (PrT), Public Transport system (PuT) and travel demand.

23

The name VISUM is driven from the German (Verkehr In Städten –

Umlegung) which means (traffic in cities – assignment). VISUM supports

planners to develop measures and determines the impact of these measures.

VISUM uses User indicators which describe connection quality between

traffic zones, operator indicators which quantify operational and financial

requirements of implementing a given public transport supply,

environmental indicators which quantify the impact of motorized private

transport on the environment. VISUM contains input data tables and allow

the ability to print the outputs.

VISSIM is microscopic multi-modal traffic flow simulation software. The

name is derived from (Verkehr In Städten – SIMulationsmodell) (German

for “Traffic in cities - simulation model”). VISSIM was started in 1992 and

is today a global market leader. The network models consists of several

network objects which contain relevant data about the transport network To

describe the transport supply, VISUM distinguishes between the following

network object types:

• Zones and global zones,

• Nodes,

• Links,

• Turning relations,

• Zone connectors,

• Public transport lines with line routes and timetables.

The software allows the modification of the road network inputs. The

demand models contain the travel demand data. The program VISSIM

estimates and forecasts mode-specific origin-destination matrices for

behaviorally homogeneous person groups.

Regarding to data transfer between VISUM and VISSIM, fully or partially

networks can be directly exported to VISSIM with all attributed

automatically converted including signal timing, vehicle types and flared-

24

approaches. As VISUM has no 3D presentation facility, exporting network

to VISSIM could be useful way of making a real life 3D presentation of the

network. VISUM is unable to import INP files (VISSIM files)

Assignment procedures are based on search algorithms which determine

routes or connections between origin and destination. The search procedure

is followed by choice and split procedures which distribute the travel

demand of an origin-destination relation (O-D pair) onto the

routes/connections. The routes and connections also carry the necessary

information for calculating indicators, such as times, distances and number

of transfers. VISUM offers various assignment procedures for private and

public transport. They differ by the search algorithm and by the procedure

used for distributing trips. Assignment results in volume values for the used

network objects (nodes, links, connectors, turning relations, lines). As a

unique feature VISUM stores all routes for post assignment analysis, e.g.

flow bundle calculation and display. Due to memory capacity, PuT-

connections can only be saved as routes after assignment i.e. only

information about the used sequence of lines is stored. The departure time

and exact transfer times are not stored.

VISUM provides four models which calculate environmental impact, that

is, noise and pollution emissions, caused by motorized private transport. The

results can be displayed in tabular or graphic form. VISUM is extremely

flexible in display and powerful in map design and maintains a

geographically accurate street network, including the exact shape and length

of links. The boundaries of zones and higher-level area objects are

maintained as part of the data set. As in GIS software, all network objects

can have as many user-defined data variables as wished. Also, VISUM

includes integrated "Undo" and "Redo" commands that restore network

integrity after a complex series of user interactions and network

modifications.

25

3.2.6 TransCAD TransCAD (TRANSport Computer Aided Design) was the first software

program for urban transportation planning that combine a true geographic

information system (GIS) with a urban transportation planning .This

package is basically a Transport Geographic Information System (GIS-T)

application with augmented ability of transportation planning because it

encompasses zone building and four-step transportation planning process. It

was also the first urban transportation planning program to offer a fully

integrated set of menu screens. The network building facility in TransCAD

can be quite challenging to master, even for experts. TransCAD is GIS

program for higher-level of aggregate transportation planning. There are no

limits on the number of zones, number of links, nodes or transit lines in

TransCAD.

For the planning purpose, TransCAD requires data about Scio-economic

situation, trip rates of households, land–use data and road network data

besides the utility function of different transportation modes and fuel

consumption rates of different transport modes. TransCAD can also apply

micro-simulation options.

3.3 Comparison of Software Used for Urban Transportation planning

Table (3-1) and Table (3-2) illustrate a comparison between software used for urban transportation planning. This comparison provides- for each software- the following items:

• Data import and export • Input requirements. • Trip generation model • Trip distribution model • Modal split model • Trip assignment model • GIS integration • Non-motorized travel

26

• Compatibility with land use models These Tables indicate the following facts:

• EMME/2 support only ASCII text files, shape files and dBase files for importing data and didn't support other modeling software files.

• TransCAD, VISUM and TRANPLAN support importing data from many various modeling software.

• All transportation planning software require inputting detailed data to complete the transportation planning analysis.

• EMME/2 and TRANPLAN support just two trip generation models (regression and cross classification), TransCAD and VISUM support further trip generation models as trip rate daily activity schedules and time of day generation methods.

• EMME/2 and TRANPLAN include the gravity model or the FRATAR method as trip distribution models, while TransCAD support further Trip distripution models as destination choice (aggregate and disaggregate), tri-proportional. VISUM support all pre-mentioned distribution models besides trip chain building model.

• In the modal split stage, all models allow both the logit and nested logit methods, but VISUM have the ability to specific visual basic scripts using VISUM’s objects and methods can also be used to develop logit models. EMME/2 has the ability of using any other demand function.

• TRANPLAN supports All or nothing, Capacity restrain, Incremental trip assignment model. Other software support more trip assignment models.

• Except EMME/2, all software support GIS integration, EMME/2 has Enif as an alternative interface to access EMME/2 data banks, shape files and dBase files.

• VISUM and TransCAD are compatible with all land use models and can be linked to them through GIS files. EMME/2 Interfaces with land use methods with sub-programs (MEPLAN, EMPAL/DRAM), while TRANPLAN is compatible with some land use models.

27

Table (3-1): Comparison of EMME/2 and TransCAD.

Software EMME/2 TransCAD

Data import and export

EMME/2 support ASCII text files, shape files and dBase files for importing data. The software is only capable of exporting to ASCII files.

TransCAD is versatile in importing and exporting data. TransCAD is able to transfer data between other modeling software as Tranplan, MINUTP, TP+, EMME/2, Tmodel .

Input requirments

Requires detailed data about : • Classified and detailed

Scio-economic data • Trip rates of

households • Land –use data • Full transit and road

network data • fuel consumption rates

of different transport modes

• utility function of different transportation modes





of different transport modes.


Trip generation

model

Trip-generation using either regression, cross classification or trip rates

Can perform trip generation using either regression, cross-classification or trip rates. It can also use Institute of Transportation Engineers (ITE) trip generation rates, logit, user defined macros and user written programs.

Trip

distribution

model

Gravity model or the FRATAR method.

Can estimate and apply gravity models, destination choice (aggregate and disaggregate), tri-proportional, FRATARs

Modal split Supports of logit and nested logit methods. Any demand model may be used.

Allows both the logit and nested logit methods of modal split

28

Table (3-1): Comparison of EMME/2 and TransCAD. (Continued).

Trip assignment

model

has a versatile assignment procedure and allows using any of the following methods:

• All or nothing • Capacity restraint • Intersection based

capacity restraint • Stochastic/probabilistic • Equilibrium

TransCAD can perform traffic assignment using any of the following methods:


capacity restraint • Stochastic/probabilistic • Incremental • Equilibrium or • Dynamic.

GIS integration

Enif is an alternative interface to access EMME/2 data banks, shape files and dBase files.

TransCAD is a true GIS package and links easily to ArcView, ArcGIS, Mapinfo and MAPTITUDE. Models can run on true GIS networks and TAZ layers and maintains accurate GIS-based link shape, network topology and network distances.

Non-motorized

travel

Permits analysis of walk trips, bicycle trips and other non-motorized modes.

Can have separate and fully integrated networks for bicycles and pedestrians.

Compatibility with land use

models

Interfaces with land use methods as MEPLAN, EMPAL/DRAM have been developed and can be used with EMME/2.

Compatible with virtually all land use models and can be linked to them through GIS files. Can display and color code parcel and land use data directly.

29

Table (3-2): Comparison of VISUM and TRANSPLAN.

Software VISUM TRANPLAN

Data import and export

Expansive data import and export abilities built into the software.VISUM is able to exchange data between other packages as Tmodel,EMME/2, Tranplan, MINUTP, QRS and TransCAD.

Able to transfer data between other modeling software as , MINUTP, TP+, EMME/2.

Input requirments





of different transport modes






of different transport modes.


Trip generation

model

Generate trips using regression, cross-classification, trip rate daily activity schedules and time of day generation methods.

Can perform trip generation using either regression, cross-classification or.

Trip distribution

model

Can be performed using the gravity model, FRATAR method and trip chain building.

Can estimate and apply FRATAR and gravity models.

Modal split

Uses nested logit and allows user specific models. Specific visual basic scripts using VISUM’s objects and methods can also be used to develop logit models.

Allows both the logit and nested logit methods.

30

Table (3-2): Comparison of Comparison of VISUM and TRANSPLAN. (Continued).

Trip assignment

model

Can perform traffic assignment using any of the following methods:


capacity restraint • Stochastic/probabilistic • Incremental • Equilibrium or • Dynamic.

Can perform traffic assignment using any of the following methods:

• All or nothing • Capacity restraint • Incremental

GIS integration

Maintains a geographically accurate street network, including the exact shape and length of links. The boundaries of zones are maintained as part of the data set. As in GIS software, all network objects can have as many user-defined data variables as wished.

TRANPLAN is GIS supporting, that can update up to 15 types of polygon boundaries.

Non-motorized

travel

Walk and bike trips are included in the trip chain model as mode choice alternatives. During assignment, they can be modeled as simple transit mode or with separate route choice in a full street network.

Can have separate networks for bicycles and pedestrians.

Compatibility with land use

models

The user can define attributes for the zones or for higher-level area objects. All results from a land use model can be imported into these attributes to be of use in the modeling process. In addition, zoning/parcel layers can be displayed and color-coded.

Compatible with some land use models and can be linked to them through GIS files.

31

3.4 Urban Transportation Planning Software for Developing Countries

In developing countries, there is a general lack of adequately current or

relevant demographic and socio-economic data and information required for

the transportation planning process. Also, sometimes inaccurate statistics

makes the use of mentioned transportation planning software difficult and

may lead to inaccurate results. In this case, a new transportation planning

software must be built.

Chapter 4

Proposed Program for Urban Transportation PlanningProcess in Developing Areas

32

4.1 Introduction The transportation planning process is a set of analytical techniques used to forecast future transportation requirements and evaluate proposed systems. Until quite recently, transportation planners used only mainframe computers for these applications because of the relative complexity of these models. Today, most Urban Transportation Planning (UTP) professionals use personal computers (PCs) that are as powerful as the mainframe computers of the past and offer a number of distinct advantages. Their lower cost, smaller size, and increased ease of use make them highly suitable for UTP modeling. Transportation planning software automates the four steps process. This process typically forecasts future travel demand by employing separate forecasting sub-models for trip generation, trip distribution, modal split, and route assignment, usually on a regional basis. The process describes the transportation system in terms of a simplified network of links and nodes. A model is appropriate if it can do the job. Transportation analysts seek a universal model that can do most of the jobs, big doubt that such a model exists. Personal computer implementations of urban transportation planning (UTP) packages have resulted in a proliferation of modeling efforts. However, stuff experience and availability, particularly at small planning agencies has not kept up with this proliferation. As many (UTP) models are complex and difficult to use [42]. By using the planning software and after inputting the data, complex analyses is done in a short time using the programmed transportation models.

In this research, a computer program named (UTPP-TC) is designed to perform many operations and calculations needed through models of urban transportation planning. MATLAB is chosen as a programming language for this program because it contains many programming operations that can be applied to transportation models. It is able to perform many effective mathematical operation needed for transportation planning studies. The software is programmed using MATLAB 7.8.0 (R2009a). 4.2 Structure and Components of the Proposed Program The planner begin with input transportation and socio-economic data collected in a chosen base year. The main inputs of the proposed program are:

33

• Population. • Employment and working places. • Income. • Car ownership. • Travel demand (O/D) matrix of the base year. • Modal split. • Existing or designed transportation network.

The program contains the following phases: 1- Input of socio-economic data of base year. 2- Forecasting of socio-economic data for target year. 3- Trip production and trip attraction. 4- Trip distribution . 5- Distribution of trips on transportation models (Mode choice). 6- Assignment of trips on transportation network. 7- Determination of (L.O.S) and evaluation of the network. 8- Environmental assessment (pollution and noise).

4.3 Models Used in the Proposed Program A model is a simulation of the realistic world. Model assists the planner to generate decisions on a base of study of a collectable amount of data, acceptable cost and available time limit. Jessop A. [27] defined the essence of a model as it captures some important aspects of real situation in a way that permits easy comprehension and manipulation to aid decision. There are two approaches to the transport planning process, namely the direct model approach and the sequential approach. The direct approach needs more specific data that may not be available in developing countries. The sequential choice model approach utilizes various models for trip generation (e.g. regression models), trip distribution (e.g. gravity model), modal split (e.g. logit model) and route assignment (e.g. minimum path model). These models simplify the direct model via sequential steps. Thus, they are easier to calibrate and check for the reasonability of results for each of the steps, which is a useful practice [14]. The proposed program uses the sequential four-stage transportation planning process by applying mathematical models for the following phases:

• Trip production and trip attraction. • Forecasting of socio-economic data.

34

• Trip distribution. • Modal split. • Operational evaluation of road network. • Environmental assessment.

4.3.1 Models for Forecasting Socio-economic Data Depending on the annual growth factors of the independent variables, the socio-economic data affects the trip generation and attraction models can be forecasted according to the following mathematical equation:

[ ])*(1 vif GnVV += Where: Vf : Future value of the variable. Vi : Initial vale of the variable n : Forecasting time in years . Gv : Average annual growth factor of the variable. The main independent variables that affect the trip generation and attraction has been identified according to the regression analysis, the result indicates the following variables: X1 : Population number in transportation zone i (in 1000) X2 : Number of educants in transportation zone i(in 1000) X3 : Number of employees in transportation zone i(in 1000) X4 : Number of private cars in transportation zone i X5 : Number of population number having average annuale income <6000 L.E in transportation zone i (in 1000) X6 : Number of population number having average annuale income 6000 ~ 10000 L.E in transportation zone i(in 1000) X7 : Number of population number having average annuale income 10000 ~ 30000 L.E in transportation zone i(in 1000) X8 : Number of population number having average annuale income >30000 L.E in transportation zone i (in 1000). Y1 : Area of transportation zone j (km2)

35

Y2 : Area of stations in transportation zone j (m2). Y3 : Number of educational places (faculties, pre-primary, primary, middle and secondary) in transportation zone j. The independent variables that affect the trip generation / attraction in Tanta city have been forecasted by the proposed program. The annual growth factors of the socio-economc data of the study area of Tanta city are illustrated in Table (4-1). Growt factors are from data of (CAPMAS), and The Authorized Urban Plan (AUP) of Tanta city-2004 [18]. The socio-economic data, that affects trip generation / attraction for the base year 2000 is shown in Appendix (B). The socio-economic data, that affects trip generation / attraction for the target year 2030 is shown in Table (4-2), where: Table (4-1): Annual Growth Factors of Socio-Economc Data of Study Area

[18].

Socio-economic data Annual growth factor %

Population number. 1.2 Number of educants. 1.1 Number of employees. 1.0 Number of cars. 6.1 Average annual income. 2.5 Area of transportation zone in (m2). 0.952 Area of stations in transportation zone in (m2). 1 Number of educational places. 0.7

36

Table (4-2): Forecasted Independent Variables that Affect Trip Generation / Attraction for Tanta city for Year 2030.

Y3 Y2 Y1 X8 X7 X6 X5 X4 X3 X2 X1 zone 21.78 18000 0.695 6.125 35.7 11.9 3.325 6342.879 13.78 26.467 44.336 1 25.41 18000 0.834 9.45 55.65 18.55 5.25 10148.66 21.32 47.481 69.088 2 19.36 18000 0.834 3.325 19.6 6.475 1.925 3805.784 7.54 14.763 24.344 3 22.99 0 1.529 8.225 48.65 16.1 4.55 8879.974 18.72 41.895 60.384 4 8.47 0 1.112 1.75 10.15 3.325 0.875 1691.491 3.9 7.98 12.512 5 14.52 0 1.529 5.075 29.575 9.8 2.8 5497.275 11.44 24.605 36.72 6 9.68 0 1.112 3.15 18.725 6.125 1.75 3382.982 7.15 13.3 23.12 7 16.94 0 1.946 1.4 8.575 2.8 0.875 1691.491 3.25 5.719 10.608 8 16.94 0 0.834 0.7 3.85 1.225 0.35 845.604 1.43 2.66 4.76 9 10.89 0 1.112 6.475 37.975 12.6 3.5 6899.54 16.51 24.605 47.056 10 13.31 0 1.39 8.05 47.6 15.75 4.55 8781.49 20.67 30.59 58.888 11 27.83 18000 1.39 7.35 43.75 14.525 4.2 8044.841 20.67 31.92 54.264 12 43.56 18000 1.807 12.6 74.025 24.5 7 13561.36 34.97 55.86 91.8 13 7.26 0 1.112 1.225 7.525 2.45 0.7 1379.059 3.51 5.453 9.248 14

4.3.2 Models for Trip Generation / Attraction A trip can be defined as a single directional movement, for example home to work. It is classified to two main groups- home-based and non-home-based trips. Home based trips are those trips that have one trip end at a household and non-home-based trips are those trips between work and shop and business trips between two places of employment. The objective of trip generation stage is to understand the reason behind trip making behavior and to produce mathematical relationship to synthesize the trip making pattern, the bases of observed trips and household characteristics [43]. Trips can be referred to the location of home, working, shopping, education and other activities, besides the transport system. Demographic and socio-economic characteristics of population. Trip generation is sub-divided into trip production and trip attraction. The most common mathematical forms of trip generation model are multiple regression equations, trip rate models and cross-classification model.

37

The main models used in trip generation stage are: • Trip rate models. • Cross – classification models and • Multiple regression analysis.

Trip rate model is based on the determination of trip production and trip attraction rate associated with land use category and population of each land-use. Rate method is used for traffic impact analysis but, it does not consider other characteristics such as household size, income and auto ownership. Cross-classification model determines the number of trips individually by categorizing households according to certain socio-economic characteristics. it requires very detailed and accurate data of the transportation zones to predict trip generation and no mathematical model is driven. . Both types of data, required for the previous models, are not available for the study area. So, a multiple non-linear regression analysis is chosen for the trip generation model of the proposed program. Multiple regression analysis is a well-known statistical technique for fitting mathematical relationships between dependent and independent variables. It is divided into two types, 'aggregate analysis' and 'disaggregate analysis'. In aggregate analysis each Transportation Analysis Zone (TAZ) is treated as a one observation, where as disaggregate analysis threat individual household or person as an observation. Disaggregate analysis provides more accurate results than aggregate analysis [2]. This technique has been exploited fruitfully in a number of transportation planning studies. In the case of trip production equations, the dependent variable is the number of trips generated and the independent variables are socio-economic variables affects trip generation. It is based on the concept that the equilibrium between trips generated and factors affects the generation in a specific time is applicable at any future time. The multiple linear regression models proposed to estimate the trip generation for the study area are:

38

Qi = a0 + a1 x1i + a2 x2i + a3 x3i +.....+ an xni

Zj = b0 + b1 y1j + b2 y2j + b3 y3j +.....+ bn ynj Where: Qi : Number of trips produced from zone i. Zj : Number of trips attracted to zone j. x :Independent variable affects trip production. y : Independent variable affects trip attraction. a , b : Model parameters of independent variables. In the regression process, not all the variables can be treated as independent variables. Some variables may not have any relationship with the dependent variable and some variables may be represented by other independent variables. Model parameters (a and b) measure the relation between dependant and independent variables. The regression coefficient of the model (R2) measures the goodness of fit of the model. It ranges from zero to one.

Using a statistical analysis package (MedCalc v.11.3) to perform multiple linear regression analysis, two regression methods are considered, Forward method and Enter method. In Forward method, the regression enters significant independent variables sequentially. The independent variable is considered significant if it has a P-value less than 0.05, and not significant if it has a P-value more than 0.05. On the other hand, Enter method, the regression enters all independent variables in the model in one single step.

For the trip production model of the study area (Qi trip/day), eight independent variable were taken into consideration while performing the multiple regression process, these are:

X1 : Population number in transportation zone i (in 1000) X2 : Number of educants in transportation zone i(in 1000) X3 : Number of employees in transportation zone i(in 1000) X4 : Number of private cars in transportation zone i X5 : Number of population number having average annuale income <6000 L.E in transportation zone i (in 1000) X6 : Number of population number having average annuale income 6000

39

~ 10000 L.E in transportation zone i(in 1000) X7 : Number of population number having average annuale income 10000 ~ 30000 L.E in transportation zone i(in 1000) X8 : Number of population number having average annuale income >30000 L.E in transportation zone i (in 1000). The Forward method gives a model with a regression coefficient (R2=0.9), while the Enter method gives a model with a regression coefficient (R2=1.0) so, this model is used as a trip production model for the study area. Analysis of model parameters of different independent variables shows that, X5 (Number of population number having average annuale income <6000 L.E in transportation zone i (in 1000)) has the highest positive model parameter (19272.5277), while X8 (Number of population number having average annual income >30000 L.E in transportation zone i (in 1000)) has the highest negative model parameter (-11860.9205). X4 (Number of private cars in transportation zone i) has the least model parameter among all model parameters (5.9678). Table (4-3) shows model parameters of different independent variables in the trip production model of the study area. Table (4-3): Model Parameters of the Independent Variables of the Proposed

Trip Production Model.

Trip Production ( Qi Trip/day)

Variable a X1 -842.0327 X2 54.5475 X3 -1960.1120 X4 5.9678 X5 19272.5277 X6 -8629.1366 X7 5380.0964 X8 -11860.9205

constant of the equation a0=24.7073

For the trip attraction model of the study area (Zj trip/day), three independent variables were interpolated in the multiple regression process, these are:

40

Y1 : Area of transportation zone j (km2) Y2 : Area of stations in transportation zone j (m2). Y3 : Number of educational places (faculties, pre-primary, primary, middle and secondary) in transportation zone j. Forward method gives a model with a regression coefficient (R2=0.6), while the Enter method gives a model with a regression coefficient (R2=0.7), so, the model from the Enter method is used as a trip production model for the study area. The analysis shows that, Y1 (Area of transportation zone j (km2)) has the highest positive model parameter (9458.4687), while Y3 (Number of educational places - faculties, pre-primary, primary, middle and secondary - in transportation zone j) has the second highest poitive model paramenter of (986.1692). Finally, Y2 (Area of stations in transportation zone j) has the least model parameter of (0.08019). Area of stations is the area of the place where trip maker can change a transport mode to travel intercity or out of the city. This area is an expression of the trips attracted to the transport mode exists in the station. Table (4-4) shows model parameters of different independent variables in the trip attraction model of the study area.

Table (4-4): Model Parameters of the Independent Variables of the Proposed

Trip Attraction Model.

Trip Attraction ( Zj Trip/day)

Variable b Y1 9458.4687 Y2 0.08019 Y3 986.1692

constant of the equation b0 = -12961.0167 The final proposed trip production and trip attraction model of the study area has been formulated as:

iiii XXXQ 321 1120.19605475.540327.8427073.24 −+−=

iii XXX 654 1366.86295277.192729678.5 −++

ii XX 87 9205.118600964.5380 −+

41

jjjj YYYZ 321 1692.98608019.0 4687.94580167.12961 +++−= Where: Qi :Trips produced from transportation zone i (Trip / day). Zj :Trips attracted to transportation zone j (Trip / day). X1 : Population number in transportation zone i (in 1000) X2 : Number of educants in transportation zone i(in 1000) X3 : Number of employees in transportation zone i(in 1000) X4 : Number of private cars in transportation zone i X5 : Number of population number having average annuale income <6000 L.E in transportation zone i (in 1000) X6 : Number of population number having average annuale income 6000 ~ 10000 L.E in transportation zone i(in 1000) X7 : Number of population number having average annuale income 10000 ~ 30000 L.E in transportation zone i(in 1000) X8 : Number of population number having average annuale income >30000 L.E in transportation zone i (in 1000). Y1 : Area of transportation zone j (km2) Y2 : Area of stations in transportation zone j (m2). Y3 : Number of educational places (faculties, pre-primary, primary, middle and secondary) in transportation zone j. Socio-economic data used for the multiple regression model is represented in Appendix (B). These data represents the base year 2000. 4.3.3 Model for Trip Distribution Trip distribution models (destination models) are used to determine zone-to-zone trip interchange. Trips will be attracted to the zone with higher level of attractiveness. Destination choice modeling uses trip distribution or spatial interaction models. These models assume that the total trips from an origin node and the total trips to a destination node are known. The travel times (costs, or distances) are also known, and the result of the model is an origin-destination matrix that contains the trips from origins to destinations in its cells [32].

42

Among trip distribution models gravity model and Fratar model are used but, Fratar model took no account of changing spatial accessibility due to increased supply or changes in travel patterns and congestion besides, Frater model is not sensitive to impedance between zones which significantly affects the inter-zonal distribution of trips. The gravity model is by far the most widely used trip distribution technique. With mathematical models being complex, it is always possible to build more sophisticated urban transportation planning models introducing more variables and parameters in the analysis. These additional variables and parameters may contribute to a better model fitting, but require much more data brought into the model. For the distribution of all trips produced from transportation zone i of study area, and with the absence of statistics about trip purpose classification in study area, the third phase of this program uses the results of first and second phases and gravity model to get the total (O/D) matrix on the target year. Gravity model is a analogy of Newton’s Gravitational Model The number of trips between two areas is directly related to activities in the area represented by trip generated and inversely related to the separation between the areas represented as a function of travel time. The form of trip distribution gravity model is:

Where : Fij :Number of trips generated from zone i to zone j. Qi :Trips generated from zone i . Zj :Trips attracted to zone j. Kij : Socio-economic balance factor. Qi : Trips produced from zone i. Zj : Trips attracted to zone j. Wij : Impedance between zoon i and j (travel time, travel cost or travel

=∑ −

−

ijijj

ijijjiij

KWZ

KWZQF

**

***

γ

γ

43

distance). γ : Sensitivity factor of travel resistance. The bracketed term is the proportion or probability of trips produced by zone i that will be attracted to zone j. Thus, this term can measure the relative attractiveness between zones. According to [7], the socio-economic adjustment factor Kij is a calibration of the gravity model to incorporate effect between pairs of zones that not captured by the limited number of independent variables included in the model, this factor is assumed to be one for the study area. Originally, γ was assumed 2.0, by the analogy to the inverse square law of gravity. Hence, the name gravity model, in fact γ is rarely found to be exactly 2.0 in calibration of the gravity model, but this value will give a reasonable close approximation [26]. For the transportation zones of study area (Tanta City), the travel impedance between transportation zones is the travel time and the sensitivity factor is equal to 2. The proposed program uses the gravity model for trip distribution. Socio-

economic balance factor (Kij) of one and sensitivity factor (γ) of two are assumed. The distance (in Km) between centroids of zones is used as impedance between zones. Some times, after trip distribution, the total trip attractions to transportation zones Zj

0 do not tally with the predicted attractions Zj. An iteration procedure is done to balance the trip attractions. 4.3.4 Model for Modal Split The selection between several modes of travel is determined upon three types of factors: socio-economic status of the trip maker, characteristics of the trip and characteristics of the mode. One widely researched phase of the sequential travel-modeling procedure for urban transportation planning is the modal split analysis, which involves the allocation of total person trips (by all modes) to the respective modes of travel. Proportion of trips that uses each travel mode can be estimated statistically, or by using a modal split model.

44

Modal split models basically relate the probability of transit usage to explanatory variables or factors in a mathematical form. The empirical data necessary to develop these models usually are obtained from comprehensive (O/D) surveys in specific urban areas. In applying these models to predict the future transit usage, one must make the implicit assumption that the variables which explain the present level of transit usage will do so in much the same manner in the future. The main models used for modal split are:

• Pre-distribution models. • Post-distribution models. • Simultaneous trip distribution and modal split models. • The Disaggregate Behavioral models and • Multinomial logit models.

The Pre-distribution (or Trip End) Models are used to separate the trip productions in each zone into the different modes to distributed by mode-specific trip distribution models. The primary disadvantage of these models is that they cannot include variables related to transportation system characteristics. Pre-distribution models are not commonly used. The Post-distribution (or Trip Interchange) models are very popular because it can include variables of all types. However, conceptually it requires the use of a multimodal trip distribution model and currently such distribution models are not used commonly. The Simultaneous trip distribution and modal split models strive to estimate the number of trips between two zones by specific modes in one step directly following the trip generation phase. Conceptually and theoretically this type of a model has a sound basis, but it is not commonly used at this time. The Disaggregate Behavioral Logit models recognize each individual’s choice of mode for each trip instead of combining the trips in homogeneous groups. The underlying premise of this modeling approach is that an individual trip maker’s choice of a mode of travel is based on a principle called utility maximization. Finally, the multinomial logit model measures proportion of travels use each mode depending on the maximum utility. The form of the model is:

45

Um = a0 + a1 F1m + a1 F1m +……+ an Fnm

Where : P(m) : Proportion of travelers that use mode m. Um : Utility of mode m. a : Model parameter of mode m. F : Factor affecting the utility of mode m (trip cost or trip time or safety). Calibrations an statistics is done to get model parameters of any mode k. It is not even necessary to include the same variables in the utility equation of different modes. Sometime a new mode is introduced. In this case, it would be next to impossible to estimate the utility associated with the new mode base-year data required for the calibration of its utility function would be unavailable [7]. The proposed program uses two methods for modal splitting. The first is the constant factor modal split, and the second is multinomial logit model. 4.3.5 Model for Trip Assignment The trip assignment step is to assign the projected travel demand onto the transportation network so that potential problems such as congestion can be identified. In other words, trip assignment is to predict the number of travelers using various routes and, hence, the traffic volumes on the links of a transportation network. When vehicular trips rather than person trips are estimated, we call it a traffic assignment problem [25]. The basic principal of trip assignment is that assuming all travelers are rational thinking and will select the route of least perceived individual cost [43]. The factors affects the choice of a route are cost, time, comfort, safety and journey length. Depending on this principal, different trip assignment techniques are derived, such as All-or-Nothing assignment (AON), Diversion Techniques and Capacity Restraint Assignment.

( )∑

=

= n

m

u

u

m

m

e

emP

1

46

All-or-Nothing assignment method is known as The Shortest Path. It represents the shortest time path between the centroids of two transportation zones. This method is unrealistic because only one path between every two transportation zones is utilized, even if there may be another path with the same travel time or cost. In addition, it ignores the fact that link travel time is a function of traffic volume. The Diversion Technique is based upon that the traffic volume between two zones in divided among number of paths according to the utility of each path. It dos not take the capacity of a rout into consideration. Capacity Restrained Assignment has been developed to overcome the weakness of All-or-Nothing assignment method. Capacity Restrained is based on the principal that when the traffic flow increases towards the capacity, the speed of the traffic decreases from free flow speed towards maximum flow speed and the travel time also increases. In elastic traffic assignment problem, the minimum paths computed prior before assignment may not be the minimum paths after assignment because of traffic congestion. Several assignment procedures are done. In addition, at the end of each assignment, the traffic volume on each link is re-calculated and compared to the capacity of this link, and the new travel time of all the routes is calculated according to the following formulation:

……………………………..(1)

Where: ti :Trip time after assignment phase i. t0 : Trip time before assignment phase i (free-flow time). V :Assigned traffic volume (pcu/hr). C :Practical road capacity (pcu/hr). This form was developed by Bureau of Public Roads (BPR). It shows that the travel time on each link is a non-linear function of the total traffic volume on this link. In addition, it shows that at capacity the travel time is 15% higher than the free flow travel time. The free-flow time t0 is equal to 0.87 of travel time at practical capacity. After the first iteration, the new time changes to ti and consequently the

+=4

0i C

V15.01tt

47

speed on the link changes. To minimize fluctuation in loading, only one quarter of the difference between t0 and ti is applied. Thus:

…………………………….……(2)

Where: tn : New trip time after assignment. By combining the two equations, the following time model is reached:

…………………………(3)

This process may be continued for as much iteration as desired. Usually four iterations are adequate. The proposed program uses capacity restrained assignment model with the last mentioned model (No. (3)) 4.3.6 Model for Operational Evaluation of Road Network It is the most concern of the transportation planner to know how the road network will operate under traffic. Because oversaturated links should be deeply examined and reassignment on these links should be done. The concept of levels of service (LOS) uses measures to describe the operational conditions within a traffic stream, motorists and passengers. Parameters are selected to define (LOS) such as travel times, speeds, total delay, comfort, and safety. Methods for analysis capacity and service flow are incorporated in The Highway Capacity Manual (HCM) published by the Transportation Research Board (TRB) HCM defines six levels of service, from A to F, A is the highest level of service and F the lowest. These levels of service vary depending on the type of roadway or roadway element under consideration.

−+=4

tttt 0i0n

+=4

0 13.087.0C

Vttn

48

The parameters selected to define levels of service for each type of facilities are called Measures of Effectiveness and represent available measures that best describe the operation on the subject facility type. Table (4-5) presents the primary Measures of Effectiveness used to define levels of service for each facility type. An ideal condition of a facility is one for which further improvement will not achieve any increase in capacity. There are three methods used for the purpose of the operational evaluation of road networks, the first method is (HCM2000 - Two Lane Highway) which is used to determine the level of service on intercity two lane highways. The second and third methods are used to evaluate urban streets networks. The Second method is known as the average travel speed method (HCM2000 – ATS) method, while the third method is known as the probability method (HCM2010 –NCHRP 3-70) method.

Table (4-5): Primary Measures of Effectiveness for LOS Definition.

Type of Facility Measure of Effectiveness

Basic freeway segments Density (pc/km/ln)

Weaving areas Density (pc/ km /ln)

Ramp junctions Flow rates (pcph)

Multilane highways Density (pc/ km /ln), Free-flow speed (mph)

Two-lane highways Time delay (percent)

Signalized intersections Average control delay (sec/veh)

Unsignalized intersections Average control delay (sec/veh)

Arterials Average travel speed (km ph)

Transit Load factor (pers/seat, veh/hr, people/hr)

Pedestrians Space (sq ft/ped)

49

4.3.7 (HCM2000 - Two Lane Highway) method

A two-lane highway may be defined as a two-lane undivided roadway having one lane for use by traffic in each direction. Two-lane highways differs from multilane highways in that passing of slower vehicles requires the use of the opposing lane where sight distance and gaps in the opposing traffic stream permit. Also, multilane highways usually locates near urban areas and have higher geometric features than two-lane highways such as vertical and horizontal curves.

Two-lane highways are categorized into two classes: ClassI are two-lane highways that are major intercity routes, serve long distance trips and travelers expected to travel at high speed. ClassII are two-lane highways that function as access routes to class-I highways, serve short distance trips and travelers not necessarily expected to travel at high speed. Level of service criteria for two-lane highways address both mobility and accessibility concerns. The primary measure of service quality is percent time delay, with speed and capacity utilization used as secondary measures. LOS of classI two-lane highways is a function of percent-time spent following (PTSF), and average travel speed (ATS). While LOS of classII two-lane highways is determined as function of (PTSF). Percent time spent following (PTSF) can be defiend as the percent of total travel time that vehicles must travel in platoons behind slower vehicles due to inability to pass on a two-lane highway. Table (4-5) shows LOS criteria of classI two-lane highways while Fig (4-1) represents a graphical representation of LOS criteria of classI two-lane highways. Table (4-7) shows LOS criteria of classII two-lane highways.

LOS for class I and class II two-lane highways is determined as a function of PTSF. However, the ATS was selected as an auxiliary criterion for class I highways because ATS makes LOS sensitive to design speed. In addition, specific upgrades and downgrades can be analyzed by a directional-segment procedure. PTSF was assumed to describe traffic conditions better than density because density is less evenly distributed on two-lane highways than on multilane highways and freeways [31]. Basic conditions for a two-lane highway segment are :

• Lane widths ≥3.6 m

50

• Shoulders Clearance ≥1.8m. • Free flow speed of 100 km/hr for multilane highways. • All passenger cars. • No no-passing zones. • Level terrain. • No impediments to through traffic due to traffic control or turning

vehicles.

Under these ideal conditions, The capacity of a two-lane highway is 1.700 eq.pcu/hr for each direction. Considering directional split of traffic, 3.200 eq.pcu/hr is the capacity of both directions combined. Determining the LOS is done by the following sequence:

1- Determine the free flow speed (FFS). 2- Determine ATS using the value of FFS. 3- Determine PTSF, and 4- Use ATS and PTSF to determine LOS based on Table (4-6) and

Table (4-7).

Table (4-6): LOS Criteria for Two-Lane Highways Class I.

LOS

Percent Time-Spent-Following

(PTSF)

Average Travel Speed (ATS)

(Km/h) A ≤35 >88.5

B >35–50 >80.5–88.5

C >50–65 >72.5–80.5

D >65–80 >64.5–72.5

E >80 ≥64.5

51

Fig (4-1): LOS graphical criteria for two-lane highways in class I [44].

Table (4-7): LOS Criteria for Two-Lane Highways Class II.

LOS

Percent Time-Spent-Following

(PTSF) A ≤40

B >40–55

C >55–70

D >70–85

E >85 4.3.7.1 Determination of the Free Flow Speed (FFS) FFS is the theoretical speed of traffic over an urban street segment without signalized intersections or freeway or multilane highway segment under conditions of low volume when density is zero The following model is used to determine FFS [44]:

Where: FFS : Free-flow speed (Km/h).

ALS ffBFFSFFS −−=

52

BFFS : Base Free-flow speed (Km/h). f LS : Adjustment for lane width and shoulder width. Table (4-8).

f A : Adjustment for access points. Table (4-9). BFFS reflects the driver-desired speed when traveling on the road. Since

many factors influence the desired speed of drivers, no guidance on estimating the BFFS is provided. BFFS ranges between 72.5-104.5 Km/h. For study area BFFS is 85 Km/h. Access points density can be calculate by dividing the number of access point on both sides of the segment of a two-lane highway by the length of the segment in mile. Each access point in mile reduce BFFS by about 4 Km/h. Default values of access point density on two-lane highways can be used. 8 for rural, 16 for Low-Density Suburban and 25 for high-Density Suburban.

Table (4-8): Adjustment for Lane Width and Shoulder Width.

f LS (Km/h) Shoulder Width (m) Lane

Width (m) ≥0,<0.6 ≥0.6,<1.2 ≥1.2,<1.8 ≥1.8

≥2.7<3 10.3 7.7 5.6 3.5

≥3<3.4 8.5 6 3.9 1.8

≥3.4<3.7 7.6 4.8 2.7 0.6

≥3.7 6.8 4.2 2.1 0

Table (4-9): Adjustment for Access Points. Access Point

Density f A (Km/h)

0 0

10 4

20 8

30 12.1

40 16.1

53

4.3.7.2 Determination of the Average Travel Speed (ATS) The following model is used to determine the average travel speed [44]:

Where: ATS : Average travel speed for both directions (Km/h).

FFS : Free-flow speed (Km/h). V p : Passenger-car equivalent flow rate for peak 15-min period (pcu/h).

f np : Adjustment for percentage of non-passing zones. Table (4-10) represents the adjustment factor for percentage of non-passing zones.

Table (4-10): Adjustment factor for Percentage of Non-passing Zones.

f np (mi/h) No-Passing Zones (%)

Two-Way Flow Rate

V p (pc/h) 0 20 40 60 80 100

0 0.0 0.0 0.0 0.0 0.0 0.0 200 0.0 0.6 1.4 2.4 2.6 3.5 400 0.0 1.7 2.7 3.5 3.9 4.5 600 0.0 1.6 2.4 3.0 3.4 3.9 800 0.0 1.4 1.9 2.4 2.7 3.0 1000 0.0 1.1 1.6 2.0 2.2 2.6 1200 0.0 0.8 1.2 1.6 1.9 2.1 1400 0.0 0.6 0.9 1.2 1.4 1.7 1600 0.0 0.5 0,8 1.1 1.3 1.5 1800 0.0 0.5 0.7 1.0 1.1 1.3 2000 0.0 0.5 0.6 0.9 1.0 1.1 2200 0.0 0.5 0.6 0.9 0.9 1.1 2400 0.0 0.5 0.6 0.8 0.9 1.1 2600 0.0 0.5 0.6 0.8 0.9 1.0 2800 0.0 0.5 0.6 0.7 0.8 0.9 3000 0.0 0.5 0.6 0.7 0.7 0.8 3200 0.0 0.5 0.6 0.6 0.6 0.6

npp fVFFSATS −−= 00776.0

54

4.3.7.3 Determination of Model Parameters Equivalent passenger car demand flow, grade adjustment factors, and heavy vehicle factor are the main parameters used in (HCM2000 - Two Lane Highway) model. Vp is the equivalent passenger car demand flow rate. It is the adjustment of the hourly traffic flow due to grade, heavy vehicle and PHF. Vp can be calculated from the following model [44]:

Where: V : Traffic Hourly volume (pcu/h)

PHF : Peak-hour factor.(0.88 for rural areas and 0.92 for urban areas). f G : Grade Adjustment factor, Table (4-11). f HV : Heavy-vehicle adjustment factor.

PHF is the hourly volume during the maximum-volume hour of the day divided by the peak 15-min flow rate within the peak hour. It is a measure of traffic demand fluctuation within the peak hour. For study area (Urban Zones of Tanta), PHF = 0.92. Table (4-11): Grade Adjustment Factor (f G) to Determine Speeds on Two-

Way and Directional Segments. Type of Terrain Range of Two-

Way Flow Rates (pc/h)

Range of Directional Flow

Rates (pc/h) Level Rolling

0–600 0–300 1.0 0.71

> 600–1200 > 300–600 1.0 0.93

> 1200 > 600 1.0 0.99

The heavy-vehicle factor (FHV) accounts for the additional space occupied by these vehicles and for the difference in operating capabilities of heavy vehicles compared with passenger cars. It can be calculated through the following model [44]:

GHVp ffPHF

VV

∗∗=

55

Where: PT ,PR : proportion of trucks or buses and recreational vehicles(RVs) in the traffic stream assumed (10% rural, 5% urban). ET ,ER : passenger-car equivalents for trucks or buses and recreational vehicles RVs in the traffic stream, Table (4-12). For study area PT,PR = 5% , ET=1.3 and ER=1

Table (4-12): Passenger-Car Equivalents on Two-Lane Highway.

Type of Terrain Factor

Level Rolling

ET 1.3 2

ER 1.0 1.1

4.3.7.4 Determination of Percent Time Spent Following (PTSF) Percent time spent following (PTSF) is the percent of total travel time that vehicles must travel in platoons behind slower vehicles due to inability to pass on a two-lane highway. It can be calculated through the following model [44]:

Where: PTSF : percent time-spent-following. BPTSF : base percent time-spent-following for both directions of travel combined. BPTSF can be calculated using the following form:[44]

( ) ( )1111

−+−+=

RRTTHV EPEP

f

npdfBPTFSPTSF /+=

( )pveBPTSF

000879.01100 −−=

56

f d/np : adjustment for the combined effect of the directional distribution of traffic and of the percentage of no-passing zones. V p : Passenger-car equivalent flow rate for peak 15-min period (pcu/hr).

4.3.7.5 Determination of Level of Service (LOS)

To evaluate the road network, the equivalent passenger car demand flow rate (Vp) is compared to the capacity of the highway. If Vp is bigger than capacity, then LOS is F. If Vp is less than capacity the LOS is determined depending on the value of ATS and PTSF using Table (4-6) and Table (4-7). (HCM2000 - Two Lane Highway) method is used for the evaluation of two lane highways, and is not used to determine LOS of urban streets networks. For this reason, this method was not used in the proposed program for the case study. 4.3.8 Operational Evaluation of Urban Streets There are two methods, which used to the operational evaluation of the streets in urban areas, these are:

• The average travel speed method (HCM2000 – ATS). • The probability method (HCM2010 –NCHRP 3-70).

These methods have been utilized in the proposed program. 4.3.8.1 The Average Travel Speed (HCM2000 – ATS) method Urban street is a street with high relatively density of driveway located in urban areas with traffic signal not 3.00 km apart. In AASHTO hierarchy, urban streets are classified as Arterials, Collectors and local streets. Arterial streets (principal and minor) are roads that primarily serve longer trips, provide access to abutting commercial and residential zones and always have traffic signals. Collector streets provide both land access and traffic circulation within residential, commercial, and industrial areas. Their access function is more important than that of arterials and unlike arterials, their operation is not always dominated by traffic signals.

57

Local streets include all streets not on a higher system. These streets may be short in length or frequently interrupted by traffic control device. Travel distance on local streets is short, typically to the nearest collector street. local streets are city streets often have numerous driveways, as they are the addresses for most home. Fig (4-2) shows the functional classification of urban streets. The urban street network of the study area (Tanta City) consists of arterial streets such as Algalaa, Algesh, Elmoderia and Elnhas, and collectors such as Botros, Hassan Radwan and Saeed, and local streets such as Tut Ank Amon, Sabri and Elsayed Abd Elateef. Classification of urban streets is based on functional and design criteria.. The classes are designated by number (i.e., I, II, III, and IV) shown in Table (4-13) and reflect unique combinations of street function and design criteria shown in Table (4-14). The lesser the urban street class, the lower the driver's expectation for that facility and speed associated. The LOS of Urban Street is based on average travel speed (ATS) for the entire street under consideration and the urban street class. The (HCM2000 – ATS) method determines LOS of urban streets by calculating the Average Travel Speed (ATS). Table (4-15) shows LOS criteria for urban streets according to HCM2000. To calculate ATS, the proposed program calculates:

1- Uniform delay d1 and Incremental delay d2 , 2- Control delay d, and 3- Average travel speed (ATS).

Table (4-13): Classification Of Urban Street According To Functional And

Design Criteria. Functional Category Design

Category Principal Arterial Minor Arterial High-Speed I N/A

Suburban II II

Intermediate II III or IV

Urban III or IV IV

58

Fig (4-2): Functional Classifications of Urban Streets.

59

Table (4-14): Functional and Design Categories of Urban Streets.

Functional Category Criteria

Principal Arterial Minor Arterial

Mobility function

Very important Important

Access function

Very minor Substantial

Points connected

Freeways, important activity centers, major traffic

generators Principal arterials

Predominant trips served

Relatively long trips between major points and through-trips entering, leaving, and

passing through the city

Trips of moderate length within relatively small

geographical areas

Design Category Criteria

High-Speed Suburban Intermediate Urban Driveway/acce

ss density Very low density

Low density Intermediate Urban

Arterial type

Multilane divided;

undivided or two-lane

with shoulders

Multilane divided;

undivided or two-lane

with shoulders

Multilane divided or undivided; one- way, two-lane

Undivided one-way,

two-way, two or more lanes

Parking No No Some Significant

Separate left-turn lanes Yes Yes Usually Some

Signals/km 0.3-1.2 0.6-3 2-6 4-8

Speed limit 75-90 km/h 65-75 km/h 50-65 km/h 40-55 km/h

Pedestrian activity

Very little little Some Usually

Roadside development

Low density Low to medium

Medium to moderate High density

60

Table (4-15): LOS Criteria for Urban Streets According to HCM2000. Street class I II III IV

Range of FFS (Km/h)

88.5- 72.5 72.5 – 56.5 56.5 – 48.5 56.5 – 40.5

Typical FFS (Km/h) 80 65 55 45

LOS Average Travel Speed (Km/h)

A >72 >59 >50 >41 B >56-72 >46-59 >39-50 >32-41

C >40-56 >33-46 >28-39 >23-32 D >32-40 >26-33 >22-28 >18-23 E >26-32 >21-26 >17-22 >14-18 F ≤26 ≤21 ≤17 ≤14

4.3.8.1.1 Calculating of Uniform delay d1 and Incremental delay d2 Computing the average travel speed of an urban street requires the calculation of the intersection control delays. Because the function of an urban street is to serve a through traffic. Uniform delay d1 is calculated from the form [44]:

Incremental delay (d2) due to non uniform arrival and individual cycle time failure is calculated from the form [44]:

( ) ( )

+−+−=

cT

KIXXXTd

811900 2

2

−

−=

C

gX

C

gC

d

),1min(1

15.02

1

61

Where: X : (v/c) ratio for the lane group (degree of saturation). C : cycle length (sec). c : capacity of lane group (veh/h). g : effective green time for lane group (sec). T : duration of analysis period, usually (0.25-1 hr). K : incremental delay adjustment for the actuated control (0.55). I : incremental delay adjustment for the filtering or metering by upstream signals, Table (4-16).

Table (4-16): Incremental Delay Adjustment Factor I X of the

up stream 0.4 0.5 0.6 0.7 0.8 0.9 ≥1

I 0.922 0.858 0.769 0.650 0.50 0.314 0.090

4.3.8.1.2 Calculating of Control delay (d) Control delay is the delay for a vehicle approaching a signalized intersection that attributes traffic and calculated by the formula [44]:

Where: d : control delay (s/veh) d1 : uniform delay (s/veh) d2 : incremental delay due to non-uniform arrivals and individual cycle failures (s/veh) PF : progression adjustment factor. The arrival of a high proportion of vehicles on the green result from good signal progression. Progression primarily affects uniform delay so, PF applies to the adjustment is applied only to d1, and can be determined by the following model [44]:

21 d)PF(dd +=

62

Where: PF : progression adjustment factor. (may not exceed 1). p : proportion of all vehicles arriving during green. (may not exceed one). g/c : effective green time ratio fPA : adjustment factor for platoon arrival during the green, Table (4-17).

Table (4-17): Adjustment Factor for Platoon Arrival During the Green. Arrival

type (AT) AT1 AT2 AT3 AT4 AT5 AT6

fPA 1.00 0.93 1.00 1.15 1.00 1.00

Arrival type is a parameter that describes the quality of progression and the percentage of lane group arriving during green and red phase of the traffic signal [44]. A default value (AT3) is used for uncoordinated movements and a default value (AT4) is used for coordinated movements. Table (4-18) illustrates conditions under which every arrival type occurs.

Table (4-18): Arrival Types Occurrence Conditions [44]. Arrival type

(AT) Conditions Under Which Arrival Type Is Likely To Oc cur

AT1 Occurs for coordinated operation on two-way street where one direction of travel does not receive good progression. Signals are spaced less than 500m apart.

AT2 A less extreme version of Arrival Type 1. Signals spaced at or more than 500m but less than 1000m apart.

AT3 Isolated signals spaced at or more than 1000m apart (whether or not coordinated).

AT4 Occurs for coordinated operation, often only in one direction on a two-way street. Signals are typically between 500m and 1000m apart.

AT5 Occurs for coordinated operation. More likely to occur with signals less than 500m apart.

AT6 Typical of one-way streets in dense networks and central business districts. Signal spacing is typically under 250m.

[ ]

−

−=

C

gfP

PF PA

1

1

63

4.3.8.1.3 Calculating of Average Travel Speed (ATS) Urban street LOS is based on Average Travel Speed (ATS). Average travel speed is the length of the highway segment divided by the average travel time of all vehicles traversing the segment, including all stopped delay times. It is determined be the following formula [44]:

Where: ATS : average travel speed of through vehicles in the segment (Km/h). L : segment length (Km). TR : total of running time on all segments in defined section (sec), Table (4-19) d : control delay for through movements at the signalized intersection (sec).

Table (4-19): Segment Running Time (sec/km). Street class I II III IV

Typical FFS (Km/h) 80 65 55 45

Segment length (km) Running time per km TR (s/km)

0.1 63 68 - 129 0.2 63 68 88 99

0.4 63 68 75 81 0.6 55 61 75 81 0.8 49 58 75 81 1.0 48 57 75 81 1.2 47 57 75 81 1.4 46 56 75 81 1.6 45 55 75 81

The average travel speed method (HCM2000 – ATS) is used to determine the LOS of urban streets. For the case study (Tanta City), there is a decline in the number of intersections controlled by traffic signals (only four traffic signals on the entire urban street network), so, it is not possible to calculate delays using mentioned formulas. The average travel speed (ATS) of each

dT

L3600ATS

R +=

64

link of the urban network has been calculated manually by dividing the total travel time of the link (as an output of the program) at the end of the assignment stage, by the link length (as an input of the program). The total travel time after assignment (as an output of the program) is the sum of the initial travel time of the link and travel delays. The initial travel time of each link is calculated using a free flow speed of 50 Km/hr (calculated manually), then, LOS of each link is determined using the calculated (ATS) according to Table (4-15). The proposed program uses the (HCM2000 – ATS) method by calculating:

1. The percentage time delay on each link, (output of traffic assignment stage).

2. The delay time of each link after assignment using percentage time delay (output of the program) and initial travel time of the link, (manually).

3. Total travel time of each link by summing the initial travel time and the increase in travel time, (manually).

4. Average Travel Speed (ATS) by dividing the total travel time by the link length, (manually), and

5. Determine LOS from Table (4-15). 4.3.8.2 The Probability (NCHRP 3-70 – HCM2010) Method One recently completed study for the Highway Capacity Manual 2010 (HCM2010), National Cooperative Research Program Project 3-70 (NCHRP 3-70) incorporated to provide tools to better integrate the consideration of level of service in urban street design and analysis. This research led to cumulative Multimodal LOS (MMLOS) model. The Cumulative Logit Model developed with NCHRP 3-70 was found to be superior to the existing HCM 2000 models [16]. Determining the LOS is done through the following sequence:

1. Determine the probability that an individual will response with LOS “J” or worse, (Pr (LOS ≤ J)).

2. Determine the probability that driver will perceive LOS “J” , (Pr (LOS = J)). 3. Determine the LOS model, and 4. Determine the link LOS grade.

65

4.3.8.2.1 Determine the probability that an individual will response with LOS “J” or worse The LOS for an urban street in this model is a combination of stops and left turn lane presence. The LOS rating in this model is the weighted average of the sum of the probabilities of people reporting each LOS rating multiplied by a system of weights that gives greater weight to the proportion of people who perceive poorer level of service. Probability that an individual will response with LOS “J” or worse is calculated using the following form [11]:

Where: Pr (LOS ≤ J): Probability that an individual will response with LOS “J” or worse. J : A,B,C,D, E or F, (LOS grade). e :Exponential function (2.718…) αJ : Alpha, Maximum numerical threshold for LOS grade “J” Table (4-20). βK : Beta, Calibration parameters for attributes, Table (4-20). XK : Attributes (k) of the facility (stops/km and left turn lanes.

Table (4-20): Alpha and Beta Parameters for Recommended LOS Model [11].

Parameter Value Alpha Values

Intercept LOS F -3.8044

Intercept LOS E -2.7047

Intercept LOS D -1.7389

Intercept LOS C -0.6234

Intercept LOS B 1.1614

Beta Values X1 = No. of stops/km 0.253

X2 = Left-Turn-Lane Presence -0.3434

)(1

1)Pr(

∑+=≤

∗−− kkJ Xe

JLOSβα

66

The attribute, X1 (Number of stops per km) is the number of times a vehicle decelerates to a full stop, divided by the length of street being evaluated. The Stops/Km is calculated from the following equation [11]:

Where: L : Length of street. (mile) V/C : Volume to capacity ratio of the analyzed direction. A1, A2, A3 : Signal progression parameters, Table (4-21). For the case study (Tanta city) - with its only four existing signals - the signal progression is assumed to be (no signal coordination), thus the parameters applied are in the 3rd line of Table (4-21).

Table (4-21): Parameters for Stops Per Km Equation [11]. Signal Progression A1 A2 A3

Adverse Signal Progression 0.636 5.133 0.051

No Signal Coordination 0.478 6.650 0.028

Good Signal Progression 0.327 9.572 0.013 The attribute X2 (Left-Turn-Lane Presence) takes on the values of (1) if exclusive left-turn lane at intersections, (0) if not. For the case study (Tanta city), the street network has no special left turn lanes The attribute Left-Turn-Lane Presence is assumed to be (0). 4.3.8.2.2 Determine the probability that driver will perceive LOS “J” The probability of obtaining an LOS rating equal to “J” is the difference between the probability of rating the facility at (LOS equals J or lower) and the probability of rating the facility at (LOS J-1 or lower). The probability that driver will perceive LOS “J” is calculated from the model [11]:

+

−+

−+= 321 11/ AC

V

C

VAALkmStops

)1JLOSPr()JLOSPr()JLOSPr( −≤−≤==

67

Where: J : A,B,C,D, E or F, (LOS grade). 4.3.8.2.3 Determine the LOS model The LOS model takes the following form [11]:

Where: J : A,B,C,D, E or F, (LOS grade). Wj : Numerical equivalent of LOS (1 for A, 2 for B, 3 for C, 4 for D, 5 for E and 6 for F). 4.3.8.2.4 Determine LOS Grade Level of service of urban street links can be classified with regard to the result of the LOS model output. If any link hourly volume/capacity ratio (v/c) exceeds 1.00, that link is considered to be operating at LOS (F). Table (4-22) illustrates LOS grade for different ranges of the LOS model.

Table (4-22): LOS Letter Grade Numerical Equivalents [11]. LOS Letter Grade LOS Model

A Model ≤2.00

B 2.00 < Model ≤2.75

C 2.75 < Model ≤ 3.50

D 3.50 < Model ≤ 4.25

E 4.25 < Model ≤ 5.00

F Model > 5.00 The proposed program uses the (HCM2010 –NCHRP 3-70) method to analysis the capacity and traffic volumes assigned, and thus determines the LOS of the road network links. This method is used to evaluate the street network of the study area (Tanta city). The program processes the following steps on each link of the street network:

JJ

WJLOSelLOS *)Pr(mod6

1∑

=

==

68

1. Calculating number of stops/km using the signal progression type. 2. Calculating probability that an individual will response with LOS “J”

or worse using alpha and beta parameters, (Pr (LOS ≤ J)). 3. Calculate the probability that driver will report LOS “J”, (Pr

(LOS=J)). 4. Calculate the values of the LOS model. 5. Determine the LOS grade according to Table (4-22).

4.3.9 Models for Environmental Assessment In sustainable urban transportation, the planning cannot be simply based on the traditional four-step models only. Given the policy goal of planning for sustainable urban transportation, it becomes apparent that urban transportation policy and planning cannot be simply based on the traditional four-step models only [17]. Government and environmental agencies have clearly defined motorized transport as a significant source of air pollution and noise emanation, and should therefore be reduced and set as a top priority in environmental protection strategies by all governments. Up to the 1960s, the evaluation of transportation systems sector was based on the assessment of transport cost, time and level of service, neglecting the indirect effect of the transportation system on the environment. The planning process has given considerable emphasis of the effect of transportation alternatives on the environmental consequences, Cars, trucks, buses, and trains - the world relies on transportation to fuel its economic growth and development. Increased transportation has, until now, gone hand in hand with increases in greenhouse gas (GHG) emissions, vehicle emissions of nitrogen oxides (NOx), acid rain and noises. It is essential that the environmental impacts of traffic be understood and taken into account in any transportation decision-making context. According to the most recent data available, the annual growth rate in CO2 emissions by the transport sector in developing countries is projected to be 3.4% [24]. Moreover, in the context of rising motorization, the environmental effects of transport tend to be more negative. For example, estimates suggest that the developing world’s share of cars will rise from 25% in 1995 to 48% in 2050 (United Nations, 2001) [39]. The growing demand of travel is the major factor, and is largely due to the increasing population in urban areas. Highway vehicles are the main source

69

of air pollution emissions that contribute to about 59% of carbon monoxide and 40% of nitrogen oxides. Fig (4-3) and Fig (4-4). What was considered a big alarm, European Community Committee evaluated that world emission of (GHG) resulting from transport sector will increase by 45.8% during the period from 1990 to 2015.

Fig (4-3): Major sources of carbon monoxide [46].

Fig (4-4): Major sources of NOx [46].

Because of these reasons, future transportation scenario analysis has become more and more critical for air quality management and transportation policy makers. Also, Predicting environmental impacts is essential when performing an environmental assessment on urban transport

70

planning. For these reasons, the proposed program take into account two main environmental evaluation models, these are:

• Air Pollution evaluation model. • Noise pollution evaluation model.

4.3.9.1 Air Pollution model Emissions can be calculated on a link-by-link basis, trip basis, or by considering travel associated with different categories of roads, with aggregation of emissions presented at the regional level. Estimates of vehicle emissions vary depending on the approach used to aggregate travel data. In general, emissions are lowest when using a trip-based approach, and highest when using link-by-link approach. The maximum difference between the two approaches was less than 10% for each pollutant analyzed [28]. Egypt emissions represent almost 0.6% of the global emissions. In Egypt, The transport sector is a major consumer of fossil fuels; transport sector is responsible for 28% of the final energy consumption and therefore contributes a significant share of the country's emissions of greenhouse gases (GHGs). In 2003–2004 the transport sector was responsible for 29.16 per cent of overall energy consumption and about 31.6 million tones of CO2, representing nearly 26 per cent of the energy-related CO2 emissions [37]. The first national communication to the UN Framework Convention on Climate Change (UNFCCC) outlined the overall national policy to address the challenge of climate change. A number of options for the transport sector outlined were assessed, of which improvement of vehicle maintenance and tuning-up of vehicle engines, using compressed natural gas as a vehicle fuel, extending metro lines to newly developed cities and encouraging private sector participation in financing and managing the new metro lines [13]. A vehicle emissions testing, engine-tuning and certification program were established in Egypt in order to improve fuel efficiency and air quality. This test has become mandatory for vehicle licensing. In Tanta City, the average vehicle age is relatively old and most vehicle are operating since 30 years with low motor burning efficiency resulting an incomplete combustion of fuel. Vehicles motors are of old generation that powered by diesel and benzene fuel, which have high level of particulates and nitrogen oxides emissions which directly affect the roadside air quality. Passenger cars are expected to constitute the fastest-growing category over

71

the next few years due to economic growth, gradual decreases of fees duties on imported cars and increased numbers of locally assembled ones. Because of the increasing numbers of vehicle types, fuel types, pollutants and emission modes, transportation emission models are becoming more complex and comprehensive. The proposed emission module uses the following trip based module to estimate the expected carbon dioxide (CO2), nitrous (N2O) and methane (CH4) of certain transport system [33]:

Where: Q : Total (CO2) and (CO2) equivalent emission in kg

qm.CO2 : (CO2) greenhouse gas emission from transport mode m in kg CO2 / pass.km for passenger transport or kg CO2 / ton.km for freight transport. m : Transport mode (car, bus, truck). qm.CH4 : (CO2) equivalent emission from(CH4) in kg CO2 / pass.km. qm.N2O : (CO2) equivalent emission from(N2O) in kg CO2 / pass.km. TVm : Transport productivity of mode m, in pass.km or ton.km.

Where: a m.CO2 : factor of calculating (CO2) emissions from primary energy in kg CO2 /MJ. Table (4-23) bm : specific primary energy consumption in MJ/veh.km, Table (4-23) cm : the occupancy rates (passenger/mode unit). F1 , F2 : factors present behavior of car driver and status of transport mode (life time for car, bus and truck), assumed to be

( ) mONmCHmCOm TVqqqQ *242 ... ++=

mCHmCHmCHm CeFFbaq /****444 21.. =

mONmONmONm CeFFbaq /****222 21.. =

m21mCO.mCO.m C/F*F*b*aq22

=

72

(1.35). a m. CH4 : factor of calculating (CH4) emissions from primary energy consumption in kg CH4 /MJ, Table (4-23) am. N2O : factor of calculating (N2O) emissions from primary energy consumption in kg N2O /MJ, Table(4-23) e CH4 : coefficient to convert CH4 to CO2 equivalent emissions, Table (4-23)

e N2O : coefficient to convert N2O to CO2 equivalent emissions, Table (4-23)

Table (4-23): Parameter to Estimate Air Pollution From Transportation Sector [33].

Parameter Value

a m.CO2

0.00369 for electricity tram and train. 0.0741 for diesel vehicle. 0.0693 for benzene vehicle. 0.0561 for natural gas vehicles.

bm

3.6 for benzene vehicle. 2.9 for diesel vehicle. 14.02 for bus and trucks. 7 for mini-bus. 78.9 for trains. 20 for trams. For natural gas: 2 for vehicles 6 for bus

a m. CH4 0.0974*10-3 for benzene vehicle. 0.008*10-3 for diesel vehicle 0.3*10-3 for natural gas vehicles.

am. N2O 0.022*10-3 for benzene vehicle. 0.009*10-3 for diesel vehicle 0.002*10-3 for natural gas vehicles

e CH4 11 e N2O 270

73

4.3.9.2 Model for Noise Pollution Noise is some sounds which are unwanted. Transportation vehicles are the worst offenders, with aircraft, railroad stock, trucks, buses and motorcycles all producing noise. (WHO)'s Press Releases (1998) reported that noise not only damages people’s hearing but interferes with sleep patterns and causes cardiovascular problems through increased stress and aggression levels. Ischemic heart disease and hypertension have been linked to noise levels of less than 70 decibels. Noise has also been proven to affect human’s ability to concentrate and learn Transportation operations are major source of noise in urban environment. Sources of highway noise are tire-pavement interaction, acceleration rate, deceleration rate, engines, exhaust systems and aerodynamic sources. Because noise vanishes with distance, the transportation noise problem is related to transportation corridors. Quantifying noise depends on the noise source and purpose for the noise measurement. The intensity of sound is measured in a unit called a decibel (dB). For measuring noise levels of transport traffic operations, a weighted decibel of weight (A) dB(A) is used. Many noise-quantifying models were developed. Wesler J. E. [48] traced the first formula of traffic noise in the following formulation:

L : mean noise level for in dB. V : traffic volume (veh/hr). D : distance from traffic line to the observer (ft). This model dose not take into consideration that the traffic volume can occurs at different speeds. Since then, efforts have been done to calibrate noise measurement models for various traffic conditions. In the program, the following model published in [34] is applied in the program to estimate the transport noise level on roads.

Dlog20Vlog5.868L e.m −+=

6.27)5

1log(10)40500

log(33log10. −+++++=V

P

VVQL em

74

Where: L m.e : Mean noise level at the center of road dB(A). Q : Traffic volume (pcu/hr). V : Traffic speed (km/hr). P : Proportion of truck in traffic. A correction of +5 dB(A) is added in case of rigid pavement and a correction of +13 dB(A) is added in case of rain. Another correction (∆) is added resulting from the distance between observation place and center of road is calculated from the following formula:

Where: d : distance from observer to center of road (m). Noise limits prescribed by Federal Highway Administrations (FHWA) in (U.S.A) require that the external noise level in residential areas should not exceed 65 dB(A) and the internal noise level in the building is limited by 50 dB(A) [26]. 4.3.10 Modules of The Program Finally, the proposed program uses all the previously mentioned models to perform the transportation planning process by the sequence modules. Fig (4-5) shows the proposed program modules.

)5.13

dlog(10∆ −=

75

Start

Target Year Socio-economic Data Affect

Trip Generation

Annual Percentage Growth Factor of

Socio-economic Data

Base Year and Target Year

Target Year Socio-economic Data

Trip Production and Trip Attraction

Models

Target Year Socio-

economic Data

Target Year Trip Production (Qi and Zj)

Travel Impedance Matrix Target Year Trip Production

Target Year Origin Destination (O/D) Matrix

Target Year (O/D) Matrix

Modal Split Ratios or Utility Function

Target Year Modal Split (O/D) Matrices

76

Fig (4-5): Modules of Transportation Planning Program.

Target Year (O/D) Matrix

Road Network Description Matrix

Target Year Trip Assignment Table

Target Year Trip Assignment

Table

Signal progression type

Level of Service (LOS) of Road Network Links

Traffic Volumes Transportation

Modes Emission Characteristics

Total Emissions of Transportation Modes

Traffic Volumes Speed on Road network Links

Noise Levels on Road Network Links

Chapter 5

Analysis of Socio-Economic Data of the Study Area andZoning System

77

5.1 Introduction The transportation planning process begins with data about the existing Transportation situation. Data of transportation analysis zone is the major input to transportation analysis model as it is the main indicator of transportation attraction and production forecasting. Data contribute to the transportation planning process is about the existing and historical travel pattern, urban development, land-use, employment, transport system and socio-economic data. Just as urban transportation planners need socio-economic data by traffic zones, urban management requires socio-economic data by small areas to estimate the demand for public services [30]. The transportation planning process requires that voluminous amounts of data be available in a readily usable format. Socio-economic data describes both demographic and economic characteristics of the region. This data should be provided thought annual statistics performed in the study area (country or governorate or city) or through interview survey, and must be stored in such a manner that they can easily be identified, retrieved, summarized, and updated. In Egypt, some socio-economic data could be obtained from Central Agency for Public Mobilization and Statistics (CAPMAS). But, the availability of specific data about the existing transportation situation in Egypt's urban areas is not sufficient. Leakage of the required transportation planning data is a constant problem faces transportation planners in developing countries. The aim of this chapter is to collect and analyze transportation planning data of Tanta city as case study to reach the factors affects transportation in the study area. To achieve this aim the study area was firstly described, the socio-economic data were analyzed and the present situation of the transportation system in study area was also analyzed.

5.2 Study Area and Zoning System The Arab Republic of Egypt is an Arab state in Northern Africa. It has the following geographic characteristics:

• Location: Middle East, bordering the Mediterranean Sea, between Libya and the Palestine, and the Red Sea north of Sudan, and includes the Asian Sinai Peninsula.

• Geographic coordinates: 27 00 N, 30 00 E

78

• Capital: Cairo. • Area: total 1,001,450 sq km, land 995,450 sq km, water 6,000 sq km. • Terrain: vast desert plateau interrupted by Nile valley and delta.

Economic conditions in Egypt have started to improve considerably after a period of stagnation from the adoption of more liberal economic policies by the government, as well as increased revenues from tourism and a booming stock market. Egypt's economy depends mainly on agriculture, media, petroleum exports, and tourism, information technology (IT) sector has been expanding rapidly in the past few years. Fig (5-1) represents a map of Egypt.

Fig (5-1): Map of Egypt [23]. Gharbia Governorate is one of the governorates of Egypt. It is located in the north of the country, south of Kafr El-Sheikh Governorate, and north of Monufia Governorate. The total area of Gharbia governorate is 25,400 km²,

79

making it the tenth largest governorate of Egypt. Fig (3-2) shows the location of Gharbia Governorate in Egypt.

Fig (5-2): Location of Gharbia Governorate in Egypt [22].

Tanta city is the capital of Gharbia Governorate and locates in the middle of Nile Delta. Tanta is 94 km north of Cairo and 130 km southeast of Alexandria. Its Geographic coordinates are (30° 47' 28N, 30° 59' 53E). Tanta is the capital of Gharbia Governorate. It is the cotton-ginning center and the main railroad hub of the Nile Delta. The biggest commercial city in the delta. The biggest university in the delta locates in Tanta. Biggest and most common streets in Tanta are Al- Bahr(algeish) street, Al-Galaa Street, Al-Nahaas Street and Saeed Street. Gharbia Governorate is divided into 8 administrative centers (Tanta- Almahalla Alkobra - Kafr El Zayat - Basyoun - Qutor – Elsanta – Samanod -Zefta) Tanta is the biggest administrative center in Gharbia Governorate. The transportation planning process in this thesis is applied to Tanta city based on the available data of transportation zones and origin destination matrices provided by The Authorized Urban Plan (AUP) of Tanta city-2004 [18], done by General Organization of Physical Planning (GOPP). Administratively, Tanta city is divided into two main residential districts: First district and Second district. Every district is divided into 7 zones as shown in Table (5-1) and Fig (5-3).

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Table (5-1): The Administrative divisions of Tanta city.

First district Second district

1/1 Waboor Elnoor 2/1 Quhafa

1/2 Midan Elsaa 2/2 Almalga

1/3 Eldawaween 2/3 Ali Agha

1/4 Elborsa 2/4 Elsalakhana

1/5 Kobri Elmahata 2/5 Elemari

1/6 Kafr Segar 2/6 Elkafr Elsharkya

1/7 Sedi Mrzoq 2/7 Sabri

The zoning system is the first step towards the traffic analysis. Zoning system could be in various levels (national, regional or urban). For the transportation planning study of Tanta city, depending on the administrative division of the city and according to the penetration of the railways inside the city, and according to (AUP), the study area was divided into three transportation zones each includes number of sub-zones as shown in Table (5-2) and Fig (5-4).

Table (5-2): Transportation Zones in Study Area.

Transportation Zone Sub-Zones

1 Quhafa,Waboor Elnoor,Ali Agha,Almalga,Midan Elsaa,Eldawaween,Elborsa,Elkafr Elsharkya and Sabri

2 Elsalakhana and Elemari

3 Kafr Segar,Kobri Elmahata and Sedi Mrzoq

81

Fig (5-3): The Administrative divisions of Tanta city.

82

Fig (5-4): Transportation Zones in Study Area.

Socio-economic data collected from AUP is given only of the basis of the three zones, but the population number, number of educates and number of employees are achieved on the basis of the sub-zones. Depending on the ratio of the population number of all sub-zones and the main zones, other socio economic data (number of private cars, number of population number in different income categories, number of educational places) has been created on the basis of the fourteen sub-zones. The main reason of these calculations is to achieve more microscopic scale by calculating the

83

proposed model program. The following equation has been used to generate the sub-zones socio-economic data:

Where: Dsz : Socio-economic data of the sub-zone. Dz : Socio-economic data of the main zone that the sub-zone belongs. Psz : Population number of the sub-zone in year 2000. Pz : Population number in year 2000 of the main zone that the sub-zone belongs. Appendix (A) represents the socio-economic data of the 3 main zones in year 2000. Appendix (B) shows the following:

• Ratio of sub-zone population to main transportation zones population for the base year 2000.

• Socio-economic data on the basis of the fourteen sub-zones in year 2000.

5.3 Analysis of Socio-economic Data in the Study Area Database is the first step in every transportation planning process. Travel demand analysis is based on the concept that travel is a derived demand of social and economical activities. In Urban Transportation Planning Process (UTPP), the relation between travel demand and socio-economic data can be obtained through the analysis of zonal demographic data, such as population, households, and income. This part of research analyzes the following socio-economic data of Tanta city:

1- Population and household. 2- Education. 3- Employment. 4- Income. 5- Car ownership.

z

szzsz P

PDD *=

84

5.3.1 Population and Household Population is the total number of people living within a zone. Gharbia Governorate represents 5.5% of the population of Egypt. In 1995 population of Gharbia Governorate was 3.319 million; the urban population of the governorate was 1.107 million. Tanta is ranked second biggest population after Almahalla Alkobra of all cities of Gharbia Governorate. It contains 23.4% of the urban population of the governorate in 1995 with population of 379 thousand. Males represented 50.7%, while females represented 49.3%. The city turned from a population attracting city in 1976 to a population expulsion city in 1986 and will continue if there are no soon social, economical and urban development programs. The average annual growth rate of population in urban areas of Tanta 1.76% in 1991 and decreased to 1.3% in 1995. Analyzing the data collected by (CAPMAS) in 2000, Transportation zone (1) represents the highest population of all transportation zones with 210.144 thousand inhabitants which represents about 52.2% of whole population number in Tanta city. Zone (3) lies in the second rank with 114.242 thousands inhabitants which represents 28.4% of whole population number in the city. Zone (2) is ranked third with 77.929 thousands inhabitants with a percentage of 19.4 of whole population number in Tanta. From 2000 to 2006 the highest change in the number of population among the three transportation zones was in zone (1) with an increase of about 17.491 thousand in the number of population, because the immigration to Tanta city from the surround rural areas is oriented to zone (1) as new extension of the residential areas in addition to the existence of health services and educational places in this zone. While the change is very small and can be neglected in zone (2) and zone (3). In 2006 zone (1) still lies in the first rank with about 54.2% of whole population number in Tanta city while Zone (3) still lies in the second rank with 27.2% of whole population number in the city and zone (2) is ranked third with a percentage of 18.6 of total Tanta city population number. Table (5-3) represents the population numbers in transportation zones of study area for year 2006 and Table (5-4) represents classification of population in study area according to its transportation zones year 2000. The difference between the population numbers in 2000 and 2006 is shown in Fig (5-5).

85

Table (5-3): Population Numbers of Transportation Zones Year 2006.

Transportation Zone

1 2 3 Total Tanta City

Population (in 1000)

227.635 77.929 114.241 419.805

% of total Tanta city Population Number.

54.2 18.6 27.2 100

Table (5-4): Classification of Population of Transportation Zones Year 2000.

Transportation Zone 1 2 3 Total Tanta City

(in 1000) 210.144 77.929 114.242 402.315 Population

% 52.2 19.4 28.4 100 (in 1000) 116.620 39.503 57.235 213.358

Male % 55.5 50.7 50.1 53

(in 1000) 93.524 38.426 57.006 188.956 Female

% 44.5 49.3 49.9 47

Classification of population in study area according to its transportation zones for the year 2000 indicates that 55% of the population in zone (1) is male and 44.5% is female, Male represents approximately the same percentage of population in zone (2) and zone (3) with about 50.5% while Female represents about 49.5% of the population.

86

050

100150200250300350400450

Po

pu

lati

on

(in

100

0)

Zone1 Zone2 Zone3 Total

Pop.2000 (in 1000)Pop.2006 (in 1000)

Fig (5-5): Difference between the Population Numbers in Study Area in the year 2000 and 2006.

Population density is the average number of inhabitants living on the unit area of land. The population density of Tanta city rose from 33.95 (1000inh/km2) in 2000 to 35.43 (1000inh /km2) in 2006. Zone (2) has the highest population density in 2000 and 2006 while zone (3) lies in the second place and zone (1) is in the third rank. The Analysis of the change in population density in transportation zones of the study area between year 2000 to 2006 indicates that there was no significant change in population density in zone (2) and zone (3) while the biggest change in population density between 2000 and 2006 was in zone (1) with about 2.5 (1000inh /km2) which represents an increase of 3.8% in population density. The reason for the significant population density change in zone (1) is the immigration from rural areas around Tanta to urban Tanta, this immigration is directed to transportation zone (1) as a new extend of the residential areas and because of the institution of new educational places (schools and collages) in this transpiration zone. Table (5-5) shows the change in population density of the transportation zones in study area in 2000 and 2006.

87

Table (5-5): Change in Population Density of Transportation Zones in Study Area Between Year 2000 and Year 2006.

Transportation Zone 1 2 3 Total Tanta City

Area (km2) 6.894 1.843 3.113 11.85

Population Density in 2000 (1000inh /km2)

30.48 42.28 36.69 33.95

Population Density in 2006 (1000inh /km2)

33.01 42.29 36.70 35.43

% Increase in Population Density

8.30 0.02 0.03 4.36

Analysis of age characteristics in Tanta city indicates that 63.4% of the people between 15-64. This age level has the maximum trip rate of 2.8 trip/person/day. Fig (5-6) shows the trip rate for age groups. Age characteristics analysis shows also the following facts:

1- The number of Population between 0 and 14 years represents 32.3% of the total population number.

2- The number of Population between 15 and 64 years represents 63.4% of the total population number.

3- The number of Population (65 +) years old represents 4.3% of the total population number.

Age characteristics of study area are illustrated in Fig (5-7). According to data of (CAPMAS), the analysis of population and household characteristics in Tanta city shows that:

• The average population age is 25.4 years old. • The average family size is 3.7 persons. • The expected average annual growth rate of population 1.2 % during

the period from (2000 to 2030)

88

2.8

1.4

2.2

0

0.5

1

1.5

2

2.5

3

(0-14) years old (15-64) years old (65+) years old

Tri

p/P

erso

n/D

ay

Trip Rate/ Agegroup

Fig (5-6): Trip Generation Rate for Age Groups of Study Area.

32.3%

63.4%

4.3%

(0-14) yearsold

(15-64) yearsold

(65 or more)

Fig (5-7): Age Characteristics of Study Area.

5.3.2 Education Analysis of the numbers of students in the transportation zones of the study area in 2000 indicates that zone (1) has the highest percentage of educates. 66% of population number in the zone is educates representing 55.4% of all educates in the city. Zone (3) lies in the second rank with an educates percentage of 61.4% of population number in the zone representing 28% of

89

whole educates in Tanta city while zone (2) is ranked in the third place with an educates percentage of 53.3% of population number in the zone which represents 16.6% of all educates in the city. Number of educates in the zone gives an indicator of more travel demand in it. The analysis also shows that percentage of educates in Tanta city reduced from 71% in 1986 to 62.3% in 2000 representing a reduction of 12.25%. Table (5-6) and Fig (5-8) illustrates the number of educates according to transportation zones of study area in year 2000.

Table (5-6): Number of Educates in the Transportation Zones of Study Area

in Year 2000.

Transportation Zone 1 2 3 Total

Educates (in 1000)

138.821 41.531 70.116 250.468

% Educates Related to Zone Population

66 53.3 61.4 62.3

% Educates Related to Tanta City

Educates 55.4 16.6 28 100

0

20

40

60

80

100

120

140

Ed

uca

tes

(in

100

0)

Zone 1 Zone 2 Zone 3

Fig (5-8): Number of Educates in The Transportation Zones of Study Area

in year 2000.

90

5.3.3 Employment The number of employment in study area was 142341 in year 2000 and reached 148013 in 2006 with a total increase percentage of about 4% and yearly growth rate 0.66%. The analysis of the number of employees in the transportation zones of the study area in 2000 and 2006 shows that the highest number of employees locates in zone (1) and the lowest number locates in zone (2). Zone (3) is raked second. On the other hand, in year 2000 and 2006, employees represent the same percentage of the total population number in the zones. Employees represent a percentage of 39.9% of the total number of population in zone (3). More than a percentage of 36.8% of the total number of population in zone (2), while zone (1) is ranked third with an employment percentage of 32.4% of the total number of population in the zone. Table (5-7) and Table (5-8) show the number of employment in the transportation zones of study area in 2000 and 2006 respectively. Fig (5-9) shows a comparison of the number of employment in study area between 2000 and 2006.

Table (5-7): The Number of Employees According Transportation Zones in

Year 2006.

Transportation Zone


Workers (in 1000)

73.765 28.673 45.575 148.013

% of Population

32.4 36.8 39.9 35

Table (5-8): The Number of Employees According Transportation Zones in Year 2000.

Transportation Zone


Workers (in 1000)

68.086 28.673 45.582 142.341

% of Population

32.4 36.8 39.9 35

91

0

20

40

60

80

100

120

140

160E

mp

loye

es (i

n 1

000)

Zone 1 Zone 2 Zone 3 Total

Employees2000(in1000)

Employees2006(in1000)

Fig (5-9): Comparison of the Number of Employment between year2000

and 2006 in the Study Area. 5.3.4 Income Data collected about household income in the study area in year 2000 describes household income levels in 4 categories. Analysis shows that the percentage of population number with annual income less than 6000 L.E is 5.9% of the total population number in the study area. Percentage of population number with annual income 6000 ~ 10000 L.E is 20.8% of total population number in study area while more than 62% of population of study zones has an average annual income between 10000 ~ 30000 L.E. People with average annual income more than 30000 L.E represents 10.6% of the total population number in the study area. Analysis shows also that the average annual income in all zones is almost constant and equal 21327 L.E. Table (5-9) and Fig (5-10) show a comparison between population number in every annual income category for different transportation zones. Fig (5-11) illustrates the distribution of the average annual income of population number in study area year 2000.

92

Table (5-9): Annual Income of Population Numbers of Transportation Zones in Year 2000.

Transportation Zone 1 2 3

Total Tanta City

(in 1000) <6000 L.E 12.398 4.598 6.739 23.735

6000 ~ 10000 43.710 16.209 23.759 83.678

10000 ~ 30000 131.760 48.861 71.618 252.239

>30000 L.E 22.276 8.261 12.108 42.645

Average Annual Income

21327 L.E

0

50

100

150

200

250

300

Pop

.No.

(in

100

0)

Zone 1 Zone 2 Zone 3 Total

Pop. No (in1000)with annual income<6000 L.E

Pop. No (in1000)with annual income6000 ~ 10000 L.E

Pop. No (in1000)with annual income10000 ~ 30000 L.E

Pop. No (in1000)with annual income>30000 L.E

Fig (5-10): Comparison between Population Number in Every Annual Income Category of Different Transportation Zones for Year 2000.

93

5.9%

62.7%

10.6%

20.8%

Less than 6000 L.E

6000 - 10000 L.E

10000 - 30000 L.E

More than 30000L.E

Fig (5-11): Distribution of the Average Annual Income in the Study Area

Year2000.

5.3.5 Car Ownership According to data available in transportation zones of study area, the car ownership increased from 44 Car/1000 inh in 1990 to about 61 Car/1000 inh in 1997. This indicates a yearly growth rate of 6.1%. Calculating this value for year 2000 (base year) shows that car ownership will be 71.1 Car/1000 inh. Table (5-10) shows the number of cars in study area from 1990 to 1997. Fig (5-12) illustrates the development of car ownership between 1990 and 1997 in study area.

Table (5-10): Number of Cars in Study Area Between Year 1990 and Year1997. [18]

Year No. of Cars

1990 15700 1991 15900

1992 16200

1993 18200

1994 19150

1995 20650

1997 23595

94

0

10

20

30

40

50

60

70

1990 1991 1992 1993 1994 1995 1997

Year

Car

ow

ners

hip

/ 100

0 in

h.

Fig (5-12): Development of Car Ownership in Study Area.

Analysis of the number of private cars according to transportation zones in Tanta city indicates that zone (1) has the maximum number of cars between all transportation zones with 14942cars which represents with a percentage of 52.2% of the total number of cars in study area. Zone (3) lies in the second place with 8122cars representing 28.4% of the total number of cars in study area, while zone (2) has the third place with 5541 cars with a percentage of 19.4% of the total number of cars in Tanta city. Table (5-11) illustrates the number of private cars in the transportation zones of the study area and the percentage of the total number of private cars in Tanta city. Fig (5-13) illustrates the percentage of private cars in transportation zones of the total number of private cars in Tanta city.

95

Table (5-11): Number of Private Cars in The Transportation Zones of The Study Area in Year 2000.

Transportation Zone


Number of Cars 14942 5541 8122 28605

% of Total Number of Cars

in Tanta City 52.2 19.4 28.4 100

19.4%

28.4%

52.2%

Zone1

Zone2

Zone3

Fig (5-13): Percentage of Private Cars According to the Transportation

Zones in Study Area in Year 2000.

96

5.4 Analysis of the Present Situation of Transportation System in the Study Area The analysis of the present transportation system consists of the analysis of the following items:

1- Transport system in the study area. 2- Mode choice analysis (Modal Split).

5.4.1 Transport System in the Study Area Analyzing the transportation system in the study area shows that the transportation system is mainly road based. The study area suffers from the absence of an effective transportation system. Many problems make the operation of the existing transport system in the city a matter of difficulty. Among these problems, the absence of exact location for transport stations, no clear parking system, absence of clear travel fees and a bad status and low efficiency of the vehicles in the transport system. The present transportation demand in the study area is covered by the following transportation systems:

• Public transport (Public bus). • Collection Taxi. • Taxi • Private cars. • Motorcycle.

Public transport is more efficient transportation mean to transport large number of people in urban areas. Public transportation system in study area is provided by the buses of more than one provider. Such as the Internal Transport Organization (ITO) and Co-operation Organization of Transport (COT). Many problems faces the public transportation system such as the absence of clear transport fees, the absence of the station systems for the public transportation. Number of working buses dose not satisfy the transport demand in the study area and that makes the buses service passengers more than the buses occupancy. Besides, the average vehicle age is relatively old and the bad mechanical status of the buses causes more air emissions. The capacity of the transport mode of in public system is 30 passenger.

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Collection taxi or (Microbus) plays a big role in the transportation system in the study area, as it works on the same roads beside public transport more than it penetrates zone (2), zone (3) and the eastern side of zone (1) where there are no public transport buses provided. 30% of the service of the collection taxi is provided by the (COT) and 70% of the service is provided by the private microbus. The absence of parking system and fees system beside the low mechanical efficiency of the vehicles are the major problems faces this transportation sector in the study area. The capacity of the transport unit in this system is 13 passengers. Taxi and private cars have the biggest number of vehicles among all transportation systems in the study area. Car ownership of 71.1 Car/1000 inh has placed tremendous pressure on urban transportation system in the study area. It shares the same ways with bus and microbus making a mixed traffic and leading to low level of service on the road network. problems faces this transportation sector in the study area as the bad status of the asphalt ways, no parking system and sharing their ways with public buses and collection taxies. The occupancy of this type of transport is between 2 and 3 passengers. Beside the previous transportation modes, Private motorcycles participate with a small role (about 5.4%) in the modal split in the transportation system of the study area. No private lanes or parking places are specialized for motorcycles. It also shares its way with other transportation systems on the road network of the study area leading to the decrease of level of service on the road network. Fig (5-14) illustrates the routes of present transportation system in the study area. According to the research of equivalent passenger car units of vehicles on Egyptian roads done by the Academy of Scientific Research and Technology (ASRT), the Occupancy of transportation modes in study area ranges between 1.5 passenger for motorcycle with an equivalent passenger car units equals 0.82, and an occupancy of 30 passenger for public transport (bus) with an equivalent passenger car units equals 2.6. The occupancy and equivalent passenger car units (eq.pcu) of different transportation modes in study area are shown in Table (5-12).

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Table (5-12): Occupancy and Equivalent Passenger Car Unit of Transportation Modes in Study Area [1].

Mode Number of passengers / vehicle. (Occupancy)

Eq.pcu

Private car 2 1

Taxi 3.3 1

Microbus 13 1.34

Bus 30 2.6

Motorcycle 1.5 0.82

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Fig (5-14): The Routes of the Existing Transportation System in Study Area.

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Car13.2%

Taxi21.2%

Microbus27.2%

Bus33.0%

Motorcycle5.4%

5.4.2 Mode Choice of the Study Area Travel demand in Transportation zones of the study area is covered by different travel modes, namely public transport, collection taxi (microbus), private cars, taxi and motorcycle. 33% of the transportation demand in Tanta city is covered by public transport (public bus), while 27.2% of the transportation demand is covered by collection taxi. Private cars represent 13.2% of the transportation demand, while Taxi and motorcycle cover 26.6% of the demand. Fig (5-15) shows distribution of demand by mode in study area.

Fig (5-15): Distribution of Demand by Mode in Study Area. 5.5 Road Network The major transport infrastructure in the study area is the road network. The analysis of road network data from (AUP) shows that the road network in Tanta city consists of 198.4 km paved roads. All roads are two directions. The pavement case of roads on the network ranges between good and poor. Width of the roads ranges between 50 m in roads like Algalaa and Algesh, to 10m in roads like Ahmed Maher and Elsayed Abd Alatef. Some streets on the road network have a changing width like Elmoderia which changes from 15m to 17m width and Saeed street which changes between 14m to 19m width. Table (5-13) illustrates the characteristics of the road network in the study area. Fig (5-16) shows a map of study area road network represents the road network in Tanta city.

Fig (5-16): The Road Network of the Study Area.

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Table (5-13): Characteristics of road network of the study area in year 2000.

Road no. Road name Total width

Directions Pavement Case

1 Algalaa 50 2 good 2 Algesh 50 2 good 3 Alkornesh 25 2 good 4 Elnahas 25 2 good 5 Abd Elmonem Ryad 25 2 good 6 Aleskandrya 20 2 mediocre 7 Shams Eden 20 2 good 8 Elganabya 17 2 poor 9 Hasan Afifi 15-20 2 poor 10 Hafez Wahbi 16-20 2 mediocre 11 Segar 20-25 2 mediocre 12 Sabri 10 2 good 13 Halaket Elqotn 14 2 poor 14 Elseka Egideda 30 2 good 15 Elborsa 18 2 good 16 Elqantara 14 2 good 17 Ahmed Maher 10 2 good 18 Elqadi 8 2 good 19 Elfateh 20 2 mediocre 20 Moheb 16 2 good 21 Tut Ank Amon 14 2 poor 22 Botrus 15 2 good 23 Hassan Radwan 20 2 good 24 Elsayed Abd Elateef 10 2 poor 25 Saeed 14-19 2 good 26 Elmoderia 15-17 2 good 27 Seket Elmahalla 15 2 good 28 Eltareeq Alzeraae 50 2 good

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The existence of high economic activities and the absence of road maintenance works leaded to low level of service of road network. The analysis of the traffic volumes of main roads on the study area in year 2000 illustrates that road number (2) (Algesh) has the maximum directional traffic volume among all the roads on the network with 2694 pc/hr/direction while road number (8) (Elganabya) is ranked second traffic volume with 2310 pc/hr/direction. Road number (4) (Elnahas) has a traffic volume of about 1680 pc/hr/direction. Related to the level of service (LOS), the road network of study area suffers from low LOS. For example road number (2) (Algesh) consists of 3 lanes per direction and the practical capacity per lane is 700 pcu/hr, that means that in 2000 the volume to capacity ratio is greater than 1, LOS (F). Elnahas street consists of three lane per direction and has a volume to capacity ratio is 0.8. The road network in the study area suffers from problems such as low level of service, very low speeds, congestions, higher of accidents rate and delays. This current situation refers to:

• The relative land use characteristics of zone (1) which includes shopping areas and educational places around narrow road sides without any parking system.

• The planning of the existing railway network, which penetrate study area and divide it into three parts, and these parts are connected only thought two arterials namely Algalaa and Segar.

• Road network does not match the functional classification of urban areas. Collectors and local streets like Ahmed Maher Street and Tut Ank Amon Street carry high traffic volumes and work as arterial streets.

• The lack of traffic control systems, specially signalized systems. • Chaotic parking conditions (double parking, parking over sidewalks,

etc.) • The lack of driving discipline (excessive U-turns on major streets,

stopping in the traveling lanes to pick-up or drop passengers, etc.). • The bad drainage systems for road network. • Random behavior of pedestrian movement due to absence of

pedestrian infrastructure. • The capacity reduction on major roads due to the chaotic parking

conditions, undisciplined drivers behavior and inadequate traffic lanes width.

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5.6 Transportation Demand Present Transportation demand can be represented by Origin-Destination matrix. This matrix is a method to describe the number of trips interchange between the zones of the study area. Analysis of demand in study area in year 2000 indicates that the maximum number of trips lies between zone (1) and zone (3), in the second rank lies the trip number between zone (3) and zone (1). The maximum generated number of trips belongs to zone (1), this returns to the following facts:

• High number of population, number of cars and educates in zone (1). • Movement from zone (1) is oriented to zone (2) for industrial

purposes because car maintenance workshops and metal workshops are placed in zone (2).

• Also, movement from zone (1) is oriented to zone (3) for commercial purposes and for railway and bus stations.

The maximum number of trips is attracted to zone (3), this returns to the following facts:

• Zone (3) has the main railway station and bus station in the study area.

• Zone (3) has the main mosque in the study area, namely Alsayed Aalbadawy mosque, which is the biggest religious visiting place in the study area.

• Zone (3) has main trading and commercial zone in the study area.

The minimum number of trips is attracted to zone (2), the reasons for this situation is that zone (2) dose not have any trip attraction places or commercial centers or educational places. Table (5-14) illustrates the (O/D) matrix between main zones of the study area for year 2000 (trip/day).

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Table (5-14): (O/D) Matrix of Transportation Zones Year 2000 (trip/day).

To zone From zone

1 2 3 Total Generated

92884 63161 29723 ـــــــ 1

26963 8089 ـــــــ 18874 2

33582 ـــــــ 1679 31903 3

Total Attracted

50777 31402 71250 153429

Chapter 6

Application of the Proposed Program on Study Area

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6.1 Introduction After analyzing the socio-economic data and transportation system in the study area, and creation of the proposed program, application of the program has been performed. This application has been performed in the study area based on the fourteen sub-zones. 6.2 Main Menu of the Program The main menu of the proposed program contains the following modules:

• Forecasting of socio-economic data. • Forecasting of the future trips (trip generation and attraction). • Distribution of trips. • Modal split. • Trip assignment. • Operational evaluation of the road network. • Air pollution assessment, and • Noise pollution assessment.

6.3 Forecasting of Socio-economic Data The proposed program begins with prediction of the socio-economic data for the target year using the socio-economic data affects trip production and trip attraction in the base year and annual growth rates of these factors. For Tanta city, socio-economic factors that affect trip production are: X1 : Population number in transportation zone i (in 1000). X2 : Number of educants in transportation zone i (in 1000). X3 : Number of employees in transportation zone i (in 1000). X4 : Number of private cars in transportation zone i. X5 : Number of population number having average annuale income <6000 L.E in transportation zone i (in 1000). X6 : Number of population number having average annuale income 6000 ~ 10000 L.E in transportation zone i (in 1000). X7 : Number of population number having average annuale income 10000 ~ 30000 L.E in transportation zone i (in 1000). X8 : Number of population number having average annuale income >30000 L.E in transportation zone i (in 1000).

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While socio-economic factors affect trip attraction are: Y1 : Area of transportation zone j (km2) Y2 : Area of stations in transportation zone j (m2). Y3 : Number of educational places (faculties, pre-primary, primary, middle and secondary) in transportation zone j. Socio-economic data of sub-zones for the base year 2000 is illustrated in appendix (B). Annual growth factors of socio-economic are shown in Table (4-1) of chapter 4. The program asks the planner to input the base year and the target year. The program also asks to load MS.Excel file of the of socio-economic data of the base year affects trip production and trip attraction existing in the data folder, the user can modify the numbers of loaded factors. Also, The program asks the planner to load an MS.Excel file of the annual percentage growth rates of socio-economic data affects trip production and trip attraction (For example: if the growth factor of any data is 1.3%, it is written in the MS.Excel as 1.3). By pushing the bottom, the target factors are calculated and can be stored to the output folder as an MS.Excell file that can be printed. At the end of this stage, the target trips window is closed and return to the main menu to process the next stage. Fig (6-1) shows the application of the first stage on Tanta city as case study using the program.

Fig (6-1): Forecasting Socioeconomic Data of Tanta city in year 2030 using the Program – First Stage.

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The forecasted matrices of socio-economic data for Tanta city as case study are shown in Table (6-1) and Table (6-2).

Table (6-1): Forecasted Socio-economic Data Affect Trip Production of Tanta city in year 2030.

X8 X7 X6 X5 X4 X3 X2 X1 zone 6.125 35.7 11.9 3.325 6342.879 13.78 26.467 44.336 1 9.45 55.65 18.55 5.25 10148.66 21.32 47.481 69.088 2 3.325 19.6 6.475 1.925 3805.784 7.54 14.763 24.344 3 8.225 48.65 16.1 4.55 8879.974 18.72 41.895 60.384 4 1.75 10.15 3.325 0.875 1691.491 3.9 7.98 12.512 5 5.075 29.575 9.8 2.8 5497.275 11.44 24.605 36.72 6 3.15 18.725 6.125 1.75 3382.982 7.15 13.3 23.12 7 1.4 8.575 2.8 0.875 1691.491 3.25 5.719 10.608 8 0.7 3.85 1.225 0.35 845.604 1.43 2.66 4.76 9

6.475 37.975 12.6 3.5 6899.54 16.51 24.605 47.056 10 8.05 47.6 15.75 4.55 8781.49 20.67 30.59 58.888 11 7.35 43.75 14.525 4.2 8044.841 20.67 31.92 54.264 12 12.6 74.025 24.5 7 13561.36 34.97 55.86 91.8 13 1.225 7.525 2.45 0.7 1379.059 3.51 5.453 9.248 14

Table (6-2): Forecasted Socio-economic Data Affect Trip Attraction of

Tanta city in year 2030.

Y3 Y2 Y1 zone 21.78 18000.08 0.695 1 25.41 18000.08 0.834 2 19.36 18000.08 0.834 3 22.99 0 1.529 4 8.47 0 1.112 5 14.52 0 1.529 6 9.68 0 1.112 7 16.94 0 1.946 8 16.94 0 0.834 9 10.89 0 1.112 10 13.31 0 1.39 11 27.83 18000.08 1.39 12 43.56 18000.08 1.807 13 7.26 0 1.112 14

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The analysis of the outputs of this stage shows that in year 2030, transportation zone (13) will represent the highest population number with 91.8 thousand, and the highest educates number with about 56 thousand among all transportation zones in Tanta city. Transportation zone (2) will lay in the second rank with 69.09 thousand population number, and 47.48 thousand educates. While transportation zone (9) will be in the last place with 4.76 thousand population number and with 2.66 thousand educates. Relating to the number of employees and the number of cars, transportation zone (13) lays in the first rank with about 35 thousand employees and about 13560 cars in year 2030. Transportation zone (2) comes the second with 21.24 thousand employees and 10148 cars, while transportation zone (9) comes in the last places with about 1.5 thousand employees and about 845 cars in year 2030. Regarding to the annual income, in year 2030, transportation zone (13) lays in the first rank with about 12600 inhabitants having average annuale income more than 30 thousand L.E. Transportation zone (2) comes in the second place with about 9450 inhabitants, while transportation zone (9) comes in the last places with 700 inhabitants having average annuale income more than 30 thousand L.E. With concern to populatin with average annual income less than 6000 L.E., transportation zone (13) will lay in the first rank with about 7000 inhabitants having average annuale income less than 6000 L.E, while transportation zone (9) comes in the last places with 350 inhabitants having average annuale income less than 6000 L.E. Fig (6-2) to Fig (6-6) illustrates the forecasted socio-economic data affect trip production of the transportation zones of study in year 2030.

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0102030405060708090

100P

op

ula

tio

n (i

n 1

000)

Zone1 Zone3 Zone5 Zone7 Zone9 Zone11 Zone13

Populatio Number Year 2030 (in 1000)

Fig (6-2): Forecasted Population Number in The Transportation Zones of the Study Area in year 2030.

0

10

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30

40

50

60

No.

of

Ed

uca

tes

(in

100

0)


No. of Educates in Year 2030 (in 1000)

Fig (6-3): Forecasted Number of Educates in The Transportation Zones of the Study Area in year 2030.

Zone 14

Zone 14

112

0

5

10

15

20

25

30

35N

o. o

f E

mp

loye

es (

in 1

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No. of Employees in Year 2030 (in 1000)

Fig (6-4): Forecasted Number of Employees in The Transportation Zones of the Study Area in year 2030.

0

2000

4000

6000

8000

10000

12000

14000

No

. of

Car

s


No. of Cars in Year 2030

Fig (6-5): Forecasted Number of Cars in The Transportation Zones of the Study Area in year 2030.

Zone 14

Zone 14

113

0

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30

40

50

60

70

80

Po

pu

latio

n N

o. (

in 1

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Average Annual Income More than 30000 L.E Average Annual Income (10000 : 30000) L.E Average Annual Income More than (6000 : 10000) L.E Average Annual Income Less than 6000 L.E

Fig (6-6): Forecasted Population Number with Different Annual Income Categories for the Transportation Zones of the Study Area in year 2030.

The Outputs of the socio-economic factors affect trip attraction indicate that the area of the transportation zone (8) will reach about 2 km2 by the year 2030 occupying the first place among all transportation zones of the study area, while transportation zone (13) lays in the second place with 1.742 km2. The last place is occupied by transportation zone (1) as its area will be 0.695 km2. The main reason for these results is the future assignment of the land use of the transportation zones. In year 2030, number of educational places in transportation zone (13) will increase to reach 44 educational places. Transportation zone (2) is in the second place with 26 educational places, while the last place is for transportation zone (14) with 7 educational places. Fig (6-7) and Fig (6-8) illustrate the forecasted socio-economic data affect trip attraction of the transportation zones of study in year 2030.

Zone 14

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0

0.5

1

1.5

2A

rea

(km

2)


Area(km2) in Year 2030

Fig (6-7): Forecasted Area (km2) of Transportation Zones of the Study Area in year 2030.

05

1015202530354045

No

. of

Ed

uca

tio

nal

Pla

ces


No. of Educational Places Year 2030

Fig (6-8): Forecasted Number of Educational Places in The Transportation Zones of the Study Area in year 2030.

6.4 Forecasting of Future Trip Produced and Attracted 6.4.1 Model Calibration The proposed trip generation model has been formulated as:

iiii XXXQ 321 1120.19605475.540327.8427073.24 −+−+=

iii XXX 654 1366.86295277.192729678.5 −++

ii XX 87 9205.118600964.5380 −+

Zone 14

Zone 14

115

The proposed trip attraction model has been formulated as:

jjjj YYYZ 321 1692.98608019.0 4687.94580167.12961 +++−= Where: Qi :Trips produced from transportation zone i (Trip / day). Zj :Trips attracted to transportation zone j (Trip / day). X1 : Population number in transportation zone i (in 1000) X2 : Number of educants in transportation zone i(in 1000) X3 : Number of employees in transportation zone i(in 1000) X4 : Number of private cars in transportation zone i X5 : Number of population number having average annuale income <6000 L.E in transportation zone i (in 1000) X6 : Number of population number having average annuale income 6000 ~ 10000 L.E in transportation zone i(in 1000) X7 : Number of population number having average annuale income 10000 ~ 30000 L.E in transportation zone i(in 1000) X8 : Number of population number having average annuale income >30000 L.E in transportation zone i (in 1000). Y1 : Area of transportation zone j (km2) Y2 : Area of stations in transportation zone j (m2). Y3 : Number of educational places (faculties, pre-primary, primary, middle and secondary) in transportation zone j. The output data of these models has been calculated for the year 2000. These data has been compared with the existing data for the same year to calibrate the proposed modules. Analysis of the result of the model indicates that the maximum difference between the model calculation and the existing data is 9.98%, this result can be acceptable (< 10%). Table (6-3) illustrates a comparison between model output and existing trip generation / attraction in the year 2000. Fig (6-9) and Fig (6-10) represents the model calibration for trip generation and attraction respectively.

116

Table (6-3): Comparison Between Trip Generation/ Attraction Calculated by Program and Trip generation Data of Different Transportation Zones in year

2000.

Sub-Zone

(Qi) from year 2000

Data (Trip/day).

(Qi) Calculated from Trip Generation

Model (Trip/day).

(Zj) from Data

(Trip/day).

(Zj) Calculated from Trip Attraction

Model (Trip/day).

1 13933 13913 7617 8376

2 22292 22286 12186 13400

3 8360 8326 4570 5027

4 19506 19550 10663 11629

5 3715 3729 2031 2234

6 12075 12074 6601 7161

7 7431 7347 4062 4457

8 3715 3661 2031 2235

9 1858 1934 1016 1116

10 11864 11904 13817 15197

11 15099 15130 17585 19332

12 11754 11724 24938 27430

13 19813 19804 42038 46231

14 2015 2050 4275 4697

117

Fig (6-9): Calibration of Trip generation Model (year 2000).

Fig (6-10): Calibration of Trip Attraction Model (year 2000).

0

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20000

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1 2 3 4 5 6 7 8 9 10 11 12 13 14

Sub-zone

Trip

Pro

duct

ion

(Trip

/day

) (Qi) year 2000 (From Data) (Qi) year 2000 (Calculated from The Trip Production Model)

0

5000

10000

15000

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35000

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1 2 3 4 5 6 7 8 9 10 11 12 13 14

Sub-zone

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Attr

actio

n (T

rip/d

ay)

(Zj) year 2000 (From Data) (Zj) year 2000 (Calculated from The Trip Attraction Model).

118

6.4.2 Forecasting of Trip Generated and Attracted This stage begins with pressing the icon (Forecasting of trip generated and attracted). Then, loading the matrices of socio-economic data that affects trip generation and attraction. Theses matrices are MS.Excel sheets, which are the output of the first module and saved in an output folder. Then, the program asks to fill in regression equations of trip production and attraction by inputting the regression coefficients in interactive text boxes. The outputs are produced by pressing "Get target year trip production" and "Get target year trip attraction" bottoms. The outputs are the trips production and attraction (Trip/day) of every transportation zone in target year as an MS.Excel sheets that can be saved to the output file and also can be printed. At the end of this stage, the target trips window is closed and return to the main menu to process the next stage. Fig (6-11) shows the application of trip generation stage on Tanta city transportation zones using the program. Table (6-4) shows trips production and attraction of every transportation zones of the study area in the target year 2030.

Table (6-4): Trip Production and Trip Attraction of Tanta City

Transportation Sub-zones in Year 2030.

Zone Trip Production (Qi)

(Trip/day). Trip Attraction (Zj)

(Trip/day).

1 55794 16535 2 91643 21429 3 35503 15463 4 80712 24173 5 14397 5910 6 49150 15820 7 31710 7103 8 17359 22151 9 6991 11633 10 56794 8296 11 76392 13312 12 67376 29075 13 110467 48531 14 12190 4716

Fig (6-11): Forecasting of the Trip Produced/Attracted Using the Proposed Program – Second Stage.

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Outputs of the trip production in year 2030 shows sub-zone (13) represents the highest trip production among all transportation sub-zones of the study area with 110467 (trip/day), this is because this transportation sub-zones has the highest population number, number of educates and number of employees among all other zones. Transportation zone (2) lays in the second rank with a production of 91643 (trip/day), while transportation zone (9) lays in the last rank with 6991 (trip/day). With concern to trip attraction outputs, transportation zone (13) is the most attracting among all transportation zones with 48531 (trip/day), as this sub-zones has one of the biggest passenger stations and has the second biggest area among all transportation sub-zones, besides the commercial activities inside it. In the second rank, lays transportation zone (12) which attracts 29075 (trip/day), while transportation zone (14) is the least attracting with about 4716 (trip/day). Fig (6-12) and Fig (6-13) illustrate future trip generated / attracted of the study area.

0

20000

40000

60000

80000

100000

120000

Tri

p P

rod

uct

ion

(T

rip

/day

)


Trip Production (Trip/day) in Year 2030

Fig (6-12): Trip Production of Transportation Zones in the Study Area (Trip/day) in year 2030.

Zone 14

121

0

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50000T

rip

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/day

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Trip Attraction (Trip/day) in Year 2030

Fig (6-13): Trip Attraction of Transportation Zones of in the Study Area (Trip/day) in year 2030.

6.5 Trip Distribution Stage The third stage of the proposed program is the trip distribution stage. In this stage the program asks the planner to load a MS.Exell file of the impedance matrix between transportation zones of the study area. This MS.Excell file is saved in the input folder. The impedance between transportation zones can be travel distance in km, travel time in seconds or travel cost (L.E/km). The planner can choose the art of the impedance affected the trip distribution. Also, the program asks to input the travel resistance sensitivity factor (γ) in an interactive text box. The forecasted trips productions (Qi) and attractions (Zj) in Excell files, which are saved in the output folder from the previous stage, is loaded in the current stage to be distributed. For Tanta city, the travel distance in km between centroids of the transportation zones is considered to be the travel resistance, while the travel resistance sensitivity factor is assumed to be 2 for Tanta city. By pushing the icon "Get target year origin destination (O/D) matrix", the future origin destination matrix in (Trip /day) will be shown in the software interface and can be saved in the output folder as an MS.Excell file so that it can be printed. At the end of this stage, the assignment window is closed and return to the main menu to process the next stage.

Zone 14

122

Inputs of the trip distribution stage of the program on Tanta city as case study is shown in appendix (C). Table (6-5) shows origin destination (O/D) matrix of Tanta city in the target year 2030 resulting from the program. Fig (6-14) represents the menu of the third stage of the proposed program on Tanta city transportation zones.

Table (6-5): Origin Destination Matrix of Tanta City Transportation Sub-Zones for year 2030 (Trip /day).

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 19236 3774 1478 7928 387 1336 380 9771 6614 678 486 1802 1771 153

2 6639 14293 2401 9922 1959 9354 899 30099 4676 770 690 6261 3257 424

3 3115 2877 2513 9700 1213 1500 1821 2934 960 1933 1021 2431 3212 272

4 15815 11252 9181 10628 1430 2972 1141 11407 6944 1500 979 3748 3382 333

5 663 1909 987 1228 458 2002 806 1234 259 240 276 2534 1549 252

6 1705 6787 908 1902 1491 9211 628 6274 762 378 457 15121 2982 545

7 1342 1805 3053 2022 1661 1738 1388 1879 407 1231 1674 4391 8672 446

8 1692 2963 241 990 125 851 92 7878 1111 100 96 731 441 48

9 2021 812 139 1063 46 182 35 1960 299 49 38 184 148 15

10 4239 2733 5730 4699 874 1849 2176 3617 1003 6222 8594 3977 10562 518

11 2617 2110 2606 2640 866 1926 2548 2971 662 7401 9050 5319 34789 887

12 1562 3084 1000 1628 1281 10265 1077 3656 523 552 857 30514 8527 2851

13 2739 2863 2357 2622 1398 3613 3795 3940 748 2614 10000 15219 54266 4292

14 226 357 191 247 218 631 187 413 71 123 244 4867 4105 312

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Analysis of the application of the 3rd stage of the proposed program indicates that zone (13) (Kobri Elmahata) is 54266 (Trip/day), representing the biggest trip number in the (O/D) matrix of Tanta city in year 2030. The second biggest inter-zonal trip lays by zone (12). The biggest trip number between two transportation zones, which is the trips produced from zone (11) (Elemari) to zone (13) (Kobri Elmahata) which is 34789 (trip/day). The last place is occupied by 15 (trip/day) which represents trips produced from transportation zone (9) (Sabri) to transportation zone (14) (Sedi Mrzoq) this is because these two zones have high impedance between them (3.08 km). 6.6 Modal Split Stage In this module the program produces target year origin destination matrix of every transportation mode in the study area. The planner loads a MS.Excel matrix of the target year origin destination trips which was saved in the output folder, then the user has two options, either to enter the mode choice split ratios of the transportation modes, or to use the utility function in which, the utility factor matrix MS.Excell file must be loaded. And by pressing the icon "Get transportation mode (O/D) matrix", the program calculates modal split matrices and the open window so that that the user can save every mode MS.Excell file (O/D) matrix in (Trip/day). For Tanta city as case study, there are five mode of choice; public bus, collection taxi, taxi, motorcycle and private cars. The splitting ratios of the transportation modes are 33%, 27.2%, 21.2%, 5.4% and 13.2% respectively. Fig (6-15) indicates the menu of the fourth stage of the proposed program. The results of the applicating program in Tanta city for year 2030 are illustrated from Table (6-6) to Table (6-10).

Fig (6-14): Distribution of Trips Using the Proposed Program – Third Stage.

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Fig (6-15): Modal Split Using the Proposed Program – Fourth Stage. 125

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Table (6-6): Forecasted Origin Destination Matrix of Public Bus in year 2030 in Tanta City (Trip/day).

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 6348 1245 488 2616 128 441 125 3224 2183 224 160 595 584 50

2 2191 4717 792 3274 646 3087 297 9933 1543 254 228 2066 1075 140

3 1028 949 829 3201 400 495 601 968 317 638 337 802 1060 90

4 5219 3713 3030 3507 472 981 377 3764 2292 495 323 1237 1116 110

5 219 630 326 405 151 661 266 407 85 79 91 836 511 83

6 563 2240 300 628 492 3040 207 2070 251 125 151 4990 984 180

7 443 596 1007 667 548 574 458 620 134 406 552 1449 2862 147

8 558 978 80 327 41 281 30 2600 367 33 32 241 146 16

9 667 268 46 351 15 60 12 647 99 16 13 61 49 5

10 1399 902 1891 1551 288 610 718 1194 331 2053 2836 1312 3485 171

11 864 696 860 871 286 636 841 980 218 2442 2987 1755 11480 293

12 515 1018 330 537 423 3387 355 1206 173 182 283 10070 2814 941

13 904 945 778 865 461 1192 1252 1300 247 863 3300 5022 17908 1416

14 75 118 63 82 72 208 62 136 23 41 81 1606 1355 103

Table (6-7): Forecasted Origin Destination Matrix of Collection Taxi in year 2030 in Tanta City (Trip/day).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 5232 1027 402 2156 105 363 103 2658 1799 184 132 490 482 42

2 1806 3888 653 2699 533 2544 245 8187 1272 209 188 1703 886 115

3 847 783 684 2638 330 408 495 798 261 526 278 661 874 74

4 4302 3061 2497 2891 389 808 310 3103 1889 408 266 1019 920 91

5 180 519 268 334 125 545 219 336 70 65 75 689 421 69

6 464 1846 247 517 406 2505 171 1707 207 103 124 4113 811 148

7 365 491 830 550 452 473 378 511 111 335 455 1194 2359 121

8 460 806 66 269 34 231 25 2143 302 27 26 199 120 13

9 550 221 38 289 13 50 10 533 81 13 10 50 40 4

10 1153 743 1559 1278 238 503 592 984 273 1692 2338 1082 2873 141

11 712 574 709 718 236 524 693 808 180 2013 2462 1447 9463 241

12 425 839 272 443 348 2792 293 994 142 150 233 8300 2319 775

13 745 779 641 713 380 983 1032 1072 203 711 2720 4140 14760 1167

14 61 97 52 67 59 172 51 112 19 33 66 1324 1117 85

127

Table (6-8): Forecasted Origin Destination Matrix of Taxi in year 2030 in Tanta City (Trip/day).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 4078 800 313 1681 82 283 81 2071 1402 144 103 382 375 32

2 1407 3030 509 2103 415 1983 191 6381 991 163 146 1327 690 90

3 660 610 533 2056 257 318 386 622 204 410 216 515 681 58

4 3353 2385 1946 2253 303 630 242 2418 1472 318 208 795 717 71

5 141 405 209 260 97 424 171 262 55 51 59 537 328 53

6 361 1439 192 403 316 1953 133 1330 162 80 97 3206 632 116

7 285 383 647 429 352 368 294 398 86 261 355 931 1838 95

8 359 628 51 210 27 180 20 1670 236 21 20 155 93 10

9 428 172 29 225 10 39 7 416 63 10 8 39 31 3

10 899 579 1215 996 185 392 461 767 213 1319 1822 843 2239 110

11 555 447 552 560 184 408 540 630 140 1569 1919 1128 7375 188

12 331 654 212 345 272 2176 228 775 111 117 182 6469 1808 604

13 581 607 500 556 296 766 805 835 159 554 2120 3226 11504 910

14 48 76 40 52 46 134 40 88 15 26 52 1032 870 66

Table (6-9): Forecasted Origin Destination Matrix of Motorcycle in year 2030 in Tanta City (Trip/day).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 1039 204 80 428 21 72 21 528 357 37 26 97 96 8

2 359 772 130 536 106 505 49 1625 253 42 37 338 176 23

3 168 155 136 524 66 81 98 158 52 104 55 131 173 15

4 854 608 496 574 77 160 62 616 375 81 53 202 183 18

5 36 103 53 66 25 108 44 67 14 13 15 137 84 14

6 92 366 49 103 81 497 34 339 41 20 25 817 161 29

7 72 97 165 109 90 94 75 101 22 66 90 237 468 24

8 91 160 13 53 7 46 5 425 60 5 5 39 24 3

9 109 44 8 57 2 10 2 106 16 3 2 10 8 1

10 229 148 309 254 47 100 118 195 54 336 464 215 570 28

11 141 114 141 143 47 104 138 160 36 400 489 287 1879 48

12 84 167 54 88 69 554 58 197 28 30 46 1648 460 154

13 148 155 127 142 75 195 205 213 40 141 540 822 2930 232

14 12 19 10 13 12 34 10 22 4 7 13 263 222 17

128

Table (6-10): Forecasted Origin Destination Matrix of Private Cars in year 2030 in Tanta City (Trip/day).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 2539 498 195 1046 51 176 50 1290 873 89 64 238 234 20

2 876 1887 317 1310 259 1235 119 3973 617 102 91 826 430 56

3 411 380 332 1280 160 198 240 387 127 255 135 321 424 36

4 2088 1485 1212 1403 189 392 151 1506 917 198 129 495 446 44

5 88 252 130 162 60 264 106 163 34 32 36 334 204 33

6 225 896 120 251 197 1216 83 828 101 50 60 1996 394 72

7 177 238 403 267 219 229 183 248 54 162 221 580 1145 59

8 223 391 32 131 17 112 12 1040 147 13 13 96 58 6

9 267 107 18 140 6 24 5 259 39 6 5 24 20 2

10 560 361 756 620 115 244 287 477 132 821 1134 525 1394 68

11 345 279 344 348 114 254 336 392 87 977 1195 702 4592 117

12 206 407 132 215 169 1355 142 483 69 73 113 4028 1126 376

13 362 378 311 346 185 477 501 520 99 345 1320 2009 7163 567

14 30 47 25 33 29 83 25 55 9 16 32 642 542 41

6.7 Trip Assignment Stage The 5th stage of the proposed program is trip assignment. This stage requires the calculations of the capacity of the road links of the network. To calculate these capacities, the roadway conditions including the geometrical characteristics and the parking conditions should be determined. The ideal conditions in which the basic capacity of road link is determined are:

• Lane widths of 3.75 m. • Clearance of 1.85m. • Only passenger cars in the traffic stream. • Level terrain with grades no greater than 1 to 2 percent. • No pedestrian movement.

Fig (6-16) shows the menu of the 5th stage of the proposed program.

129

According to Z. Moses Santhakumar et. al. [50], the base capacity of urban road links is 1300-1500 pcu / lane per hour. Adopting the average value of 1400 and a lane width of 3.50 m, the capacity is computed as 400 pcu/hr per one meter width of way. The following formula is adopted for the calculation of capacity of the urban road links:

meterhrpcuWCapacity ./400*= Where: W : Effective width of roadway in meters. The effective width of road is the total width of road with out parking width. The parking width for roadways can be calculated from the following condition:

1- Road with width <5m, the parking width allowed is 0.0m 2- 10 m > Road with width >5m, the parking width allowed is 2.0m. 3- 15 m > Road with width >10m, the parking width allowed is 2.5m. 4- Road with width >15m, the parking width allowed is 3.0m.

According to HCM2000, a directional distribution of 60/40 is used to distribute the two lane capacity for two lane urban roads. In addition, the capacity is effected by the lateral clearance, lane width and terrain level. To calculate the practical capacity, base capacity is reduced by a ratio determined according to the roadway geometric conditions including lane width, obstructions, and edge clearance. Table (6-11) shows reduction factor of roadway capacity due to effect of lateral clearance and lane width. Table (6-12) illustrates the geometrical and operational characteristics of the road links of the network. This table contains the capacity of links calculated from the previous formula.

Fig (6-16): Trip Assignment Using the Proposed Program – 5th Stage.

130

131

Table (6-11): Reduction Factor of Roadway Capacity Due To Effect Of Lateral Clearance and Lane Width.

Two Lane Highway

Obstruction From One Side Obstruction From One Side

Lane Width (m) Lane Width (m)

Edge Clearance

3.75 3.50 3.00 2.75 3.75 3.50 3.00 2.75 1.85 100 86 77 70 100 86 77 70 1.50 96 83 74 68 92 79 71 65 0.50 91 78 70 64 81 70 63 57 0.00 85 73 66 60 70 60 54 49

Multi Lane Highway

Obstruction From One Side Obstruction From One Side

Lane Width (m) Lane Width (m)

Edge Clearance

3.75 3.50 3.00 2.75 3.75 3.50 3.00 2.75 1.85 100 97 91 81 100 97 91 81 1.50 99 96 90 80 98 95 89 79 0.50 97 94 88 79 94 91 86 76 0.00 90 87 82 73 81 79 74 66

132

Table (6-12): Geometrical and Operational Characteristics of the Coded Road Links of the Network.

Link

Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

1 9 9 4 472 2 3.5 0 One 1314

9 1 4 9 472 2 3.5 0 One 1314

2 5 4 9 445 2 3.5 0 One 1314

5 2 9 4 445 2 3.5 0 One 1314

3 5 9 9 599 2 3.5 0 One 1314

5 3 9 9 599 2 3.5 0 One 1314

6 7 9 9 466 1 3.5 0 One 613

7 6 9 9 466 1 3.5 0 One 613

7 8 9 2 198 1 3.5 0 One 613

8 7 2 9 198 1 3.5 0 One 613

8 4 9 9 388 4 3.5 0 Both 3476

4 8 9 9 388 4 3.5 0 Both 3476

8 29 9 8 82 4 3.5 0 Both 3476

29 8 8 9 82 4 3.5 0 Both 3476

4 3 2 9 369 2 3.5 0 One 1314

3 4 9 2 369 2 3.5 0 One 1314

2 9 9 4 221 2 3.5 0 One 1314

9 2 4 9 221 2 3.5 0 One 1314

3 10 9 4 255 2 3.5 0 One 1314

10 3 4 9 255 2 3.5 0 One 1314

9 15 4 3 348 2 3.5 0 One 1314

15 9 3 4 348 2 3.5 0 One 1314

10 14 4 3 322 2 3.5 0 One 1314

14 10 3 4 322 2 3.5 0 One 1314

4 11 9 4 320 4 3.5 0 Both 3476

11 4 4 9 320 4 3.5 0 Both 3476

11 12 4 4 116 4 3.5 0 Both 3476

12 11 4 4 116 4 3.5 0 Both 3476

12 13 4 4 154 4 3.5 0 Both 3476

133

Table(6-12): continued Link

Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

13 12 4 4 154 4 3.5 0 Both 3476

10 11 4 2 311 1 3 0 One 475

11 10 2 4 311 1 3 0 One 475

14 13 4 2 309 2 3.5 0 One 1314

13 14 2 4 309 2 3.5 0 One 1314

10 9 4 4 198 2 3.5 0 One 1314

9 10 4 4 198 2 3.5 0 One 1314

14 15 4 4 188 2 3.5 0 One 1314

15 14 4 4 188 2 3.5 0 One 1314

15 16 4 1 815 2 3.5 0 One 1314

16 15 1 4 815 2 3.5 0 One 1314

86 87 3 3 176 1 3 0 One 475

87 86 3 3 176 1 3 0 One 475

87 88 3 3 237 1 3 0 One 475

88 87 3 3 237 1 3 0 One 475

88 89 3 3 219 1 3 0 One 475

89 88 3 3 219 1 3 0 One 475

89 86 3 3 231 2 3 0 One 924

86 89 3 3 231 2 3 0 One 924

14 86 4 3 248 2 3 0 One 924

86 14 3 4 248 2 3 0 One 924

15 87 4 3 228 1 3 0 One 475

87 15 3 4 228 1 3 0 One 475

88 25 3 7 211 1 3 0 One 475

89 23 3 7 190 1 3 0 One 475

13 18 4 3 170 4 3.5 0 Both 3476

20 22 3 5 176 4 3.5 0 Both 3476

20 86 5 3 226 1 3 0 One 475

22 89 5 3 110 1 3 0 One 475

134


Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

51 23 5 3 84 4 3.5 0 Both 3476

18 20 3 5 125 4 3.5 0 Both 3476

22 51 3 7 206 4 3.5 0 Both 3476

22 51 3 5 206 4 3.5 0 Both 3476

22 51 5 7 206 4 3.5 0 Both 3476

11 12 2 4 116 4 3.5 0 Both 3476

11 12 2 5 116 4 3.5 0 Both 3476

12 13 5 4 154 4 3.5 0 Both 3476

18 17 3 10 1159 2 3.5 0 One 1314

18 17 5 10 1159 2 3.5 0 One 1314

87 19 3 10 680 1 3 0 One 475

88 21 3 10 491 1 3 0 One 475

17 19 3 3 112 2 3 0 One 924

19 21 3 3 401 2 3 0 One 924

17 19 10 10 112 2 3 0 One 924

19 21 3 10 401 2 3 0 One 924

21 24 3 7 476 2 3 0 One 924

21 24 10 7 476 2 3 0 One 924

23 25 3 7 161 4 3.5 0 Both 3476

25 24 7 10 348 4 3.5 0 Both 3476

25 24 3 10 348 4 3.5 0 Both 3476

17 16 3 1 53 2 3 0 One 924

17 16 10 1 53 2 3 0 One 924

51 52 7 7 318 1 3 0 One 475

52 53 7 7 364 1 3 0 One 475

52 55 7 11 452 1 3 0 One 475

55 53 7 7 584 1 3 0 One 475

53 54 7 7 370 2 3.5 0 One 1314

24 55 10 7 125 1 3 0 One 475

135


Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

54 47 7 7 463 2 3 0 One 924

54 47 7 5 297 2 3 0 One 924

47 46 7 5 87 1 3 0 One 475

46 45 7 7 132 1 3 0 One 475

45 51 5 3 222 1 3 0 One 475

55 72 7 11 460 2 3.5 0 One 1314

72 68 11 13 270 2 3.5 0 One 1314

68 73 13 11 787 2 3.5 0 One 1314

73 26 11 11 1049 2 3.5 0 One 1314

72 27 11 11 649 2 3.5 0 One 1314

24 27 7 11 356 3 3.5 0 Both 2370

24 27 10 11 356 3 3.5 0 Both 2370

27 26 11 10 431 3 3.5 0 Both 2370

26 28 11 10 600 3 3.5 0 Both 2370

24 25 10 10 1025 1 3 0 One 475

28 25 10 10 579 3 3.5 1.2 Both 2760

25 16 10 1 1319 3 3.5 1.2 Both 2760

1 83 9 1 669 1 3 0 One 475

5 85 9 1 806 1 3 0 One 475

16 83 1 1 1103 3 3.5 1.2 Both 2760

83 85 1 1 416 3 3.5 1.2 Both 2760

85 84 1 8 1097 3 3.5 1.2 Both 2760

84 29 8 8 498 3 3.5 1.2 Both 2760

77 84 8 1 242 2 3.5 0 One 1314

77 78 8 8 119 2 3 0 One 924

78 79 8 8 260 2 3 0 One 924

79 80 8 8 169 3 3.5 0 Both 2370

80 81 8 8 301 2 3 0 One 924

79 76 8 8 241 3 3.5 0 Both 2370

136


Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

76 29 8 9 378 3 3.5 0 Both 2370

75 80 8 8 672 2 3.5 0 One 1314

75 30 8 2 589 2 3.5 0 One 1314

29 30 8 2 238 3 3.5 1.2 Both 2760

30 31 2 2 373 3 3.5 0 Both 2370

31 32 2 6 675 3 3.5 1.2 Both 2760

6 7 9 9 532 1 3 0 One 475

31 34 2 2 445 2 3 0 One 924

4 34 2 2 628 2 3 0 One 924

11 34 4 2 533 2 3.5 0 One 1314

33 34 2 2 135 2 3.5 0 One 1314

33 32 2 6 447 2 3.5 0 One 1314

34 90 2 2 280 2 3 0 One 924

90 40 2 5 135 2 3 0 One 924

90 35 2 5 196 2 3.5 0 One 1314

33 36 2 6 235 2 3.5 0 One 1314

90 36 2 6 285 2 3.5 0 One 1314

36 37 6 6 633 2 3.5 0 One 1314

37 38 6 6 252 2 3 0 One 924

38 56 6 12 252 2 3 0 One 924

38 39 6 5 970 2 3.5 0 One 1314

12 35 4 2 299 3 3.5 0 Both 2370

35 40 2 5 175 3 3.5 0 Both 2370

40 39 5 6 315 3 3.5 0 Both 2370

39 41 5 6 206 3 3.5 0 Both 2370

41 42 5 6 109 3 3.5 0 Both 2370

42 56 6 6 960 2 3.5 0 One 1314

32 82 6 6 1602 3 3.5 1.2 Both 2760

82 57 6 12 824 2 3.5 1.2 One 1458

137


Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

56 57 6 6 165 2 3.5 0 One 1314

41 43 5 5 451 1 3 0 One 475

43 40 6 5 442 1 3 0 One 475

41 44 5 5 409 1 3 0 One 475

44 50 5 5 246 1 3 0 One 475

42 49 5 5 528 1 3 0 One 475

49 48 5 5 80 1 3 0 One 475

42 50 5 5 472 2 3.5 0 One 1314

50 54 5 5 369 2 3.5 0 One 1314

43 44 5 5 442 1 3 0 One 475

44 46 5 7 261 1 3 0 One 475

57 61 6 14 1337 2 3 0 One 924

57 64 12 12 1411 3 3.5 0 Both 2370

64 59 12 12 809 2 3 0 One 924

64 91 12 13 1438 3 3.5 0 Both 2370

60 69 14 13 985 2 3.5 0 One 1314

58 63 12 14 354 2 3 0 One 924

59 63 12 14 100 2 3 0 One 924

61 60 12 14 477 2 3 0 One 924

60 62 14 14 318 2 3 0 One 924

62 63 14 14 174 2 3 0 One 924

60 65 14 13 308 2 3 0 One 924

65 68 13 11 505 2 3 0 One 924

69 91 13 13 455 2 3 0 One 924

69 66 13 13 636 2 3 0 One 924

66 65 13 13 282 2 3 0 One 924

66 67 13 13 193 1 3 0 One 475

67 68 13 13 356 1 3 0 One 475

91 70 13 13 594 2 3.5 0 One 1314

138


Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

70 74 13 13 1093 2 3.5 0 One 1314

74 73 13 11 700 2 3.5 0 One 1314

70 27 13 13 1153 2 3 0 One 924

32 37 2 6 316 2 3 0 One 924

25 88 7 3 211 1 3 0 One 475

23 89 7 3 190 1 3 0 One 475

18 13 3 4 170 4 3.5 0 Both 3476

22 20 5 3 176 4 3.5 0 Both 3476

86 20 3 5 226 1 3 0 One 475

89 22 3 5 110 1 3 0 One 475

23 51 3 5 84 4 3.5 0 Both 3476

20 18 5 3 125 4 3.5 0 Both 3476

51 22 7 3 206 4 3.5 0 Both 3476

51 22 5 3 206 4 3.5 0 Both 3476

51 22 7 5 206 4 3.5 0 Both 3476

12 11 4 2 116 4 3.5 0 Both 3476

12 11 5 2 116 4 3.5 0 Both 3476

13 12 4 5 154 4 3.5 0 Both 3476

17 18 10 3 1159 2 3.5 0 One 1314

17 18 10 5 1159 2 3.5 0 One 1314

19 87 10 3 680 1 3 0 One 475

21 88 10 3 491 1 3 0 One 475

19 17 3 3 112 2 3 0 One 924

21 19 3 3 401 2 3 0 One 924

19 17 10 10 112 2 3 0 One 924

21 19 10 3 401 2 3 0 One 924

24 21 7 3 476 2 3 0 One 924

24 21 7 10 476 2 3 0 One 924

25 23 7 3 161 4 3.5 0 Both 3476

139


Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

24 25 10 7 348 4 3.5 0 Both 3476

24 25 10 3 348 4 3.5 0 Both 3476

16 17 1 3 53 2 3 0 One 924

16 17 1 10 53 2 3 0 One 924

52 51 7 7 318 1 3 0 One 475

53 52 7 7 364 1 3 0 One 475

55 52 11 7 452 1 3 0 One 475

53 55 7 7 584 1 3 0 One 475

54 53 7 7 370 2 3.5 0 One 1314

55 24 7 10 125 1 3 0 One 475

47 54 7 7 463 2 3 0 One 924

47 54 5 7 297 2 3 0 One 924

46 47 5 7 87 1 3 0 One 475

45 46 7 7 132 1 3 0 One 475

51 45 3 5 222 1 3 0 One 475

72 55 11 7 460 2 3.5 0 One 1314

68 72 13 11 270 2 3.5 0 One 1314

73 68 11 13 787 2 3.5 0 One 1314

26 73 11 11 1049 2 3.5 0 One 1314

27 72 11 11 649 2 3.5 0 One 1314

27 24 11 7 356 3 3.5 0 Both 2370

27 24 11 10 356 3 3.5 0 Both 2370

26 27 10 11 431 3 3.5 0 Both 2370

28 26 10 11 600 3 3.5 0 Both 2370

25 24 10 10 1025 1 3 0 One 475

25 28 10 10 579 3 3.5 1.2 Both 2760

16 25 1 10 1319 3 3.5 1.2 Both 2760

83 1 1 9 669 1 3 0 One 475

85 5 1 9 806 1 3 0 One 475

140


Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

83 16 1 1 1103 3 3.5 1.2 Both 2760

85 83 1 1 416 3 3.5 1.2 Both 2760

84 85 8 1 1097 3 3.5 1.2 Both 2760

29 84 8 8 498 3 3.5 1.2 Both 2760

84 77 1 8 242 2 3.5 0 One 1314

78 77 8 8 119 2 3 0 One 924

79 78 8 8 260 2 3 0 One 924

80 79 8 8 169 3 3.5 0 Both 2370

81 80 8 8 301 2 3 0 One 924

76 79 8 8 241 3 3.5 0 Both 2370

29 76 9 8 378 3 3.5 0 Both 2370

80 75 8 8 672 2 3.5 0 One 1314

30 75 2 8 589 2 3.5 0 One 1314

30 29 2 8 238 3 3.5 1.2 Both 2760

31 30 2 2 373 3 3.5 0 Both 2370

32 31 6 2 675 3 3.5 1.2 Both 2760

7 6 9 9 532 1 3 0 One 475

34 31 2 2 445 2 3 0 One 924

34 4 2 2 628 2 3 0 One 924

34 11 2 4 533 2 3.5 0 One 1314

34 33 2 2 135 2 3.5 0 One 1314

32 33 6 2 447 2 3.5 0 One 1314

90 34 2 2 280 2 3 0 One 924

40 90 5 2 135 2 3 0 One 924

35 90 5 2 196 2 3.5 0 One 1314

36 33 6 2 235 2 3.5 0 One 1314

36 90 6 2 285 2 3.5 0 One 1314

37 36 6 6 633 2 3.5 0 One 1314

38 37 6 6 252 2 3 0 One 924

141


Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

56 38 12 6 252 2 3 0 One 924

39 38 5 6 970 2 3.5 0 One 1314

35 12 2 4 299 3 3.5 0 Both 2370

40 35 5 2 175 3 3.5 0 Both 2370

39 40 6 5 315 3 3.5 0 Both 2370

41 39 6 5 206 3 3.5 0 Both 2370

42 41 6 5 109 3 3.5 0 Both 2370

56 42 6 6 960 2 3.5 0 One 1314

82 32 6 6 1602 3 3.5 1.2 Both 2760

57 82 12 6 824 2 3.5 1.2 One 1458

57 56 6 6 165 2 3.5 0 One 1314

43 41 5 5 451 1 3 0 One 475

40 43 5 6 442 1 3 0 One 475

44 41 5 5 409 1 3 0 One 475

50 44 5 5 246 1 3 0 One 475

49 42 5 5 528 1 3 0 One 475

48 49 5 5 80 1 3 0 One 475

50 42 5 5 472 2 3.5 0 One 1314

54 50 5 5 369 2 3.5 0 One 1314

44 43 5 5 442 1 3 0 One 475

46 44 7 5 261 1 3 0 One 475

61 57 14 6 1337 2 3 0 One 924

64 57 12 12 1411 3 3.5 0 Both 2370

59 64 12 12 809 2 3 0 One 924

91 64 13 12 1438 3 3.5 0 Both 2370

69 60 13 14 985 2 3.5 0 One 1314

63 58 14 12 354 2 3 0 One 924

63 59 14 12 100 2 3 0 One 924

60 61 14 12 477 2 3 0 One 924

142


Start Node

End Node

Origin Zone

Dest- ination Zone

Length ( m)

No of Lanes

Lane Width

(m)

Edge Clearance

(m)

Obst-ruction Sides

Capacity (eq.pcu

/hr/direction)

62 60 14 14 318 2 3 0 One 924

63 62 14 14 174 2 3 0 One 924

65 60 13 14 308 2 3 0 One 924

68 65 11 13 505 2 3 0 One 924

91 69 13 13 455 2 3 0 One 924

66 69 13 13 636 2 3 0 One 924

65 66 13 13 282 2 3 0 One 924

67 66 13 13 193 1 3 0 One 475

68 67 13 13 356 1 3 0 One 475

70 91 13 13 594 2 3.5 0 One 1314

74 70 13 13 1093 2 3.5 0 One 1314

73 74 11 13 700 2 3.5 0 One 1314

27 70 13 13 1153 2 3 0 One 924

37 32 6 2 316 2 3 0 One 924

45 22 5 3 200 1 3.5 0 One 613

22 45 3 5 200 1 3.5 0 One 613

78 81 9 9 604 2 3 0 One 924

81 78 9 9 604 2 3 0 One 924

48 47 5 7 80 1 3 0 One 475

47 48 7 5 80 1 3 0 One 475

61 58 5 12 177 1 3 0 One 475

58 61 12 5 177 1 3 0 One 475

61 58 14 12 177 1 3 0 One 475

58 61 12 14 177 1 3 0 One 475

The output of the assignment stage should contain the traffic volume in each link and the final percentage delay of travel time on these links. The input of this stage should be contain the coded road network links and the geometrical and operational (contains capacities and initial travel time) of these links. The second input of this stage is the peak hourly traffic volume (O/D) matrix. To get this matrix, the design hour factor (K-factor) for

143

urbanized areas has been chosen to be 0.1 to 0.15. K- factor represents the proportion of the total daily traffic that occurs during the thirteenth highest peak hour of the year, some researches assure the fifteenth highest peak hour of the year for urban areas. Multiplying the (O/D) matrix resulted from the previous stage by the K-factor will result the required peak hourly traffic volume (O/D) matrix. A directional distribution factor of 1.34 for peak hourly traffic volume has been also taken into account for getting the peak hourly (O/D) matrix. Table (6-13) represents the input data for trip assignment stage. Table (6-14) illustrates the peak hourly (O/D) matrix for the year 2000. In the assignment stage, the program assigns trips interchange between different transportation zones on the road network. This requires a road network description. The user describes the road network characteristics by using a numerical code for each node, each link is described by its start and end nodes. Also, initial travel time on each link is calculated from ideal conditions representing the free flow speed of 50 Km/hr on urban streets as in HCM2000. The initial travel time is calculated in order to calculate delays on each link after assignment; the percent delay on the road network link is the difference between travel time on the link after and before the assignment divided by the initial travel time. For Tanta city, the initial travel time is calculated in seconds. Moreover, the practical capacity of each link on the network is essential for calculating the trip time on each link after assignment. The practical capacity is calculated in (pcu/hr) according to [50]. Fig (6-17) illustrates the study area road network coding system. The program asks the planner to load (from the input folder) an MS.Excel sheet include a description of every road network link. Description includes every link start and end node, start and end zone, travel time in (sec) and the link capacity in (pcu/hr/direction) as shown in Table (6-13). Besides, the program asks the user to load the peak hour (O/D) matrix, which was saved in the output folder from the distribution stage after multiplying it by K-factor and 1.34 for peak hour two direction traffic movements. Numbers of the loaded can be modified interactively in the program window. By pressing the icon "Get target year trip assignment table", the program calculates traffic volumes in (pcu/hr), volume to capacity ratio and percent time delay on each link. These outputs can be saved in MS.Excell sheet in the output folder by pressing "Save" icon so that it can be printed. Table (6-15) represents the results of the trip assignment for the study area for year 2030.

144

Table (6-13): The Input Data for the Trip Assignment Stage (Year 2000).

Link

Start Node

End Node

Origin Zone

Destination Zone

Initial Travel Time (sec)

Capacity (pcu/hr/direction)

1 9 9 4 34.01 1314

9 1 4 9 34.01 1314

2 5 4 9 32.04 1314

5 2 9 4 32.04 1314

3 5 9 9 43.11 1314

5 3 9 9 43.11 1314

6 7 9 9 33.56 613

7 6 9 9 33.56 613

7 8 9 2 14.27 613

8 7 2 9 14.27 613

8 4 9 9 27.95 3476

4 8 9 9 27.95 3476

8 29 9 8 5.90 3476

29 8 8 9 5.90 3476

4 3 2 9 26.60 1314

3 4 9 2 26.60 1314

2 9 9 4 15.88 1314

9 2 4 9 15.88 1314

3 10 9 4 18.37 1314

10 3 4 9 18.37 1314

9 15 4 3 25.04 1314

15 9 3 4 25.04 1314

10 14 4 3 23.21 1314

14 10 3 4 23.21 1314

4 11 9 4 23.01 3476

11 4 4 9 23.01 3476

11 12 4 4 8.32 3476

12 11 4 4 8.32 3476

12 13 4 4 11.10 3476

145


Start Node

End Node

Origin Zone

Destination Zone



13 12 4 4 11.10 3476

10 11 4 2 22.37 475

11 10 2 4 22.37 475

14 13 4 2 22.27 1314

13 14 2 4 22.27 1314

10 9 4 4 14.23 1314

9 10 4 4 14.23 1314

14 15 4 4 13.56 1314

15 14 4 4 13.56 1314

15 16 4 1 58.66 1314

16 15 1 4 58.66 1314

86 87 3 3 12.65 475

87 86 3 3 12.65 475

87 88 3 3 17.07 475

88 87 3 3 17.07 475

88 89 3 3 15.75 475

89 88 3 3 15.75 475

89 86 3 3 16.66 924

86 89 3 3 16.66 924

14 86 4 3 17.89 924

86 14 3 4 17.89 924

15 87 4 3 16.41 475

87 15 3 4 16.41 475

88 25 3 7 15.20 475

89 23 3 7 13.65 475

13 18 4 3 12.21 3476

20 22 3 5 12.68 3476

20 86 5 3 16.28 475

22 89 5 3 7.91 475

146


Start Node

End Node

Origin Zone

Destination Zone



51 23 5 3 6.06 3476

18 20 3 5 8.99 3476

22 51 3 7 14.80 3476

22 51 3 5 14.80 3476

22 51 5 7 14.80 3476

11 12 2 4 8.32 3476

11 12 2 5 8.32 3476

12 13 5 4 11.10 3476

18 17 3 10 83.47 1314

18 17 5 10 83.47 1314

87 19 3 10 48.99 475

88 21 3 10 35.35 475

17 19 3 3 8.09 924

19 21 3 3 28.86 924

17 19 10 10 8.09 924

19 21 3 10 28.86 924

21 24 3 7 34.31 924

21 24 10 7 34.31 924

23 25 3 7 11.59 3476

25 24 7 10 25.04 3476

25 24 3 10 25.04 3476

17 16 3 1 3.85 924

17 16 10 1 3.85 924

51 52 7 7 22.90 475

52 53 7 7 26.18 475

52 55 7 11 32.56 475

55 53 7 7 42.04 475

53 54 7 7 26.67 1314

24 55 10 7 8.99 475

147


Start Node

End Node

Origin Zone

Destination Zone



54 47 7 7 33.30 924

54 47 7 5 21.36 924

47 46 7 5 6.29 475

46 45 7 7 9.49 475

45 51 5 3 15.97 475

55 72 7 11 33.14 1314

72 68 11 13 19.46 1314

68 73 13 11 56.68 1314

73 26 11 11 75.56 1314

72 27 11 11 46.76 1314

24 27 7 11 25.60 2370

24 27 10 11 25.60 2370

27 26 11 10 31.04 2370

26 28 11 10 43.21 2370

24 25 10 10 73.81 475

28 25 10 10 41.69 2760

25 16 10 1 94.94 2760

1 83 9 1 48.20 475

5 85 9 1 58.05 475

16 83 1 1 79.39 2760

83 85 1 1 29.93 2760

85 84 1 8 78.97 2760

84 29 8 8 35.86 2760

77 84 8 1 17.40 1314

77 78 8 8 8.56 924

78 79 8 8 18.70 924

79 80 8 8 12.18 2370

80 81 8 8 21.64 924

79 76 8 8 17.34 2370

148


Start Node

End Node

Origin Zone

Destination Zone



76 29 8 9 27.22 2370

75 80 8 8 48.35 1314

75 30 8 2 42.40 1314

29 30 8 2 17.15 2760

30 31 2 2 26.87 2370

31 32 2 6 48.59 2760

6 7 9 9 38.27 475

31 34 2 2 32.04 924

4 34 2 2 45.23 924

11 34 4 2 38.39 1314

33 34 2 2 9.73 1314

33 32 2 6 32.15 1314

34 90 2 2 20.17 924

90 40 2 5 9.73 924

90 35 2 5 14.09 1314

33 36 2 6 16.92 1314

90 36 2 6 20.54 1314

36 37 6 6 45.56 1314

37 38 6 6 18.13 924

38 56 6 12 18.13 924

38 39 6 5 69.82 1314

12 35 4 2 21.49 2370

35 40 2 5 12.59 2370

40 39 5 6 22.66 2370

39 41 5 6 14.83 2370

41 42 5 6 7.84 2370

42 56 6 6 69.09 1314

32 82 6 6 115.34 2760

82 57 6 12 59.33 1458

149


Start Node

End Node

Origin Zone

Destination Zone



56 57 6 6 11.87 1314

41 43 5 5 32.47 475

43 40 6 5 31.84 475

41 44 5 5 29.46 475

44 50 5 5 17.69 475

42 49 5 5 38.02 475

49 48 5 5 5.75 475

42 50 5 5 33.98 1314

50 54 5 5 26.57 1314

43 44 5 5 31.84 475

44 46 5 7 18.81 475

57 61 6 14 96.27 924

57 64 12 12 101.57 2370

64 59 12 12 58.22 924

64 91 12 13 103.53 2370

60 69 14 13 70.95 1314

58 63 12 14 25.50 924

59 63 12 14 7.18 924

61 60 12 14 34.32 924

60 62 14 14 22.89 924

62 63 14 14 12.52 924

60 65 14 13 22.15 924

65 68 13 11 36.39 924

69 91 13 13 32.79 924

69 66 13 13 45.77 924

66 65 13 13 20.33 924

66 67 13 13 13.87 475

67 68 13 13 25.62 475

91 70 13 13 42.75 1314

150


Start Node

End Node

Origin Zone

Destination Zone



70 74 13 13 78.69 1314

74 73 13 11 50.40 1314

70 27 13 13 83.03 924

32 37 2 6 22.74 924

25 88 7 3 15.20 475

23 89 7 3 13.65 475

18 13 3 4 12.21 3476

22 20 5 3 12.68 3476

86 20 3 5 16.28 475

89 22 3 5 7.91 475

23 51 3 5 6.06 3476

20 18 5 3 8.99 3476

51 22 7 3 14.80 3476

51 22 5 3 14.80 3476

51 22 7 5 14.80 3476

12 11 4 2 8.32 3476

12 11 5 2 8.32 3476

13 12 4 5 11.10 3476

17 18 10 3 83.47 1314

17 18 10 5 83.47 1314

19 87 10 3 48.99 475

21 88 10 3 35.35 475

19 17 3 3 8.09 924

21 19 3 3 28.86 924

19 17 10 10 8.09 924

21 19 10 3 28.86 924

24 21 7 3 34.31 924

24 21 7 10 34.31 924

25 23 7 3 11.59 3476

151


Start Node

End Node

Origin Zone

Destination Zone



24 25 10 7 25.04 3476

24 25 10 3 25.04 3476

16 17 1 3 3.85 924

16 17 1 10 3.85 924

52 51 7 7 22.90 475

53 52 7 7 26.18 475

55 52 11 7 32.56 475

53 55 7 7 42.04 475

54 53 7 7 26.67 1314

55 24 7 10 8.99 475

47 54 7 7 33.30 924

47 54 5 7 21.36 924

46 47 5 7 6.29 475

45 46 7 7 9.49 475

51 45 3 5 15.97 475

72 55 11 7 33.14 1314

68 72 13 11 19.46 1314

73 68 11 13 56.68 1314

26 73 11 11 75.56 1314

27 72 11 11 46.76 1314

27 24 11 7 25.60 2370

27 24 11 10 25.60 2370

26 27 10 11 31.04 2370

28 26 10 11 43.21 2370

25 24 10 10 73.81 475

25 28 10 10 41.69 2760

16 25 1 10 94.94 2760

83 1 1 9 48.20 475

85 5 1 9 58.05 475

152


Start Node

End Node

Origin Zone

Destination Zone



83 16 1 1 79.39 2760

85 83 1 1 29.93 2760

84 85 8 1 78.97 2760

29 84 8 8 35.86 2760

84 77 1 8 17.40 1314

78 77 8 8 8.56 924

79 78 8 8 18.70 924

80 79 8 8 12.18 2370

81 80 8 8 21.64 924

76 79 8 8 17.34 2370

29 76 9 8 27.22 2370

80 75 8 8 48.35 1314

30 75 2 8 42.40 1314

30 29 2 8 17.15 2760

31 30 2 2 26.87 2370

32 31 6 2 48.59 2760

7 6 9 9 38.27 475

34 31 2 2 32.04 924

34 4 2 2 45.23 924

34 11 2 4 38.39 1314

34 33 2 2 9.73 1314

32 33 6 2 32.15 1314

90 34 2 2 20.17 924

40 90 5 2 9.73 924

35 90 5 2 14.09 1314

36 33 6 2 16.92 1314

36 90 6 2 20.54 1314

37 36 6 6 45.56 1314

38 37 6 6 18.13 924

153


Start Node

End Node

Origin Zone

Destination Zone



56 38 12 6 18.13 924

39 38 5 6 69.82 1314

35 12 2 4 21.49 2370

40 35 5 2 12.59 2370

39 40 6 5 22.66 2370

41 39 6 5 14.83 2370

42 41 6 5 7.84 2370

56 42 6 6 69.09 1314

82 32 6 6 115.34 2760

57 82 12 6 59.33 1458

57 56 6 6 11.87 1314

43 41 5 5 32.47 475

40 43 5 6 31.84 475

44 41 5 5 29.46 475

50 44 5 5 17.69 475

49 42 5 5 38.02 475

48 49 5 5 5.75 475

50 42 5 5 33.98 1314

54 50 5 5 26.57 1314

44 43 5 5 31.84 475

46 44 7 5 18.81 475

61 57 14 6 96.27 924

64 57 12 12 101.57 2370

59 64 12 12 58.22 924

91 64 13 12 103.53 2370

69 60 13 14 70.95 1314

63 58 14 12 25.50 924

63 59 14 12 7.18 924

60 61 14 12 34.32 924

154


Start Node

End Node

Origin Zone

Destination Zone



62 60 14 14 22.89 924

63 62 14 14 12.52 924

65 60 13 14 22.15 924

68 65 11 13 36.39 924

91 69 13 13 32.79 924

66 69 13 13 45.77 924

65 66 13 13 20.33 924

67 66 13 13 13.87 475

68 67 13 13 25.62 475

70 91 13 13 42.75 1314

74 70 13 13 78.69 1314

73 74 11 13 50.40 1314

27 70 13 13 83.03 924

37 32 6 2 22.74 924

45 22 5 3 14.4 613

22 45 3 5 14.4 613

78 81 9 9 43.5 924

81 78 9 9 43.5 924

48 47 5 7 5.8 475

47 48 7 5 5.8 475

61 58 5 12 12.7 475

58 61 12 5 12.7 475

61 58 14 12 12.7 475

58 61 12 14 12.7 475

Fig (6-17): Study Area Road Network Coding System.

155

156

Table (6-14): Peak Hourly (O/D) Matrix for the Year 2000 (pcu/hr). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 1611 316 124 664 32 112 32 818 554 57 41 151 148 13

2 556 1197 201 831 164 783 75 2521 392 64 58 524 273 36

3 261 241 210 812 102 126 153 246 80 162 86 204 269 23

4 1325 942 769 890 120 249 96 955 582 126 82 314 283 28

5 56 160 83 103 38 168 68 103 22 20 23 212 130 21

6 143 568 76 159 125 771 53 525 64 32 38 1266 250 46

7 112 151 256 169 139 146 116 157 34 103 140 368 726 37

8 142 248 20 83 10 71 8 660 93 8 8 61 37 4

9 169 68 12 89 4 15 3 164 25 4 3 15 12 1

10 355 229 480 394 73 155 182 303 84 521 720 333 885 43

11 219 177 218 221 73 161 213 249 55 620 758 445 2914 74

12 131 258 84 136 107 860 90 306 44 46 72 2556 714 239

13 229 240 197 220 117 303 318 330 63 219 838 1275 4545 359

14 19 30 16 21 18 53 16 35 6 10 20 408 344 26

157

Table (6-15): Trip Assignment Results on Road Network in Year 2030 (Do- nothing Scenario).

Link

Start Node

End Node

Traffic Volume (pcu/hr)

Volume to Capacity Ratio (V/C) % Time Delay

1 9 676 0.514 2.682 9 1 556 0.423 3.429 2 5 72 0.055 0.001 5 2 6 0.004 0.000 3 5 457 0.348 1.630 5 3 676 0.514 2.682 6 7 0 0.000 0.000 7 6 0 0.000 0.000 7 8 0 0.000 0.000 8 7 0 0.000 0.000 8 4 1515 0.436 3.800 4 8 4239 1.220 14090.881 8 29 4239 1.220 14090.881 29 8 1515 0.436 3.800 4 3 1520 1.157 941.552 3 4 1554 1.183 299.130 2 9 54 0.041 0.000 9 2 72 0.055 0.001 3 10 1102 0.838 66.788 10 3 917 0.698 11.880 9 15 907 0.690 9.955 15 9 715 0.544 6.421 10 14 968 0.737 59.918 14 10 823 0.626 20.615 4 11 1422 0.409 2.340 11 4 2073 0.596 21.722 11 12 3274 0.942 302.731 12 11 1634 0.470 8.470 12 13 3870 1.113 2014.196

158


Start Node

End Node


Volume to Capacity Ratio (V/C)

% Time Delay

13 12 439 0.126 0.010 10 11 748 1.575 2512.224 11 10 852 1.795 2481.245 14 13 42 0.032 0.000 13 14 669 0.509 7.374 10 9 1463 1.113 175.505 9 10 1319 1.004 162.978 14 15 1518 1.155 463.162 15 14 1202 0.915 692.577 15 16 1618 1.231 345.948 16 15 956 0.727 23.800 86 87 808 1.701 4468.687 87 86 270 0.569 2.183 87 88 1169 2.460 579485341.757 88 87 65 0.136 0.005 88 89 93 0.197 0.023 89 88 1151 2.423 132645984.426 89 86 1591 1.721 160625.494 86 89 330 0.357 3.044 14 86 544 0.589 31.530 86 14 978 1.058 214.170 15 87 807 1.699 1590.541 87 15 72 0.151 0.033 88 25 18 0.038 0.000 89 23 953 2.007 366571.171 13 18 3296 0.948 308.286 20 22 2961 0.852 221.374 20 86 379 0.799 25.624 22 89 402 0.846 207.397

159


Start Node

End Node



% Time Delay

51 23 3504 1.008 515.7639 18 20 2747 0.790 94.15317 22 51 3025 0.870 117.3595 22 51 237 0.068 0.000323 22 51 237 0.068 0.000323 11 12 114 0.033 1.72E-05 11 12 114 0.033 1.72E-05 12 13 262 0.075 0.000483 18 17 980 0.746 16.64301 18 17 264 0.201 0.024332 87 19 605 1.274 61.93252 88 21 93 0.197 0.022513 17 19 1178 1.275 407.0754 19 21 1283 1.388 1234.364 17 19 78 0.085 0.000778 19 21 48 0.052 0.000113 21 24 454 0.492 1.361993 21 24 0 0.000 0 23 25 4167 1.199 3435.265 25 24 3007 0.865 138.75 25 24 1 0.000 4.4E-13 17 16 849 0.919 47.13301 17 16 129 0.140 0.005774 51 52 772 1.625 4001.941 52 53 238 0.502 5.22769 52 55 1286 2.708 9.57E+08 55 53 534 1.124 1589.391 53 54 737 0.561 31.47019 24 55 1143 2.407 3.1E+09

160


Start Node

End Node



% Time Delay

54 47 59 0.064 0.000249 54 47 59 0.064 0.000249 47 46 462 0.972 20.24609 46 45 922 1.941 19665.92 45 51 834 1.756 787.0972 55 72 2717 2.068 2.45E+09 72 68 3331 2.535 3.1E+17 68 73 3688 2.806 4.74E+18 73 26 2388 1.817 2.28E+09 72 27 1029 0.783 26.2461 24 27 2759 1.164 1483.532 24 27 36 0.015 8.3E-07 27 26 2456 1.036 390.4565 26 28 534 0.225 0.119412 24 25 115 0.242 0.097581 28 25 836 0.303 0.694521 25 16 32 0.011 2.62E-07 1 83 556 1.170 527.3595 5 85 528 1.112 551.5285 16 83 336 0.122 0.00874 83 85 892 0.323 1.117612 85 84 4521 1.638 1083902 84 29 2167 0.785 166.2255 77 84 1004 0.764 25.82401 77 78 1654 1.790 13012905 78 79 321 0.347 0.552548 79 80 1331 0.562 16.17783 80 81 1911 2.068 9.58E+09 79 76 135 0.057 0.00016

161


Start Node

End Node



% Time Delay

76 29 135 0.057 0.00016 75 80 957 0.728 19.94277 75 30 77 0.058 0.000173 29 30 1236 0.448 5.646166 30 31 596 0.251 1.237391 31 32 596 0.216 0.67093 6 7 0 0.000 0 31 34 1285 1.390 480.3073 4 34 121 0.131 0.004444 11 34 140 0.107 0.010647 33 34 2346 1.785 70074.65 33 32 1054 0.802 161.6494 34 90 84 0.091 0.003628 90 40 2416 2.614 1.22E+10 90 35 3033 2.308 1.64E+09 33 36 67 0.051 0.000229 90 36 2984 2.271 1.45E+09 36 37 1325 1.009 108.6241 37 38 1905 2.061 1923883 38 56 1214 1.314 2118.681 38 39 1614 1.228 563.6887 12 35 1260 0.532 7.613137 35 40 802 0.338 1.367035 40 39 1164 0.491 9.541677 39 41 1485 0.627 40.75611 41 42 1300 0.549 20.08536 42 56 1131 0.861 156.1844 32 82 1836 0.665 57.2868 82 57 2913 1.998 1.69E+12

162


Start Node

End Node



% Time Delay

56 57 2345 1.784 2.53E+12 41 43 2 0.004 5.41E-09 43 40 108 0.227 0.187318 41 44 497 1.047 149.8345 44 50 238 0.501 4.206062 42 49 551 1.160 450.7499 49 48 551 1.160 450.7499 42 50 1262 0.960 118.0648 50 54 1500 1.141 808.6903 43 44 288 0.606 11.49415 44 46 547 1.152 921.8565 57 61 2319 2.510 3.25E+17 57 64 2763 1.166 15850.31 64 59 3985 4.313 7.08E+20 64 91 914 0.386 1.439576 60 69 699 0.532 2.002143 58 63 185 0.200 0.244495 59 63 3985 4.313 7.08E+20 61 60 719 0.778 40.1483 60 62 788 0.852 115.4643 62 63 785 0.850 106.9307 60 65 2473 2.677 1.33E+09 65 68 3154 3.414 8.92E+17 69 91 73 0.079 0.000587 69 66 2619 2.835 1.67E+08 66 65 1874 2.029 99361.99 66 67 2426 5.106 4.94E+18 67 68 2252 4.740 2.57E+17 91 70 5368 4.085 3.46E+21

163


Start Node

End Node



% Time Delay

70 74 6944 5.284 7.18E+48 74 73 295 0.225 0.038113 70 27 2035 2.203 443072.1 32 37 1091 1.181 286.1354 25 88 1151 2.423 1.33E+08 23 89 291 0.612 29.76212 18 13 493 0.142 0.019877 22 20 837 0.241 0.270713 86 20 669 1.408 331.5187 89 22 1023 2.153 1145899 23 51 617 0.177 0.048633 20 18 912 0.262 0.212294 51 22 87 0.025 6.31E-06 51 22 237 0.068 0.000323 51 22 237 0.068 0.000323 12 11 114 0.033 1.72E-05 12 11 114 0.033 1.72E-05 13 12 262 0.075 0.000483 17 18 11 0.008 6.5E-08 17 18 264 0.201 0.024332 19 87 500 1.053 51.7241 21 88 1151 2.423 1.33E+08 19 17 99 0.107 0.007388 21 19 99 0.107 0.007388 19 17 78 0.085 0.000778 21 19 48 0.052 0.000113 24 21 421 0.456 2.616701 24 21 0 0.000 0 25 23 618 0.178 0.063477

164


Start Node

End Node



% Time Delay

24 25 1 0.000 4.4E-13 24 25 1 0.000 4.4E-13 16 17 959 1.037 106.4357 16 17 129 0.140 0.005774 52 51 455 0.958 104.6827 53 52 602 1.268 225.7336 55 52 606 1.276 3037.451 53 55 1029 2.166 3.35E+08 54 53 1596 1.215 2171.368 55 24 554 1.166 4423.92 47 54 421 0.455 3.708536 47 54 59 0.064 0.000249 46 47 373 0.785 32.3576 45 46 463 0.974 92.39999 51 45 568 1.195 347.7103 72 55 719 0.547 16.17714 68 72 2803 2.133 1.29E+11 73 68 1073 0.817 55.10016 26 73 3690 2.808 2.99E+18 27 72 3020 2.298 7.22E+08 27 24 423 0.179 0.054114 27 24 36 0.015 8.3E-07 26 27 2111 0.891 221.8654 28 26 2532 1.069 2010.542 25 24 1 0.003 1.24E-09 25 28 1793 0.650 14.26573 16 25 1464 0.530 6.190433 83 1 676 1.422 272.3471 85 5 676 1.422 272.3471

165


Start Node

End Node



% Time Delay

83 16 1216 0.441 2.351506 85 83 1892 0.685 22.18336 84 85 161 0.058 0.001209 29 84 4546 1.647 2361107 84 77 1654 1.259 3410.955 78 77 1004 1.087 143.6975 79 78 1610 1.743 8313728 80 79 406 0.171 0.031867 81 80 29 0.032 1.79E-05 76 79 2349 0.991 424.8109 29 76 2349 0.991 424.8109 80 75 106 0.081 0.000647 30 75 957 0.728 19.94277 30 29 3105 1.125 1054.524 31 30 3422 1.444 396177 32 31 3547 1.285 6459.996 7 6 0 0.000 0 34 31 1159 1.255 701.4536 34 4 2039 2.207 639196.8 34 11 2535 1.929 404319.6 34 33 333 0.253 0.361133 32 33 1429 1.087 369.6133 90 34 2380 2.575 2.09E+09 40 90 118 0.128 0.0204 35 90 7 0.005 1.02E-08 36 33 1705 1.298 572.4131 36 90 333 0.254 0.843489 37 36 313 0.238 0.614838 38 37 840 0.909 209.0076

166


Start Node

End Node



% Time Delay

56 38 1455 1.575 49529.35 39 38 308 0.234 0.236167 35 12 3050 1.287 4512.396 40 35 2823 1.191 1071.799 39 40 1065 0.449 3.875035 41 39 80 0.034 4.94E-05 42 41 326 0.137 0.016274 56 42 1886 1.435 7990.688 82 32 5215 1.889 1.19E+12 57 82 4431 3.039 1.17E+16 57 56 3341 2.543 3.59E+17 43 41 119 0.250 0.058734 40 43 286 0.602 11.43113 44 41 69 0.145 0.013738 50 44 40 0.084 0.000737 49 42 42 0.088 0.000884 48 49 42 0.088 0.000884 50 42 42 0.032 1.51E-05 54 50 42 0.032 1.51E-05 44 43 108 0.227 0.187318 46 44 177 0.372 1.335447 61 57 1918 2.075 4.64E+13 64 57 7243 3.056 4.39E+20 59 64 145 0.157 0.014407 91 64 7332 3.094 3.37E+20 69 60 2650 2.017 70307.97 63 58 2096 2.268 5546082 63 59 145 0.157 0.014407 60 61 2624 2.840 1.24E+18

167


Start Node

End Node

Traffic Volume (epcu/hr)


% Time Delay

62 60 1904 2.061 31150.77 63 62 2122 2.296 1.17E+18 65 60 1311 1.419 17456.02 68 65 1097 1.188 763.4091 91 69 4451 4.817 1.18E+22 66 69 120 0.130 0.004333 65 66 980 1.060 41.9337 67 66 820 1.727 1100.014 68 67 2222 4.678 2.62E+15 70 91 73 0.056 0.000144 74 70 429 0.327 0.171149 73 74 6944 5.284 7.18E+48 27 70 3611 3.908 1.89E+18 37 32 1038 1.124 821.8925 45 22 262 0.427 1.801802 22 45 69 0.112 0.006563 78 81 1910 2.067 7.3E+09 81 78 942 1.019 2088.064 48 47 551 1.160 450.7499 47 48 42 0.088 0.000884 61 58 185 0.389 3.554567 58 61 2096 4.413 1.14E+19 61 58 186 0.391 0.34902 58 61 186 0.391 0.34902

The analysis of the trip assignment of the study area in 2030 illustrates that traffic volumes on many links will exceed the capacity, in the first place lies the link (91-64) which has the highest traffic volume with about 7332 (pcu/hr) which represents about 3 times its capacity. Link (64-57) and (73-74) have the second and third places with 7243 and 6944 (pcu/hr) representing (V/C) ratios of 3.05 and 5.3 respectively. On the other hand,

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some links of the road network of the study area like link (17-18) and link (88-25) will have very small traffic flows of about 11 and 18 (pcu/hr) representing a (V/C) ratio of 0.0087 and 0.038 respectively. Regarding to volume to capacity ratio, the analysis of the road network in the target year shows that - alarmingly - 129 links representing about 41.1% of the number of road network links will have (V/C) ratio exceeds 1.00. About 26.8% of the road network links will have (V/C) ratio between 0 and 0.2, while about 7% of the total network links will have volume to capacity ratio between 0.8 and 1.00. About 7% of the total number of links will carry volume to capacity ratio between 0.6 and 0.8. Average volume to capacity ratio on the road network in year 2030 will be 0.84. Fig (6-18) represents the (V/C) ratios of the road network (in number) links of the study area in target year 2030. Fig (6-19) illustrates the (V/C) ratio of the road links of the study area (in %) in target year 2030.

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No

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(0-0.2) (0.2-0.4) (0.4-0.6) (0.6-0.8) (0.8-1.0) ( >1 )

No. of Road Network Links in every (V/C) Range

Fig (6-18): (V/C) Percentage for Road Network (in number) of Study Area –( Target year 2030) – Do-nothing Scenario.

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8.9%9.2%7.0%

41.1%

7.0%26.8%

0<(V/C)≤0.2

0.2<(V/C)≤0.4

0.4<(V/C)≤0.6

0.6<(V/C)≤0.8

0.8<(V/C)≤1

(V/C)>1

Fig (6-19): (V/C) Percentage of the Road Links of the Study Area in Target

year 2030 (Do-nothing Scenario).

In general, reducing (V/C) ratios on links can be achieved through increasing the capacity of the links and decreasing traffic volumes. To maximizing the capacity of road links, the following measures can be taken into considerations:

• Improving the pavement case on links. • Increasing the road width trough extensions on the right of way. • Parking control action along these links including restricting or

prohibiting on road side. • Introducing traffic signal system at intersections to minimize

cognitions. The analysis of the results of the trip assignment stage for target year indicated the following critical road links in the study area, these links must have urgent measures:

• Links (88-89), (21-88), (25-88), (29-88), and (87-88) and (22-89). These links contact zone (3) and zone (4) in the eastern side of Tanta city, since it has high population, educational places and commercial zones. Relocation of such trip attractive factors can be a good solution for the traffic problems in this region.

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• Conflict between pedestrians and traffic lies in road links (9-15), (15-87), (87-88), (14-86), (86-89) and (89-23). Relocation of traffic movement with announcing a time-limited pedestrian area can be a short term measure for this conflict. It can also be designed as a traffic calming area, with a maximum speed of 30km/hr and pedestrian facilities.

• Links (68-67), (60-65) and (65-68), which lie on zone (12), (13) and

(14) on the western entrance of the city, since it contains industrial area of the city. Constructing a new arterial between these zones can be medium term measure for this region.

• Links (57-82), (82-32) and (30-29), (29-84), (84-85) and (85-83).

These links acts as a direct sequential connection between zones (6), (2), (9) and (1). Parking control system can be a short term measure for these links.

• Links (16-17), (17-19), (19-21) and (21-24). These links represent

the eastern city entrance and lead to the main train station in Tanta city and also to the main mosque (Alsayed Elbadawy Mosque).

The output of the trip assignment stage contains the travel time delay. The travel time delay represents a performance indicator in estimating the quality of travel on a specific road link. Since the travel time after assignment is a function of traffic volume to capacity ratio and directly proportion to this ratio, the time delays increase on the link as its (V/C) ratio increases. The analysis of the travel time delay on road network in 2030 shows that some links like (91-70), (91-69) and (73-74) carrying high (V/C) ratio, will have a percentage delay time exceed 100%. Road network links of the same situation represent 45.9% of the total number of the road network. 145 links representing 46.2% of the number of the road network links in year 2030 will have percentage delays varies from 0 to 20% as link (4-34) and link (16-17), while about 4.1% of the links will be influenced by time delays percentage between 20% and 40% like link (16-15) and link (20-86). About 0.6% of the road network links will have 80% to 100% time delay. Average percent time delay on the road network links exceeds 100%. Fig (6-20) shows the number of road links in different time delay ranges in target year 2030 (Results of trip assignment stage). Fig (6-21) represents the

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time delay percentage of road links in different time delay ranges in target year 2030 (results of trip assignment stage).

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160

No

. of R

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inks

(0-20%) (20%-40%) (40%-60%) (60%-80%) (80%-100%) ( >100% )

No. of Road Network Links in everyTime Delay Range

Fig (6-20): Number of Road Network Links in different Time Delay Ranges for year 2030 (Do-nothing Scenario).

45.9% 46.2%

0.6% 2.6%

4.1%0.6%

0%<%Time Delay≤20%




80%<%TimeDelay≤100%%Time Delay>100%

Fig (6-21): Time Delay Percentage of Road Links in different Time Delay

Ranges (Year2030 – Do-noting Scenario).

172

6.8 Operational Evaluation of the Road Network The operational evaluation of the road network under specific traffic volumes is to determine the level of services which represent ranges of operating conditions to give a qualitative measure of traffic on each road network link. In the proposed program, level of service is determined using (HCM2010 –NCHRP 3-70). The LOS model predicts the average degree of satisfaction rating for the facility, where LOS (A) is “very satisfied” and LOS (F) is “very dissatisfied”. It represents the 6th stage of the proposed program. In this stage, the planner is asked to loads the trip assignment table as an MS.Excel table; including link start node and end node, links (V/C) ratio and length of each link in kilometer besides left turn presence. Also, the planner is asked to determine the signal progression type by selecting one type from the three types through marking the selection icon beside the suitable progression type. By pressing the icon "Get road network links level of service", the program determines the level of service of each link according to (HCM2010 –NCHRP 3-70) method, and results can be saved in MS.Excell sheet in the output folder so that it can be printed. Fig (6-22) represents the menu of the 6th stage of the proposed program. Table (6-16) indicates the output of the operational evaluation stage (year 2030), according to (HCM2010 –NCHRP 3-70). Analysis of the network evaluation shows that 5.4% of the road network links (representing 17 links) will have level of service (A), while about 41% of the network links will be suffering with level of service (F) in year 2030. The rest of the network links (53.5%) will have level of service of grade (B). No network link will have level of service (C) (D) or (E). Fig (6-23) shows level of service percentage of road network links for target year 2030.

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53.5%

41.1%

5.4%

LOS(A)

LOS(B)

LOS(F)

Fig (6-23): Level of Service Percentage of Road Network Links for Year

2030 (HCM2010 –NCHRP 3-70 – Do-nothing Scenario).

Fig (6-22): Operational Evaluation Using the Proposed Program – 6th Stage.

174

175

Table (6-16): LOS of Road Links in the Study Area in 2030 (Output of 6th Stage – Do-nothing Scenario).

Link Link Start Node

End Node

(LOS) Start Node

End Node

(LOS)

1 9 B 13 12 B 9 1 B 10 11 F 2 5 B 11 10 F 5 2 B 14 13 B 3 5 B 13 14 B 5 3 B 10 9 F 6 6 A 9 10 F 7 7 A 14 15 F 7 8 A 15 14 B 8 7 A 15 16 F 8 4 B 16 15 B 4 8 F 86 87 F 8 29 F 87 86 B 29 8 B 87 88 F 4 3 F 88 87 B 3 4 F 88 89 B 2 9 B 89 88 F 9 2 B 89 86 F 3 10 B 86 89 B 10 3 B 14 86 B 9 15 B 86 14 F 15 9 B 15 87 F 10 14 B 87 15 B 14 10 B 88 25 B 4 11 B 89 23 F 11 4 B 13 18 B 11 12 B 20 22 B

12 11 B 20 86 B

12 13 F 22 89 B

176

Table(6-16): continued Link Link Start Node

End Node

(LOS) Start Node

End Node

(LOS)

51 23 F 54 47 B 18 20 B 54 47 B 22 51 B 47 46 B 22 51 B 46 45 B 22 51 B 45 51 F 11 12 B 55 72 F 11 12 B 72 68 F 12 13 B 68 73 F 18 17 B 73 26 F 18 17 A 72 27 F 87 19 F 24 27 B 88 21 B 24 27 F 17 19 F 27 26 B 19 21 F 26 28 F 17 19 B 24 25 B 19 21 B 28 25 A 21 24 B 25 16 B 21 24 A 1 83 A 23 25 F 5 85 F 25 24 B 16 83 F 25 24 B 83 85 A 17 16 B 85 84 B 17 16 B 84 29 F 51 52 F 77 84 B 52 53 B 77 78 B 52 55 F 78 79 F 55 53 F 79 80 B 53 54 B 80 81 B 24 55 F 79 76 F

177


End Node

(LOS) Start Node

End Node

(LOS)

76 29 B 56 57 F 75 80 B 41 43 B 75 30 B 43 40 B 29 30 B 41 44 F 30 31 B 44 50 B 31 32 B 42 49 F 6 7 A 49 48 F 31 34 F 42 50 B 4 34 B 50 54 F 11 34 B 43 44 B 33 34 F 44 46 F 33 32 B 57 61 F 34 90 B 57 64 F 90 40 F 64 59 F 90 35 F 64 91 A 33 36 B 60 69 B 90 36 F 58 63 B 36 37 F 59 63 F 37 38 F 61 60 B 38 56 F 60 62 B 38 39 F 62 63 B 12 35 B 60 65 F 35 40 B 65 68 F 40 39 B 69 91 B 39 41 B 69 66 F 41 42 B 66 65 F 42 56 B 66 67 F 32 82 B 67 68 F 82 57 F 91 70 F

178


End Node

(LOS) Start Node

End Node

(LOS)

70 74 F 24 25 B 74 73 B 24 25 B 70 27 F 16 17 F 32 37 F 16 17 B 25 88 F 52 51 B 23 89 B 53 52 F 18 13 B 55 52 F 22 20 B 53 55 F 86 20 F 54 53 F 89 22 F 55 24 F 23 51 B 47 54 B 20 18 B 47 54 B 51 22 B 46 47 B 51 22 B 45 46 B 51 22 B 51 45 F 12 11 B 72 55 B 12 11 B 68 72 F 13 12 B 73 68 B 17 18 A 26 73 F 17 18 A 27 72 F 19 87 F 27 24 B 21 88 F 27 24 B 19 17 B 26 27 B 21 19 B 28 26 F 19 17 B 25 24 A 21 19 B 25 28 B 24 21 B 16 25 B 24 21 A 83 1 F 25 23 B 85 5 F

179


End Node

(LOS) Start Node

End Node

(LOS)

83 16 B 56 38 F 85 83 B 39 38 B 84 85 A 35 12 F 29 84 F 40 35 F 84 77 F 39 40 B 78 77 F 41 39 B 79 78 F 42 41 B 80 79 B 56 42 F 81 80 B 82 32 F 76 79 B 57 82 F 29 76 B 57 56 F 80 75 B 43 41 B 30 75 B 40 43 B 30 29 F 44 41 B 31 30 F 50 44 B 32 31 F 49 42 B 7 6 A 48 49 B 34 31 F 50 42 B 34 4 F 54 50 B 34 11 F 44 43 B 34 33 B 46 44 B 32 33 F 61 57 F 90 34 F 64 57 F 40 90 B 59 64 B 35 90 B 91 64 F 36 33 F 69 60 F 36 90 B 63 58 F 37 36 B 63 59 B 38 37 B 60 61 F

180


Start Node

End Node

(LOS)

62 60 F 63 62 F 65 60 F 68 65 F 91 69 F 66 69 B 65 66 F 67 66 F 68 67 F 70 91 B 74 70 B 73 74 F 27 70 F 37 32 F

45 22 B

22 45 B

78 81 F

81 78 F

48 47 F

47 48 B

61 58 B

58 61 F

61 58 B

58 61 B

181

The HCM2000 (Average Travel Speed - ATS) method of determining level of service on urban streets depends on the average travel speed as the factor determines the level of service category. The operating speed on each link of the study area can be calculated using the link length and travel time after assignment. Using (HCM2000 – ATS) method, 123 Links representing 39.2% of the road network will be in LOS (F). 148 link representing 47.1% of the road network links will be in LOS (A), while about 1.9% of the road network will have a LOS (E). LOS (B) and LOS (C) are represented by a percentage of 5.1% and 3.5% respectively. Fig (6-24) shows level of service percentage of road network links in year 2030 calculated by (HCM2000 – ATS) method.

1.9% 5.1%3.5%3.2%

47.1%39.2%

LOS(A)

LOS(B)

LOS(C)

LOS(D)

LOS(E)

LOS(F)

Fig (6-24): Level of Service Percentage of Road Network Links in

Year2030 (HCM2000 – ATS method – Do-nothing Scenario).

Comparison between the LOS of road network calculated by (HCM2010 –NCHRP 3-70) method and (HCM2000 – ATS) method shows that, percentage number of road network links in LOS (A) in the first method is less than calculated by the 2nd method by 41.7%. Percentage number of road network links in LOS (B) in the first method is more than calculated by the 2nd method by 48.4% of total number road network links. Using (HCM2010 –NCHRP 3-70), no links will be in LOS (C), (D) or (E), while using (HCM2000 – ATS) led to that 3.5%, 3.2% and 1.9% of the total number of Tanta city road network links will be in these level of services respectively.

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Regarding to LOS (F), the percentage number of road network links in LOS (F) using the (HCM2010 –NCHRP 3-70) method is more than calculated by (HCM2000 – ATS) method by 1.9% of the total number road network links. Fig (6-21) shows the difference between percentage links in every LOS calculated by the three methods. Table (6-17) shows a comparison between percentage of road network links in different level of services determined by the (HCM2010 –NCHRP 3-70) and (HCM2000 – ATS) in Tanta city in year 2030. Fig (6-25) shows the difference between percentage links in every LOS calculated by the two methods.

Table (6-17): Comparison between Percentage Level of Service of Road Network Links Determined by (HCM2010 –NCHRP 3-70) Method and

(HCM2000 – ATS) Method.

(LOS) (HCM2010 –NCHRP 3-70)

Method

(HCM2000 – ATS)

Method

A 5.4 47.1

B 53.5 5.1

C — 3.5

D — 3.2

E — 1.9

F 41.1 39.2

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Fig (6-25): Comparison between LOS Percentage LOS Calculated by (HCM2010 –NCHRP 3-70) Method and (HCM2000 – ATS) Method in

Study Area for Year 2030. This difference between (HCM2010 –NCHRP 3-70) method and (HCM2000 – ATS) method refers to that the (HCM2000 – ATS) method does not consider the distribution of LOS grades for a given situation and not based on surveys of traveler satisfaction so, it reports a single LOS grade for a given situation. On the other hand, the (HCM2010 –NCHRP 3-70) LOS method estimates the quality of services traveling on urban streets from the user satisfaction perspective, allowing the traveler to self-identify the best and worst conditions based on their own experience and perceptions for each individual situation. This method converts the statistical distribution of LOS grades reported by the public individuals into a single LOS grade for a given situation. Also, in the (HCM2010 –NCHRP 3-70) method, there are no road network links in LOS(C), (D) or (E). This is because this method squeezes together the available range limits of LOS model for LOS(C) to LOS(E) so that wider ranges are available for LOS (A), LOS (B) and LOS (F). These larger ranges for the extreme LOS grades is to ensure that extreme LOS grades

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(HCM2010 –NCHRP 3-70) Method

(HCM2000 – ATS) Method

184

will be output for distributions if a large portion of the individual select that LOS. Improving the LOS of the road network of the study area can be performed by:

• Improving the existing public transport system by increasing the capacity of public buses and collection taxies over the short terms.

• Establishing a quick plan for improving the pavement and lighting case of the existing road network and developing new arterials to connect the city sub-zones by the outer ring roads around the city.

• Providing an integrated traffic signals system that is operated according the actual traffic volumes on streets using traffic measures on main roads.

• Providing parking control actions including the providing of parking zones, parking prohibition and parking time restrict policies.

• Creating car-free zones (only pedestrian zones) in the main old and recent shopping areas.

• Modal shift plan including the investment in new public transport systems which has the capability to absorb the rapidly increased travel demand and to attract private car users. This modal shift can be exist through:

• Light rail systems as light rail transit (LRT) or regional rail transit (RRT). These systems have the ability to operate in mixed traffic, and with developing plans, its infrastructure can be transferred to under ground metro. • Water transport system using Alqased water channel. this system has the ability to serve a big travel demand with minimum economic cost.

• Introduce traffic management plans to reduce inter-city transport congestion and vehicle on on-road time.

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6.9 Environmental Assessment The final stage of the proposed program is the environmental Assessment. Evaluating any transportation system does not only depend on the operational conditions dimension, but also include the environmental cost as these transportation systems have a direct influence on the environment thought air pollution emissions and noise. The program evaluates the transportation system in two stages:

• Air pollution assessment. • Noise pollution assessment.

6.9.1 Air Pollution Assessment Transport sector is one of the biggest sources of emissions. Egyptian transport sector produces about 25 % of the energy related CO2 emissions, Egypt ranks 15th in terms of Co2 emissions (127.2 million tons of CO2 per year) [37]. The estimation of greenhouse gases from transport is essential to mitigate these emissions of greenhouse gases. Transport emissions – including CO2, N2O and CH4 emissions - depend on the transport mode, fuel type, energy consumption rate, behavior of car driver and status of transport mode and the traffic volume. Air pollution assessment model used in the proposed program uses the passenger-kilometer transport productivity to estimate the emissions. Passenger-kilometer is a unit of transport productivety measure, representing the transport of one passenger over a distance of one kilometer. The Passenger.Kilometer transport productivity for every transport mode can be calculated using the following formula:

L*QTV m =

Where: TVm : Transport productivity of mode m, in (pass.km/day).

L : Average trip length of a passenger (km). For study area equals to 22 km. Q : Demand of transport mode (trip/day).

186

Applying the proposed program for the target year (2030) indicates that, the daily transport productivity of private cars is 2051610 (Passenger.Km), while the transport productivity of taxi is 3295006 (Passenger.Km). Bus represents a daily transport productivity of 5129036 (Passenger.Km), while microbus and motorcycle will have daily transport productivity of 4227564 and 839300 (Passenger.Km) respectively. In this stage, the program calculates Co2, equivalent Co2 of N2O emissions and equivalent Co2 of CH4 emissions of every mode of transport. The program asks to input the following parameters:

• The specific primary energy consumption of transport mode (MJ/Veh.km).

• The Co2 emission rate of transport mode in (kg Co2 /MJ) • Occupancy rate of transport mode. • Factors of behavior of car driver and status of transport mode. • The CH4 emission rate. • The N2O emission rate. • Converting coefficient of CH4 N2O to Co2.

The proposed program then calculates Co2, equivalent Co2 of N2O and equivalent Co2 of CH4 emissions output text boxes so that the planner can analyze emission of every single mode. Fig (6-26) shows the menu of the 7th stage of the proposed program. For the study area, all transport modes are benzene powered except bus is diesel powered. Table (6-18) represents the emissions produced from transportation sector in do-nothing scenario in study area (year 2030). Results show that, total Co2 and Co2 equivalent emission resulting from the transport sector in the study area in year 2030 will contribute the pollution of Tanta city with about 1892670 KgCo2/day. Private cars are responsible for the biggest share in this pollution with a daily 513598.9 kgCo2 representing 27.1% of the total transport sector emissions. In the second rank, taxi shares 499921.2 KgCo2/day representing 26.4% of the total transport emissions in year 2030. Bus comes third with a share of 17.7% of the total emissions. Microbus and Motorcycle come sequence in the last ranks with a share of 16.8% and 12% of the total transport sector emissions respectively.

Fig (6-26): Air Pollution Assessment Using the Proposed Program – 7th Stage.

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188

Table (6-18): Total Co2 and Co2 Equivalent Emission Produced from Transportation Systems in Year 2030 (kg/day) – (Do-nothing Scenario).

Transport Mode Co2

Emissions (kgCO2)

Co2 Equivalent Emission from CH 4

(kgCo2)

Co2 Equivalent Emission fromN2O

(kgCo2)

Total Mode

Emissions (kgCo2)

Percentage of Total

Transport System

Emissions

Private Cars 466410 7210.9 39978 513598.9

% of the Total Emissions of the

Mode 90.8% 1.4% 7.8% 100%

27.1

Taxi 453989 7018.8 38913.4 499921.2


Mode 90.8% 1.4% 7.8% 100%

26.4

Bus 323704 384.4 10615.4 334703.8


Mode 96.7% 0.1% 3.2% 100%

17.7

Microbus 307419 365.1 10081.3 317865.4


Mode 96.7% 0.1% 3.2% 100%

16.8

Motorcycle 219134 260.2 7186.2 226580.4


Mode 96.7% 0.1% 3.2% 100%

12

Total Emissions 1892669.7 (kg co2 /day)

189

Fig (6-27) illustrates amounts of emissions of different transport modes in study area in year 2030 (Do-nothing scenario). Fig (6-28) shows the percentage of co2 equivalent emissions of different transport systems for target year 2030 (Do-nothing scenario).

Fig (6-27): Emissions According to Transport Mode for Study Area in 2030 (Do-nothing Scenario).

12.0%

26.4% 17.7%

16.8%

27.1%

Private Car

Taxi

Bus

Microbus

Motorcycle

Fig (6-28): Percentage of Co2 Equivalent Emissions of Different Transport

Systems for Target Year 2030 (Do-nothing scenario).

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CO2 EmissionsCO2 Equivalent Emissions from(CH4) CO2 Equivalent Emissions from(N2O)Total Emissions

190

To control emissions produced in the study area, the following countermeasures are suggested:

• Improving fuel efficiency. • Switching to Natural Gas and Hydrogen power in vehicles. • Enforcement of vehicle emissions standards as a pre-requirement for

vehicle license issuance • Following car renewal new policies. • Strengthen the policy of modifying travel demand, including

interpolation of new environment friendly transport modes into the transportation system to substitute split ratios of public transport.

• Improving public transport system to increase its demand. 6.9.2 Noise Assessment Vehicles are major source of noise in cities. Noise has harmful effect on human causing sleep disturbance and annoyance. The amount of noise resulting from any traffic volume depends on the traffic composition, traffic speed, pavement type and the distance from the roadway to place of measurement. The 8th stage of the proposed program is noise pollution assessment. The program asks the user to load (from the input folder) a network description MS.Excel sheet including traffic volume (pcu/hr) and speed in (km/hr) on each road network link. The planner is asked also to fill in a text box to input the distance from road center line at which the mean noise level should be determines. Also, proportion of truck of the traffic volume is asked to be input by the planner. The pavement type and rain condition is also determined by the user trough marking the selection icon beside the desired pavement and road conditions. By pushing the icon "Get road network links mean noise levels dB(A), the program calculate the mean noise level in db(A) on each road network link and open a save window so that the user can save the results in MS.Excell which can be also printed. To get the mean noise level at a specific distance from the center line of a road, the proposed program calculate the mean travel speed on this link using the travel time after assignment stage and the length of the link. The type of pavement in the study area is flexible pavement and the weather condition indicates no rain the most year time. In study urban area, it is not allowed for heavy traffic to enter the urban area, so no truck percentage is

191

assumed in applying the program on study area. Only the outer highway links around the city is assumed to have 20% heavy traffic. The mean noise level is calculated on study area at 10m distance from the road link center line. Fig (6-29) illustrates the menu of the 8th stage of the proposed program. Table (6-19) represents the outputs of the 8th stage of the proposed program.

Fig (6-29): Noise Pollution Assessment Using the Proposed Program – 8th Stage.

192

193

Table (6-19): Mean Noise Level of the Road Network Links in Year 2030 in dB(A) (Do-nothing Scenario).

Link Link Start Node

End Node

Mean Noise Level dB(A).

Start Node

End Node


1 9 68.5 13 12 66.8 9 1 67.6 10 11 86.8 2 5 58.9 11 10 87.2 5 2 48.2 14 13 56.6 3 5 66.9 13 14 68.2 5 3 68.5 10 9 70.0 6 6 0.0 9 10 69.5 7 7 0.0 14 15 73.3 7 8 0.0 15 14 75.0 8 7 0.0 15 16 72.2 8 4 72.0 16 15 69.1 4 8 120.9 86 87 95.2 8 29 120.9 87 86 64.6 29 8 72.0 87 88 311.8 4 3 78.7 88 87 58.5 3 4 71.4 88 89 60.1 2 9 57.7 89 88 284.2 9 2 58.9 89 86 160.4 3 10 68.8 86 89 65.4 10 3 69.4 14 86 66.4 9 15 69.4 86 14 68.5 15 9 68.6 15 87 81.4 10 14 68.3 87 15 58.9 14 10 68.6 88 25 52.9 4 11 71.8 89 23 173.5 11 4 72.6 13 18 74.8 11 12 74.7 20 22 73.4

12 11 72.1 20 86 65.0

12 13 91.1 22 89 64.6

194


End Node


Start Node

End Node


51 23 77.6 54 47 58.1 18 20 72.6 54 47 58.1 22 51 72.9 47 46 66.1 22 51 64.1 46 45 120.0 22 51 64.1 45 51 74.5 11 12 60.9 55 72 342.4 11 12 60.9 72 68 691.7 12 13 64.6 68 73 743.1 18 17 69.5 73 26 340.5 18 17 64.6 72 27 69.4 87 19 66.3 24 27 86.0 88 21 60.1 24 27 55.9 17 19 71.5 27 26 74.5 19 21 80.6 26 28 67.6 17 19 59.3 24 25 61.0 19 21 57.2 28 25 69.6 21 24 66.9 25 16 55.4 21 24 0.0 1 83 69.7 23 25 98.5 5 85 69.8 25 24 72.9 16 83 65.6 25 24 40.4 83 85 69.8 17 16 68.0 85 84 200.4 17 16 61.5 84 29 71.6 51 52 93.4 77 84 69.3 52 53 63.9 77 78 242.5 52 55 321.6 78 79 65.4 55 53 79.6 79 80 70.8 53 54 67.8 80 81 366.4 24 55 343.1 79 76 61.7

195


End Node


Start Node

End Node


76 29 61.7 56 57 471.4 75 80 69.3 41 43 43.4 75 30 59.2 43 40 60.7 29 30 71.0 41 44 65.2 30 31 68.1 44 50 63.9 31 32 68.1 42 49 68.8 6 7 0.0 49 48 68.8 31 34 72.8 42 50 69.1 4 34 61.2 50 54 77.3 11 34 61.8 43 44 64.4 33 34 146.8 44 46 74.0 33 32 68.5 57 61 691.0 34 90 59.6 57 64 121.0 90 40 371.9 64 59 836.9 90 35 335.4 64 91 69.9 33 36 58.6 60 69 68.7 90 36 333.1 58 63 63.0 36 37 69.3 59 63 836.9 37 38 207.4 61 60 67.4 38 56 86.7 60 62 67.1 38 39 74.8 62 63 67.1 12 35 71.0 60 65 330.6 35 40 69.3 65 68 711.2 40 39 70.5 69 91 59.0 39 41 70.6 69 66 292.1 41 42 70.6 66 65 152.3 42 56 68.8 66 67 742.0 32 82 71.2 67 68 686.5 82 57 464.8 91 70 867.8

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70 74 2043.6 24 25 40.4 74 73 65.1 24 25 40.4 70 27 180.3 16 17 67.9 32 37 69.8 16 17 61.5 25 88 284.2 52 51 64.7 23 89 63.8 53 52 66.5 18 13 67.3 55 52 88.4 22 20 69.6 53 55 301.1 86 20 68.2 54 53 88.2 89 22 195.0 55 24 93.5 23 51 68.3 47 54 66.4 20 18 70.0 47 54 58.1 51 22 59.8 46 47 64.8 51 22 64.1 45 46 64.8 51 22 64.1 51 45 67.6 12 11 60.9 72 55 68.2 12 11 60.9 68 72 416.6 13 12 64.6 73 68 68.9 17 18 50.8 26 73 734.5 17 18 64.6 27 72 320.1 19 87 65.6 27 24 66.6 21 88 284.2 27 24 55.9 19 17 60.3 26 27 71.9 21 19 60.3 28 26 89.2 19 17 59.3 25 24 40.4 21 19 57.2 25 28 72.2 24 21 66.5 16 25 71.7 24 21 0.0 83 1 67.5 25 23 68.3 85 5 67.5

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83 16 71.1 56 38 138.4 85 83 72.1 39 38 65.2 84 85 62.4 35 12 101.2 29 84 215.0 40 35 82.6 84 77 94.4 39 40 70.4 78 77 68.2 41 39 59.4 79 78 234.0 42 41 65.5 80 79 66.5 56 42 107.9 81 80 55.0 82 32 460.8 76 79 74.7 57 82 631.8 29 76 74.7 57 56 694.5 80 75 60.6 43 41 61.1 30 75 69.3 40 43 64.4 30 29 82.9 44 41 58.8 31 30 180.5 50 44 56.4 32 31 107.3 49 42 56.6 7 6 0.0 48 49 56.6 34 31 75.0 50 42 56.6 34 4 187.1 54 50 56.6 34 11 179.6 44 43 60.7 34 33 65.6 46 44 62.8 32 33 71.9 61 57 524.9 90 34 338.9 64 57 830.6 40 90 61.1 59 64 62.0 35 90 48.8 91 64 825.7 36 33 75.2 69 60 147.4 36 90 65.6 63 58 227.6 37 36 65.3 63 59 62.0 38 37 67.8 60 61 716.6

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Analysis of the results of appling the proposed program on Tanta city (target year 2030), do-nothing, indicates that the mean noise level will exceeds the residential area limit (65 dB(A)) for about 69.1% of the road links representing 217 links. 22.6% (about 70 links) of the road links will have mean noise level more than 100 dB(A), this limit represents annoying hearing. 46.5% of the road links will have mean noise level between 65 and 100 dB(A). The rest of the road links (30.9%) will produce mean noise levels less than 65 dB(A). Fig (6-30) shows the percentage of the range of mean noise level on the road links of study area (year 2030).

46.5%

22.6%

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0<Noise Level≤65 db(A) 65<Noise Level≤100 db(A) Noise Level>100 db(A)

Fig (6-30): Mean Noise Level Percentage of Road Network Links for Year 2030 – Do-nothing Scenario.

To control the mean noise level in the road network of the study area, the following countermeasures have been suggested:

• Installing noise insulation materials on face of residential buildings and schools existing directly on roadways corridors.

• Building acoustic barriers on side of links with high mean noise levels.

• Using quiet pavement in paving road surfaces besides, road surfacing relief and road evenness.

• Traffic management strategies like night time speed limitation, quiet areas.

Chapter 7

Measures to Improve Transportation System in StudyArea

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7.1. Introduction Analysis of the current situation of the transport system in the study area namely –Tanta City, has concluded that different main roads reaches its capacity in peak hours such as Seket Elmahalla, Botrus, and Eltareeq Alzeraae. In future, the situation will move difficult, since about 25% of the road network links will have the level of service (F). To improve the transport system situation in study area, different scenarios has been suggested namely:

• (Do-Nothing) scenario. • Light Rail Transit (LRT) scenario. • Public Transport scenario.

The aim of this chapter is to analyze, evaluate and compare between these suggested scenarios to improve the transport system in Tanta City, and to determine the optimum scenario. 7.2 (Do-Nothing) Scenario This scenario supposes that no improvement for the transport system of the study area will be done (Do-Nothing); no measures will be performed in the next 20 years. For this scenario, transportation planning is made for 5 modes of transport, namely:

• Public bus (Covers 33% of the transport demand of the city). • Collection taxi (Microbus) (Covers 27.2% of the transport demand of

the city). • Private car (Covers 13.2% of the transport demand of the city). • Taxi (Covers 21.2% of the transport demand of the city). • Motorcycle (Covers 5.4% of the transport demand of the city).

This scenario has been fully studied in chapter (6) of this thesis, and scenario results have led to the following facts in year 2030:

• 41.1% of the number of road network links will have (V/C) ratio exceeds 1.00. Only about 27% of the road network links will have

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(V/C) ratio between 0 and 0.2. Average volume to capacity ratio on the road network is 0.84.

• About 41.1% of the links will be in level of service (F), 58.9% of the links will be in level of service (A) and (B).

• 45.9% of the total number of the road network will have a percentage delay time exceed 100%.

• Production of 1892669.7 KgCo2 of daily greenhouse gas emissions. • 69.1% of the total number of Tanta city road network links will have

mean noise level exceeds the permissible for residential areas.

7.3 Light Rail Transit (LRT) Scenario This scenario involves introducing new transport mode in the transportation system of the city by interpolating the Light Rail Transit (LRT) system into the transportation system of the study area to cover 65% of Tanta city travel demand in year 2030. The LRT is an electric and uninterrupted transport system that has the ability to moderate in mixed traffic or on exclusive right-of-way with an isolated way in 40% to 90% of the tramway length, especially in crowded traffic zones in the city. The LRT consists of 1 to 3 vehicle/transport unit each with occupancy of 250 passengers. LRT speed ranges from 18-40 km/hr, it has the ability to accelerate and de-accelerate with a rate of 3 m/sec2. LRT transport system has a transport volume of 18000 (passenger/hr/direction). An advantage of the LRT Is that its infrastructure can be upgraded to be used as a Regional Rail Transit (RRT) transport system. [33]. One of the most important elements of the LRT network system is the transit stations. Transit stations is the place where the train stop to drop off or take passengers, Also, the station plays the role of the place where the passenger can buy tickets, change the train and take another one to another direction. Plat forms in the LRT stations can be lateral platforms or central plat forms. For the study area, the proposed LRT scenario in year 2030 will take a path consist of a length of 9.4 (km /one direction), and 7 LRT stations. The working transport units consist of 2 vehicles per each. The proposed layout of the LRT path and stations locations with respect to the study area road network links are shown in Fig (7-1).

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Fig (7-1): Layout of Proposed LRT Path in LRT Scenario.

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For this scenario, transportation planning is made for 6 modes of transport, namely:

• Public bus (Covers 11.55% of the transport demand of the city). • Collection taxi (Microbus) (Covers 9.52% of the transport demand of

the city). • Private car (Covers 4.62% of the transport demand of the city). • Taxi (Covers 7.42% of the transport demand of the city). • Motorcycle (Covers 1.89% of the transport demand of the city). • Light Rail Transit (LRT) (Covers 65% of the transport demand of

the city). Fig (7-2) shows the proposed modal split for LRT scenario.

11.6%7.4%

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Taxi

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Fig (7-2): The Proposed Modal Split in the LRT Scenario.

The implementation of Light Rail Transit scenario makes significant changes in (V/C) ratios and level of service on the road network links. Results show that only 22 links representing 7% of the total road network links of the study area will have (V/C) ratios exceeds 1.00 and. The average (V/C) ratio on the road network links is 0.2. (V/C) ratios of road links of the study area road network in case of LRT scenario are shown in appendix (D). With concern to time delays, results indicated that only 10.8 % of the links will have delays more than 100%. 267 links representing 85% of the total road network links of the study area will have time delays between 0.0 and 20%. The comparison between time delay categories of the road network

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links between the Do-nothing scenario and LRT scenario shows a reduction of 76.5% in links of time delay percentage more than 100% in LRT scenario than in Do-nothing scenario. In addition, the comparison indicates an increase of 84% in links of time delay percentage (0%-20%) in LRT scenario than in Do-nothing scenario. Percent time delay of road network links in case of LRT scenario is shown in appendix (D). Table (7-1) shows a comparison between percentage of road network links in different time delay categories in Do-nothing scenario and LRT scenario.

Table (7-1): Comparison between Time Delay Percentage of Study Area Road Network Links for Do-nothing Scenario and LRT Scenario.

Time Delay Category

Do-nothing Scenario

LRT scenario

% Change (With

respect to do-nothing scenario)

0%-20% 46.2 85 +84 20%-40% 4.1 1.3 -68.2 40%-60% 2.6 1.3 -50 60%-80% 0.6 1.6 +166.7 80%-100% 0.6 — -100

>100% 45.9 10.8 -76.5 The analysis indicates that in case of interpolating LRT in the transportation system of the city in year 2030, 75 links of the representing 23.9% of the study area road network links will have level of service (A), while only 22 links representing 7% of total links will carry level of service of grade (F). The other proportion of the road network links that represents 69.1% of the whole road network will be in level of service (B). Traffic volumes and level of service on each link of the study area road network in case of LRT scenario are shown in appendix (D). A comparison between level of service categories of the road network links between the Do-nothing scenario and LRT scenario shows a reduction of 83% in links of LOS (F) in the LRT scenario than in Do-nothing scenario. Furthermore, the comparison indicates an increase of about 342% in links of LOS (A) in the LRT scenario than in the Do-nothing scenario. No changes in links of LOS (C), (D) and (E) between the two scenarios. Table (7-2) and Fig (7-3) show a comparison between percentage of road network links in different LOS in Do-nothing scenario and LRT scenario.

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Table (7-2): Comparison between Level of Service Percentage of Study Area Road Network Links for Do-nothing Scenario and LRT Scenario.

(LOS) Do-nothing Scenario

LRT scenario

% Change (With


A 5.4 23.9 +342.6 B 53.5 69.1 +29.2 C — — — D — — — E — — — F 41.1 7 -83

Fig (7-3): Comparison Between LOS Percentage of Road Network for Do-nothing Scenario and LRT Scenario.

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Regarding to noise levels on the road network, the LRT scenario produces major changes in noise levels on the study area road network links. Results shows that 102 links, representing 32.5% of the total number of road network links on which mean noise levels will exceed 65 dB(A), which is the noise limits for residential areas. While the rest of the road network links, which represents 67.5% of the road network links, will have mean noise levels less than 65 dB(A). Mean noise levels on the study area road network links in case of LRT scenario are shown in appendix (D). A comparison between the mean noise levels categories of on the road network links between the Do nothing scenario and LRT scenario shows that the percentage of links with mean noise levels more than 65 dB(A) decreased from 69.1% in Do-nothing scenario to 32.5% in LRT scenario representing a reduction percentage of 53%. Also, the comparison shows an increase of 118.4% in links of mean noise levels less than 65 dB(A) in the LRT scenario of those in the Do-nothing scenario. Table (7-3) and Fig (7-4) show a comparison between percentage of noise level produced from Do-nothing scenario and LRT scenario for target year 2030. Table (7-3): Comparison Between Percentage of Noise Level (%) Produced

from Do-nothing Scenario and LRT Scenario.

Noise Level Do-nothing Scenario

LRT scenario

% Change (With


0<Noise Level<65 db(A)

30.9 67.5 +118.4


46.5 30.3 -34.8

Noise Level>100 db(A) 22.6 2.2 -90.3

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Fig (7-4): Comparison Between Percentages of Noise Level Produced from Do-nothing Scenario and LRT Scenario.

On the other hand, the implementation of LRT scenario also changes the transportation system gas emissions, as this scenario changes the modal split ratio in the transportation system of the city and because LRT system works with electricity and have little gas emissions in comparison with other modes. Also, the transport productivity (Passenger.Km) of different modes in the study area has significant changes as traffic volumes on the road network links changed The LRT daily transport productivity can be calculated using the following formula:

LQTV LRT *= Where: TVLRT : LRT yearly transport productivity, in (pass.km/day). Q : Traffic volume of LRT (Trip/day). L : Average trip length of the passenger (80% of LRT path (km)).

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Results show that LRT system produces about 0.2% of total transport system emissions in year 2030. With concern to whole transport system, results show that LRT scenario introduced a reduction of 64.9% in total emission in comparison with total emission produced by the transportation system in Do-nothing scenario. Table (7-4) shows the total Co2 and Co2 equivalent emission produced from transportation systems in LRT scenario for target year 2030. Fig (7-5) shows a comparison between total co2 equivalent emission produced from of different transportation systems in LRT scenario and Do-nothing scenario.

Table (7-4): Total Co2 and Co2 Equivalent Emission Produced in LRT Scenario for Target Year 2030 (kg/day).

Transport Mode

Co2 Emissions

(kgCO2)

Co2 Equivalent Emission from CH4

(kgCo2)


(kgCo2)

Total Mode

Emissions (kgCo2)

Percentage of Total

Transport System

Emissions

Private Cars 163243.5 2523.8 13992.3 179759.6 27.1 Taxi 158896.2 2456.6 13619.7 174972.5 26.4 Bus 113296.4 134.5 3715.4 117146.3 17.6

Microbus 107596.7 127.8 3528.5 111253 16.8 Motorcycle 76696.9 91.1 2515.2 79303.2 11.9

LRT 1238.6 29.6 181.3 1449.5 0.2 Total

Emissions 663884.1 (kg co2 /day)

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Fig (7-5): Comparison Between Co2 Equivalent Emissions Produced from Transport Systems in Do-nothing Scenario and LRT Scenario for Target

Year 2030. 7.4 Pubic Transport Scenario This scenario supposes a new modal split distribution among the modes as follows:

• Public bus – natural gas powered (Covers 60 % of the transport demand of the city).

• Private car (Covers 13.3% of the transport demand of the city). • Taxi (Covers 21.2% of the transport demand of the city). • Motorcycle (Covers 5.5% of the transport demand of the city).

Public Transport is represented in this scenario as public buses. The scenario proposes the elimination of collection taxies from the transport system in the study area, and replacing its demand for the favorite of public bus (27.2%) This means eliminating the Collection taxi (Microbus) from the transport system of the study area and providing a powerful public transport system

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for the city with a total travel demand of about 60% of the whole travel demand. Fig (7-6) shows the intended modal split for Public Transport scenario

13.3%5.5%

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PrivateCar

Taxi

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Motorcycle

Fig (7-6): Modal Split for Public Transport Scenario.

Public transport scenario introduces changes in (V/C) ratios on the road network links in year 2030. Results show that only 16.2% of the total road network links of the study area will have (V/C) ratios exceeds 1.00, this represents a reduction of 60.6% with comparison to the percentage of road network links having (V/C) ratios exceeds 1.00 in Do-nothing scenario. The average (V/C) ratio on the road network is 0.36. (V/C) ratios of road links of the study area road network in case of LRT scenario are shown in appendix (E). With concern to time delays, only 16.5% of the links will have time delays more than 100%, this represents a reduction of 64.1% with comparison to the percentage of road links with delays more than 100% in Do-nothing scenario. 226 links representing 72% of the total number of the road network links will have will have a percentage time delays between 0% and 20%, this represents an increase of 55.8% with comparison to Do-nothing scenario. Percent time delay of road network links in case of Public transport scenario is shown in appendix (E). Table (7-5) shows a comparison between percentage of road network links in different time delay categories in Do-nothing scenario and LRT scenario.

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Table (7-5): Comparison between Time Delay Percentage of Study Area Road Network Links for Do-nothing Scenario and Public Transport

Scenario.

Time Delay Category

Do-nothing Scenario

Public Transport scenario

% Change (With


0%-20% 46.2 72 +55.8 20%-40% 4.1 4.1 0.0 40%-60% 2.6 3.8 +46.1 60%-80% 0.6 2.6 +333.3 80%-100% 0.6 1 +66.7

>100% 45.9 16.5 -46 The application of the proposed program for the public transport scenario in the study area in year 2030 shows changes in the level of service of the road network, 31 links representing 9.9% of the total number of the road network links will have level of service (A), while about 16% of the road network will be on LOS (F). 74.2% representing 233 links will be in good operational situation with level of service (B). Traffic volumes (V/C) ratio and level of service on each link of the study area road network in case of Public Transport scenario are shown in appendix (E). The comparison of the level of service categories shows a reduction of 61.3% in the number of links in LOS (F) in Public Transport scenario with comparison to number of links in LOS (F) in Do-nothing scenario. Also, the comparison indicates an increase of about 83% in the number of links in LOS (A) in the Public Transport scenario with comparison to the Do-nothing scenario. Links in LOS (B) increased by a percentage of 38.7%. No changes in links of LOS (C), (D) and (E) between the two scenarios. Table (7-6) and Fig (7-7) show a comparison between LOS percentage of road network for Do-nothing scenario and Public Transport scenario.

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Table (7-6): Comparison Between Level of Service Percentage of Road Network Links for Do-nothing Scenario and Public Transport Scenario.

(LOS) Do-nothing Scenario


% Change (With


A 5.4 9.9 +83.3 B 53.5 74.2 +38.7 C — — — D — — — E — — — F 41.1 15.9 -61.3

Fig (7-7): Comparison between LOS Percentage of Road Network for Do-nothing Scenario and Public Transport Scenario.

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With regard to the mean noise levels produced from transport systems on the road network links, results show that links with mean noise levels more than 65 dB(A) in Public transport scenario represents 44.9% of the total road network links. This represents a reduction of 35% with comparison to the number of links with mean noise levels more than 65 dB(A) in Do-nothing scenario. Also, results indicate an increase of 78.3% in the number of links of mean noise levels less than 65 dB(A) in the Public transport scenario with comparison to Do-nothing scenario. Mean noise levels of road network links in case of Public transport scenario are shown in appendix (E). Table (7-7) and Fig (7-8) show a comparison between percentage of noise level produced from do-nothing and public transport scenarios. Table (7-7): Comparison Between Percentage of Noise Level (%) Produced

from Do-nothing Scenario and Public Transport Scenario.

Noise Level Do-nothing Scenario


% Change (With



30.9 55.1 +78.3


46.5 38.5 -17.2

Noise Level>100 db(A)

22.6 6.4 -71.7

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Fig (7-8): Comparison Between Percentage of Noise Level Produced from Do-nothing Scenario and Public Transport Scenario.

With concern to emissions, the proposed Public transport scenario includes introducing Public transport system consists of natural gas powered buses. Besides no emissions are produced from Microbus as this transport mode is eliminated from the city transport system. In comparison to Do-nothing scenario, the transport productivity of private cars increased by 0.76%, transport productivity of taxies almost has no changes. Also, the transport productivity of motorcycles increased by 1.85%, while the transport productivity of public bus increased by 81.82%. The analysis of the results of the emission produced from Public transport scenario shows that Co2 equivalent emission produced from buses in this scenario is less than that produced by buses in Do-nothing scenario by about 40% with respect to Do-nothing scenario. Also, results indicate that total emissions produced from the transport systems in Public transport scenario will have a reduction of 23.3% with comparison to the transport system emissions in Do-nothing scenario. Table (7-8) shows the total Co2 and Co2 equivalent emission produced from transport systems in public transport scenario for year 2030. Fig (7-9) shows a comparison between total co2 equivalent emission produced from Public Transport and Do-nothing scenarios.

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Table (7-8): Total CO2 and CO2 Equivalent Emission of Transportation Systems in Public Transport Scenario for Target Year 2030 (kg/day).

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Co2 Emissions

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Co2 Equivalent Emission from CH4

(kgCo2)


(kgCo2)

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Emissions (kgCo2)

Percentage of Total

Transport System

Emissions

Private Cars 469944 7265.6 40280.9 517490.5 35.6 Taxi 453990.1 7018.8 38913.5 499922.4 34.4 Bus 190692 11217.2 1835.5 203744.7 14.1

Motorcycle 223191.1 265 7319.3 230775.4 15.9 Total

Emissions 1451933 (kg co2 /day)

Fig (7-9): Comparison Between Co2 Equivalent Emissions Produced from Transport Systems in Do-nothing Scenario and Public Transport Scenario

for Target Year 2030.

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7.5 The Optimal Scenario Determining the optimal scenario is the target of the transportation

planning process. Optimum scenario can be determined on the bases of the

operational and environmental assessment of different scenarios. For the

study area, in the target year 2030, a comparison between Light rail transit

(LRT) scenario and Public transport scenario leads to the following facts:

• Number of links having (V/C) ratios exceeds 1.00 in LRT scenario is

56.8% less than that in Public transport scenario.

• Number of links having time delays more than 100% in LRT

scenario is 34.5% less than that in Public transport scenario.

• Number of links having LOS (F) in LRT scenario is 56.3% less than

that in Public transport scenario.

• Number of links having LOS (A) in LRT scenario is 141.4% more

than that in Public transport scenario.

• Number of links having mean noise level exceeds 65 dB(A) in LRT

scenario is 27.6% less than that in Public transport scenario.

• Emission production in LRT scenario is 54.3% less than emission

productions in Public transport scenario.

These facts lead to the conclusion that, in year 2030, (LRT) scenario is the

study area optimal scenario. Fig (7-10) shows a comparison between LRT

scenario and Public Transport scenarios.

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Fig (7-10): Comparison between LRT Scenario and Public Transport Scenario for Target Year 2030.

Chapter 8

Summary, Conclusions and Recommendations

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8.1 Summary The subject of transport planning in urban area is to understand of traffic problems, formulating safe and sustainable efficient solution and managing the transportation system to provide an adequate system and achieving long term, medium and short term solutions to the over all system of traffic and transport planning. These require applying scientific methods of transportation planning and traffic engineering supported with a big amount of socio-economic data and data about transportation systems and its interrelationships. The computer technique plays a big role in this issue. In developing countries, the gathering of such socio-economic data is more difficult than in developed countries. Using inadequate data in transportation planning software (as VISSIM) may lead to incorrect results; in this case, the planner must develop his own transportation model, which describe the required transportation system. The main objective of this research is to define the urban planning process in developing areas. A computer program (UTPP-TC: Urban Transportation Planning Program for Tanta City) has been built using MATLAB programming process. The program uses the four steps transportation models; trip generation and attraction, trip distribution, modal split, and trip assignment. The program can also evaluate the operational and the environmental situation of the urban road network in the study area. The program has been applied in Tanta City (Egypt). Analysis of the results of the trip assignment, operational and environmental evaluation of the road network in the study area for the target year 2030 indicates that: some road links must have a short term measures (urgent measures) such links connecting zone (4) and (3). About 41% of the road links will have a level of service (F), and about 1892670 daily KgCo2 and Co2 equivalent emission will be produced. To improve the urban transport system in the study area, different scenarios has been investigated, namely Do-Nothing, LRT, and Public Transport scenario. The result indicated that the LRT scenario is the optimal solution to solve traffic congestion and problems in the study area, since it leads to significant improvement in the level of service of the road network, as well as it reduces the pollution produced from the transportation sector.

219

8.2 Conclusions and Recommendations The urban transportation planning is a part of the overall urban planning of a region. It aims to build bases and roles to ensure that the transport system is keeping with the continuous urban development and meets with the transportation demand of the region. It is identified as conditional prediction of travel demand in order to estimate the likely transportation consequences of several transportation scenarios. Many computer software specialized in the transportation planning process, such as EMME/2 (Equilibrium Multimodal-Multimodal Equilibrium), QRSII (Quick Response System), TRANPLAN (TRANsport PLANning), HCS (Highway Capacity Software), VISUM (Verkehr In Städten – Umlegung) and VISSIM (Verkehr In Städten – SIMulationsmodel), and TransCAD (TRANSport Computer Aided Design). Using such software needs huge amount of accurate data. In developing countries, there is a lack of such adequately current or relevant demographic and socio-economic data and information required for the transportation planning process. In this case, a new and specific computer program must be built. A proposed computer program has been built for this purpose in this research. This program has been applied on Tanta City (Egypt). Applying the proposed program in developing area such as Tanta City indicates the following facts:

•••• The current number of population in Tanta City is about 420 thousand inhabitants. With an annual growth factor of 1.2%, the number of population will reach in the target year 2030 about 548 thousand inhabitants.

•••• The current transportation demand in Tanta city reaches about 153429 trip/day. In the target year this value will reach about 706478 trip/day.

•••• The current modal split depends mainly on the public transport (only bus system) with 33% and collective taxi (microbus) with 27.2% and taxi with 5.4%. Since there is no urban rail transport in the study area, the modal split is mainly depending on road transport system. This leads to main congestion problems on the road network.

•••• To improve the urban transportation system in study area, three scenarios have been studied, namely, Do-Nothing scenario, LRT

220

scenario and Public Transport scenario. The do-Nothing scenario indicates the following facts for the target year 2030:

o The traffic suffers from congestion and high volume to capacity

ratios; about 41.1% of the road network links will have a (V/C) ratio > 1.00.

o The traffic suffers from a substantial delay. About 45.9% of road links will have delays > 100%. The whole road network links will have an average delay >100%.

o The city will suffer from the traffic noise, 69.1% of the total number road links in Tanta City will have mean noise level exceeds the permissible for residential areas.

o The city will suffer from the pollution produced from the current transportation system. About 1892669.7 daily KgCo2 and Co2 equivalent emission will be produced in target year 2030 in Do-Nothing scenario.

•••• The Do-Nothing scenario determined critical road links for the target

year 2030. These links need a short, medium, or long-term measure. These are:

o Road links (88-89), (21-88), (25-88), (29-88), and (87-88) and

(22-89). These links contact zone (3) and zone (4) in the eastern side of Tanta city, since it has high population, educational places and commercial zones. Relocation of such trip attractive factors can be a good solution for the traffic problems in this region.

o Road links (68-67), (60-65) and (65-68), which lie on zone (12), (13) and (14) on the western entrance of the city, since it contains industrial area of the city. Constructing a new arterial between these zones can be medium term measure for this region.

o Road links (9-15), (15-87), (87-88), (14-86), (86-89) and (89-23) have a conflict between pedestrian and traffic. These areas can be designed as a traffic calming area, with a maximum speed of 30km/hr and pedestrian facilities. It can be planned also as time- limited pedestrian areas.

•••• To avoid the current and future traffic problems, and to improve the

urban transport systems in the study area, it is proposed to create a

221

LRT system, as a long-term measure. This system will lead to the following facts in target year 2030: o The LOS on the road network will be improved; LOS (A) will

increase from 5.4% to 23.9% of the whole network links. LOS (F) will decrease from 41.1% to 7% of the whole road network links.

o The number of road links which have delays > 100% will decrease from 45.9% to 10.8 % of the whole road network links.

o A reduction of amount of pollution (Co2 and Co2 equivalent emissions) of about 64.9% will be achieved through this scenario.

o A reduction of 53% of the number of links, which have a mean noise levels that exceeds the permissible level for residential areas, will be achieved through this scenario.

•••• The Public Transport Scenario, which must be gas powered, will

achieve the following benefits for the transport system:

o The LOS on road network will be improved; LOS (A) will increase from 5.4% to 9.9% of the whole network links. LOS (F) will decrease from 41.1% to 15.9% of the whole road network links.

o The number of road links which have delays > 100% will decrease from 45.9% to 16.5% of the whole road network links.

o A reduction of pollution (Co2 and Co2 equivalent emissions) of about 23.3% will be achieved through this scenario.

o A reduction of 35% of the number of links, which have a mean noise levels that exceeds the permissible level for residential areas, will be achieved through this scenario.

•••• The LRT scenario is the most acceptable scenario to solve traffic

congestion and problems in the study area. Concerning the difference between the software used in transportation planning process (EMME/2, QRSI, TRANPLAN, HCS, VISUM and VISSIM, and TransCAD), it can be concluded that:

•••• Each program has strength and weakness, and neither is ideal for every situation. The transportation planner should carefully consider two criteria when making a decision as which program to use for a

222

particular study. These criteria are the project objectives as well as the program characteristics.

•••• EMME/2 support only ASCII text files, shape files and dBase files for importing data and didn't support other modeling software files.

•••• TransCAD, VISUM and TRANPLAN support importing data from many various modeling software.

•••• All transportation planning software require inputting detailed data to complete the transportation planning analysis.

•••• EMME/2 and TRANPLAN support just two trip generation models (regression and cross classification), TransCAD and VISUM support further trip generation models as trip rate daily activity schedules and time of day generation methods.

•••• EMME/2 and TRANPLAN include the gravity model or the FRATAR method as trip distribution models, while TransCAD support further Trip distripution models as destination choice (aggregate and disaggregate), tri-proportional. VISUM support all pre-mentioned distribution models besides trip chain building model.

•••• In the modal split stage, all models allow both the logit and nested logit methods, but VISUM have the ability to specific visual basic scripts using VISUM’s objects and methods can also be used to develop logit models. EMME/2 has the ability of using any other demand function.

•••• TRANPLAN supports All or nothing, Capacity restrain, Incremental trip assignment model. Other software support more trip assignment models.

•••• Except EMME/2, all software support GIS integration, EMME/2 has Enif as an alternative interface to access EMME/2 data banks, shape files and dBase files.

•••• VISUM and TransCAD are compatible with all land use models and can be linked to them through GIS files. EMME/2 Interfaces with land use methods with sub-programs (MEPLAN, EMPAL/DRAM), while TRANPLAN is compatible with some land use models.

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223

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APPENDICES

Appendix (A)

Socio-economic Data of Main Transportation Zones

228

Main Transportation Zones in Study Area

229

Socio-economic Data of Main Transportation Zones of Study Area in Year2000.

ZonePopulation

Number(in 1000)

Number ofEducates(in 1000)

Number ofEmployees

(in 1000)

Numberof Private

Cars

Zone 1 210.144 138.821 68.086 14942

Zone 2 77.929 41.531 28.673 5541

Zone 3 114.242 70.116 45.582 8122

Zone Area (Km2)Area of Stations

(m2)No of Educational

Places

Zone 1 6.894 42000 129

Zone 2 1.843 - 20

Zone 3 3.113 28000 65

Zone

Number ofPopulation

NumberhavingAnnualIncome

<6000 L.E(in 1000)

Number ofPopulation

NumberhavingAnnualIncome6000 ~

10000 L.E(in 1000)

Number ofPopulation


30000 L.E(in 1000)

Number ofPopulation


>30000 L.E(in 1000)

Zone 1 12.398 43.710 131.760 22.276

Zone 2 4.598 16.209 48.861 8.261

Zone 3 6.739 23.759 71.618 12.108

Appendix (B)

Socio-economic Data and Travel Demand of Sub -zones

230

Transportation Sub-zones in Study Area

231

Ratio of Sub-Zone Population to Transportation Zones Population (z

sz

P

P) for

Year 2000.

TransportationZone

Sub-ZoneName

Sub-ZoneCode

PopulationNumber of

TransportationSub-Zone in

Year 2000 (in1000) (Psz)

PopulationNumber of

TransportationZone in Year2000 (in 1000)

(Pz)

z

sz

P

P

Quhafa 1 32.561 0.15

WaboorElnoor

2 50.754 0.24

Ali Agha 3 17.909 0.09

Almalga 4 44.407 0.21

Midan Elsaa 5 9.170 0.04

Eldawaween 6 27.022 0.13

Elborsa 7 17.020 0.08

ElkafrElsharkya

8 7.795 0.04

Zon

e(1

)

Sabri 9 3.506

210.144

0.02

Elsalakhana 10 34.630 0.44

Zon

e(2

)

Elemari 11 43.29977.929

0.56

Kafr Segar 12 39.928 0.35

KobriElmahata

13 67.475 0.59

Zon

e(3

)

Sedi Mrzoq 14 6.839

114.224

0.06

232

Socio-economic Data of the Transportation Sub -zones of Study Area inYear 2000.

Sub-ZoneCode

PopulationNumber(in 1000)

Number ofEducates(in 1000)

Number ofEmployees

(in 1000)

Numberof Private

Cars1 32.561 19.862 10.550 2241.32 50.754 35.650 16.444 3586.083 17.909 11.091 5.803 1344.784 44.407 31.465 14.388 3137.825 9.170 5.981 2.971 597.686 27.022 18.456 8.755 1942.467 17.020 10.017 5.514 1195.368 7.795 4.293 2.526 597.689 3.506 2.007 1.136 298.84

10 34.630 18.523 12.744 2438.0411 43.299 23.008 15.934 3102.9612 39.928 23.953 15.927 2842.713 67.475 42.013 26.916 4791.9814 6.839 4.149 2.728 487.32

Sub-ZoneCode Area (Km2)

Area of Stations(m2)

No of EducationalPlaces

1 0.49 14000 182 0.63 14000 213 0.63 14000 164 1.05 - 195 0.82 - 76 1.1 - 127 0.76 - 88 1.42 - 149 0.64 - 14

10 0.81 - 911 1.04 - 1112 1.03 14000 2313 1.25 14000 3614 0.83 - 6

233

Sub-ZoneCode

Number ofPopulation


<6000 L.E(in 1000)

Number ofPopulation


10000 L.E(in 1000)

Number ofPopulation


30000 L.E(in 1000)

Number ofPopulation


>30000 L.E(in 1000)

1 1.92 6.77 20.42 3.452 2.99 10.56 31.82 5.383 1.06 3.73 11.23 1.904 2.62 9.24 27.84 4.715 0.54 1.91 5.75 0.976 1.59 5.62 16.94 2.867 1.00 3.54 10.67 1.808 0.46 1.62 4.89 0.839 0.21 0.73 2.20 0.37

10 2.04 7.20 21.71 3.6711 2.55 9.01 27.15 4.5912 2.36 8.31 25.03 4.2313 3.98 14.03 42.31 7.1514 0.40 1.42 4.29 0.72

234

Total Trip Production (Qi) and Trip Attraction (Zj) of Transportation Sub-Zones of The Study Area in Year 2000 (Trip/day) [[18], private calculations].

Sub-ZoneCode

Trip Production(Qi) (Trip/day)

Trip Attraction(Zj) (Trip/day)

1 13933 76172 22292 121863 8360 45704 19506 106635 3715 20316 12075 66017 7431 40628 3715 20319 1858 1016

10 11864 1381711 15099 1758512 11754 2493813 19813 4203814 2015 4275

The main transportation zones; zone (1), zone (2) zone (3) were dividedinto 14 transportation sub-zones. The trip generation and trip attraction ofthe sub-zones were generated from the trip generation and trip attraction ofthe main zones depending on th e ratio of sub-zone population to

transportation zones population (z

sz

P

P) in the base year 2000.

235

Origin Destination Matrix of Tanta City Transportation Sub -Zones for BaseYear 2000 (Trip /day).

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 3168 1530 587 3474 144 311 234 279 644 891 625 718 1192 135

2 1019 5399 888 4050 680 2031 515 801 424 941 827 2325 2042 349

3 264 600 513 2187 233 180 576 43 48 1306 675 499 1112 124

4 1978 3464 2767 3536 405 526 533 247 513 1496 956 1134 1728 223

5 68 483 244 336 106 291 309 22 16 197 222 631 651 139

6 198 1936 254 586 391 1510 271 126 52 349 413 4239 1412 338

7 98 324 537 393 274 180 378 24 18 716 954 775 2586 174

8 293 1264 101 457 49 209 60 237 114 139 129 306 313 45

9 375 371 62 525 19 48 24 63 33 73 54 83 112 15

10 225 357 733 664 105 139 431 33 31 2633 3562 511 2291 148

11 121 241 291 326 91 126 441 24 18 2736 3276 597 6591 221

12 106 515 163 294 197 985 272 43 21 298 454 5007 2363 1037

13 127 327 264 324 147 237 658 32 21 968 3627 1711 10301 1069

14 12 47 25 35 26 48 37 4 2 53 102 632 901 90

The (O/D) matrix of the fourteen sub -zones is generated by distributing thetrip production (Qi) and trip attraction (Zj) of the sub-zones using the 3 rd

stage of the proposed program.

Appendix (C)

Application of the Program on Tanta City as Case Study

236

Program main menu

237

1st stage of The Proposed Program - Forecasting of Socio-economicData

238

2nd stage of The Proposed Program - Forecasting of Future TripProduced and Attracted

239

3rd stage of The Proposed Program - Trip Distribution

240

Impedance Between Transportation Zones of Tanta City .

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1 1.82 1.8 1.13 2.42 2.97 2.69 1.74 0.81 2.54 3.42 3.8 3.83 3.63

2 1.82 1 1.51 1.08 1.15 1.2 1.87 1.06 1.03 2.55 3.07 2.18 3.02 2.33

3 1.8 1.51 1 0.74 0.99 2.03 0.89 2.3 1.54 1.09 1.71 2.37 2.06 1.97

4 1.13 1.08 0.74 1 1.29 2.04 1.59 1.65 0.81 1.75 2.47 2.7 2.84 2.52

5 2.42 1.15 0.99 1.29 1 1.09 0.83 2.2 1.84 1.92 2.04 1.44 1.84 1.27

6 2.97 1.2 2.03 2.04 1.09 1 1.85 1.92 2.11 3.01 3.12 1.16 2.61 1.7

7 2.69 1.87 0.89 1.59 0.83 1.85 1 2.82 2.32 1.34 1.31 1.73 1.23 1.51

8 1.74 1.06 2.3 1.65 2.2 1.92 2.82 1 1.02 3.41 3.98 3.08 3.96 3.33

9 0.81 1.03 1.54 0.81 1.84 2.11 2.32 1.02 1 2.48 3.23 3.12 3.48 3.08

10 2.54 2.55 1.09 1.75 1.92 3.01 1.34 3.41 2.48 1 0.97 3.05 1.87 2.35

11 3.42 3.07 1.71 2.47 2.04 3.12 1.31 3.98 3.23 0.97 1 2.79 1.09 1.9

12 3.8 2.18 2.37 2.7 1.44 1.16 1.73 3.08 3.12 3.05 2.79 1 1.89 0.91

13 3.83 3.02 2.06 2.84 1.84 2.61 1.23 3.96 3.48 1.87 1.09 1.89 1 0.99

14 3.63 2.33 1.97 2.52 1.27 1.7 1.51 3.33 3.08 2.35 1.9 0.91 0.99 1

241

4th stage of The Proposed Program – Modal Split

242

5th stage of The Proposed Program – Trip Assignment

243

6th stage of The Proposed Program – Operational Evaluation ofNetwork

244

Level of Service of Road Links in the Study Area for (Year 2030)Calculated by (HCM2000 -ATS) Method.

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

1 9 A 13 12 A9 1 A 10 11 F2 5 A 11 10 F5 2 A 14 13 A3 5 A 13 14 A5 3 A 10 9 D6 7 A 9 10 D7 6 A 14 15 F7 8 A 15 14 F8 7 A 15 16 F8 4 A 16 15 B4 8 F 86 87 F8 29 F 87 86 A

29 8 A 87 88 F4 3 F 88 87 A3 4 F 88 89 A2 9 A 89 88 F9 2 A 89 86 F3 10 C 86 89 A

10 3 A 14 86 B9 15 A 86 14 E

15 9 A 15 87 F10 14 C 87 15 A14 10 A 88 25 A4 11 A 89 23 F

11 4 B 13 18 F11 12 F 20 22 E12 11 A 20 86 B12 13 F 22 89 E

245

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

51 23 F 54 47 A18 20 C 54 47 A22 51 D 47 46 A22 51 A 46 45 F22 51 A 45 51 F11 12 A 55 72 F11 12 A 72 68 F12 13 A 68 73 F18 17 A 73 26 F18 17 A 72 27 B87 19 C 24 27 F88 21 A 24 27 A17 19 F 27 26 F19 21 F 26 28 A17 19 A 24 25 A19 21 A 28 25 A21 24 A 25 16 A21 24 A 1 83 F23 25 F 5 85 F25 24 D 16 83 A25 24 A 83 85 A17 16 B 85 84 F17 16 A 84 29 D51 52 F 77 84 B52 53 A 77 78 F52 55 F 78 79 A55 53 F 79 80 A53 54 B 80 81 F24 55 F 79 76 A

246

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

76 29 A 56 57 F75 80 A 41 43 A75 30 A 43 40 A29 30 A 41 44 D30 31 A 44 50 A31 32 A 42 49 F6 7 A 49 48 F

31 34 F 42 50 D4 34 A 50 54 F

11 34 A 43 44 A33 34 F 44 46 F33 32 D 57 61 F34 90 A 57 64 F90 40 F 64 59 F90 35 F 64 91 A33 36 A 60 69 A90 36 F 58 63 A36 37 C 59 63 F37 38 F 61 60 B38 56 F 60 62 C38 39 F 62 63 C12 35 A 60 65 F35 40 A 65 68 F40 39 A 69 91 A39 41 B 69 66 F41 42 A 66 65 F42 56 D 66 67 F32 82 C 67 68 F82 57 F 91 70 F

247

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

70 74 F 24 25 A74 73 A 24 25 A70 27 F 16 17 C32 37 F 16 17 A25 88 F 52 51 C23 89 B 53 52 E18 13 A 55 52 F22 20 A 53 55 F86 20 F 54 53 F89 22 F 55 24 F23 51 A 47 54 A20 18 A 47 54 A51 22 A 46 47 B51 22 A 45 46 C51 22 A 51 45 F12 11 A 72 55 A12 11 A 68 72 F13 12 A 73 68 B17 18 A 26 73 F17 18 A 27 72 F19 87 B 27 24 A21 88 F 27 24 A19 17 A 26 27 E21 19 A 28 26 F19 17 A 25 24 A21 19 A 25 28 A24 21 A 16 25 A24 21 A 83 1 F25 23 A 85 5 F

248

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

83 16 A 56 38 F85 83 B 39 38 A84 85 A 35 12 F29 84 F 40 35 F84 77 F 39 40 A78 77 D 41 39 A79 78 F 42 41 A80 79 A 56 42 F81 80 A 82 32 F76 79 F 57 82 F29 76 F 57 56 F80 75 A 43 41 A30 75 A 40 43 A30 29 F 44 41 A31 30 F 50 44 A32 31 F 49 42 A7 6 A 48 49 A

34 31 F 50 42 A34 4 F 54 50 A34 11 F 44 43 A34 33 A 46 44 A32 33 F 61 57 F90 34 F 64 57 F40 90 A 59 64 A35 90 A 91 64 F36 33 F 69 60 F36 90 A 63 58 F37 36 A 63 59 A38 37 E 60 61 F

249

LinkStartNode

EndNode

(LOS)

62 60 F63 62 F65 60 F68 65 F91 69 F66 69 A65 66 B67 66 F68 67 F70 91 A74 70 A73 74 F27 70 F37 32 F45 22 A22 45 A78 81 F81 78 F48 47 F47 48 A61 58 A58 61 F61 58 A58 61 A

250

7th stage of The Proposed Program – Air Pollution Assessment

251

8th stage of The Proposed Program – Noise Assessment

Appendix (D)

Light Rail Transit (LRT) Scenario

252

Trip Assignment on Road Network Links for Year 2030.

Link

StartNode

EndNode

TrafficVolume(pcu/hr)

Volume to CapacityRatio (V/C)

% Time Delay

1 9 0 0.00 09 1 119 0.09 0.0084800052 5 55 0.04 0.0004234295 2 4 0.00 1.94698E-093 5 187 0.14 0.0776365645 3 236 0.18 0.0397479796 7 0 0.00 07 6 0 0.00 07 8 0 0.00 08 7 0 0.00 08 4 0 0.00 04 8 880 0.25 0.4201964328 29 880 0.25 0.420196432

29 8 0 0.00 04 3 156 0.12 0.0319588273 4 236 0.18 0.0397479792 9 0 0.00 09 2 55 0.04 0.0004234293 10 15 0.01 2.50193E-07

10 3 31 0.02 1.11759E-059 15 0 0.00 0

15 9 55 0.04 0.00042342910 14 37 0.03 4.57332E-0514 10 0 0.00 04 11 0 0.00 0

11 4 384 0.11 0.01636590311 12 400 0.12 0.01033276212 11 534 0.15 0.10553951612 13 720 0.21 0.153631961

253

Link

StartNode

EndNode



% Time Delay

13 12 32 0.01 1.08246E-0710 11 0 0.00 011 10 187 0.39 4.10070829314 13 3 0.00 3.70636E-1013 14 3 0.00 8.05331E-1010 9 119 0.09 0.0084800059 10 0 0.00 0

14 15 33 0.03 8.36258E-0615 14 312 0.24 0.9822413715 16 0 0.00 016 15 0 0.00 086 87 522 1.10 323.18591887 86 42 0.09 0.00089644887 88 500 1.05 461.923439788 87 42 0.09 0.00089644888 89 40 0.08 0.00075299589 88 579 1.22 940.358902589 86 538 0.58 13.1596454586 89 23 0.02 1.85481E-0514 86 23 0.02 1.85481E-0586 14 16 0.02 1.3792E-0615 87 33 0.07 0.0004897287 15 55 0.12 0.02479916988 25 17 0.04 0.00013302389 23 444 0.93 365.856237813 18 717 0.21 0.15024395720 22 717 0.21 0.15024395720 86 42 0.09 0.00089644822 89 243 0.51 23.86915253

254

Link

StartNode

EndNode



% Time Delay

51 23 1120 0.32 1.200844918 20 717 0.21 0.15024395722 51 637 0.18 0.07221151922 51 57 0.02 1.11245E-0622 51 57 0.02 1.11245E-0611 12 0 0.00 011 12 0 0.00 012 13 32 0.01 1.08246E-0718 17 57 0.04 5.44777E-0518 17 57 0.04 5.44777E-0587 19 42 0.09 0.00089644888 21 40 0.08 0.00075299517 19 0 0.00 019 21 0 0.00 017 19 0 0.00 019 21 0 0.00 021 24 0 0.00 021 24 0 0.00 023 25 1432 0.41 4.49155164825 24 1 0.00 3.54714E-1325 24 1 0.00 3.54714E-1317 16 0 0.00 017 16 0 0.00 051 52 229 0.48 5.42055761352 53 26 0.05 0.00034715152 55 377 0.79 63.87757955 53 271 0.57 42.7276109853 54 296 0.23 0.97950893624 55 0 0.00 0

255

Link

StartNode

EndNode



% Time Delay

54 47 24 0.03 6.4914E-0654 47 24 0.03 6.4914E-0647 46 272 0.57 4.86029146846 45 614 1.29 555.954156645 51 580 1.22 162.785399755 72 451 0.34 2.22924457672 68 451 0.34 2.22924457668 73 1339 1.02 318.064730873 26 339 0.26 0.40727896972 27 36 0.03 8.55992E-0624 27 340 0.14 0.01240322624 27 0 0.00 027 26 340 0.14 0.01240322626 28 27 0.01 2.57434E-0724 25 1 0.00 1.02714E-0928 25 1 0.00 9.03277E-1325 16 0 0.00 01 83 119 0.25 0.4977242685 85 242 0.51 16.48570495

16 83 0 0.00 083 85 119 0.04 0.00043563785 84 2127 0.77 162.246585884 29 456 0.17 0.10297123877 84 170 0.13 0.00421594277 78 875 0.95 555.81622278 79 269 0.29 0.57642066479 80 730 0.31 1.84209830580 81 730 0.79 117.446670679 76 22 0.01 1.02394E-07

256

Link

StartNode

EndNode



% Time Delay

76 29 70 0.03 1.16998E-0575 80 58 0.04 5.85662E-0575 30 0 0.00 029 30 209 0.08 0.01011313730 31 209 0.09 0.01860144931 32 209 0.08 0.0101131376 7 0 0.00 0

31 34 17 0.02 1.60823E-064 34 0 0.00 0

11 34 0 0.00 033 34 580 0.44 1.89875357233 32 189 0.14 0.0548718734 90 40 0.04 0.00017548890 40 880 0.95 381.686647590 35 1253 0.95 319.091344833 36 46 0.04 2.32698E-0590 36 858 0.65 63.1081772936 37 566 0.43 9.48570036137 38 760 0.82 171.590678338 56 760 0.82 171.590678338 39 182 0.14 0.01781525112 35 32 0.01 5.00885E-0735 40 70 0.03 1.16977E-0540 39 113 0.05 0.00067505139 41 217 0.09 0.01117971841 42 217 0.09 0.01117971842 56 95 0.07 0.00302522332 82 398 0.14 0.07974208182 57 1255 0.86 329.6462815

257

Link

StartNode

EndNode



% Time Delay

56 57 855 0.65 57.2414891941 43 17 0.04 2.69349E-0543 40 70 0.15 0.00724966641 44 256 0.54 3.24392212244 50 15 0.03 4.22925E-0542 49 272 0.57 4.86029146849 48 272 0.57 4.86029146842 50 706 0.54 8.8215009650 54 721 0.55 27.220845443 44 102 0.21 0.56632894944 46 343 0.72 13.3924962657 61 972 1.05 609.434303357 64 1018 0.43 11.0200803764 59 1120 1.21 9506.02940264 91 31 0.01 4.85439E-0760 69 0 0.00 058 63 82 0.09 0.01210571359 63 1120 1.21 9506.02940261 60 252 0.27 0.88522275260 62 17 0.02 6.37928E-0662 63 17 0.02 6.37928E-0660 65 696 0.75 48.8235852865 68 915 0.99 1049.76889469 91 3 0.00 8.22517E-1069 66 1000 1.08 170.746977466 65 365 0.40 1.07728982266 67 781 1.65 2832.25159467 68 636 1.34 443.233439191 70 1944 1.48 17112.40531

258

Link

StartNode

EndNode



% Time Delay

70 74 2430 1.85 48043432.874 73 107 0.08 0.00066252970 27 97 0.11 0.00195163232 37 194 0.21 0.10196962125 88 547 1.15 502.630703323 89 132 0.28 1.02964572218 13 58 0.02 1.13295E-0622 20 57 0.02 1.11245E-0686 20 42 0.09 0.00089644889 22 129 0.27 0.1733385323 51 36 0.01 1.63202E-0720 18 57 0.02 1.11245E-0651 22 57 0.02 1.11245E-0651 22 57 0.02 1.11245E-0651 22 57 0.02 1.11245E-0612 11 0 0.00 012 11 0 0.00 013 12 32 0.01 1.08246E-0717 18 57 0.04 5.44777E-0517 18 57 0.04 5.44777E-0519 87 42 0.09 0.00089644821 88 40 0.08 0.00075299519 17 0 0.00 021 19 0 0.00 019 17 0 0.00 021 19 0 0.00 024 21 0 0.00 024 21 0 0.00 025 23 1 0.00 3.52563E-13

259

Link

StartNode

EndNode



% Time Delay

24 25 1 0.00 3.54714E-1324 25 1 0.00 3.54714E-1316 17 0 0.00 016 17 0 0.00 052 51 132 0.28 1.02964572253 52 147 0.31 0.16991665755 52 158 0.33 2.55180289553 55 513 1.08 63.1911213854 53 660 0.50 3.60961733555 24 340 0.72 7.87699474547 54 24 0.03 6.4914E-0647 54 24 0.03 6.4914E-0646 47 0 0.00 045 46 22 0.05 6.56647E-0551 45 90 0.19 0.01899511572 55 249 0.19 0.5889214768 72 1121 0.85 539.086190773 68 872 0.66 53.7102342126 73 1242 0.95 264.072106127 72 339 0.26 0.40727896927 24 0 0.00 027 24 0 0.00 026 27 339 0.14 0.03842008728 26 1266 0.53 16.9288904725 24 1 0.00 1.02714E-0925 28 902 0.33 1.66602669516 25 0 0.00 083 1 0 0.00 085 5 236 0.50 2.347416903

260

Link

StartNode

EndNode



% Time Delay

83 16 0 0.00 085 83 10 0.00 2.14763E-0984 85 77 0.03 0.00010751129 84 1410 0.51 12.8166467884 77 875 0.67 60.2226477478 77 0 0.00 079 78 70 0.08 0.0004944480 79 22 0.01 1.02394E-0781 80 58 0.06 0.00023951976 79 461 0.19 0.19910908529 76 461 0.19 0.19910908580 75 58 0.04 5.85662E-0530 75 0 0.00 030 29 743 0.27 2.92199610531 30 743 0.31 5.43897739532 31 258 0.09 0.0193268757 6 0 0.00 0

34 31 486 0.53 10.9740510434 4 416 0.45 5.0224551234 11 437 0.33 0.73178903434 33 0 0.00 032 33 477 0.36 1.02154257890 34 799 0.86 198.704309440 90 3 0.00 2.14483E-0935 90 3 0.00 5.24449E-1036 33 292 0.22 0.14927334836 90 154 0.12 0.05003676537 36 154 0.12 0.05003676538 37 344 0.37 4.554041183

261

Link

StartNode

EndNode



% Time Delay

56 38 527 0.57 11.1819051439 38 61 0.05 6.86352E-0535 12 854 0.36 3.79404320540 35 744 0.31 1.03892762339 40 78 0.03 6.24221E-0541 39 15 0.01 2.13999E-0842 41 256 0.11 0.00517865356 42 1112 0.85 72.3852038582 32 738 0.27 0.30497793757 82 945 0.65 22.4764554457 56 1638 1.25 2301.91748543 41 17 0.04 2.69349E-0540 43 102 0.21 0.56632894944 41 17 0.04 2.69349E-0550 44 20 0.04 5.20232E-0549 42 17 0.04 2.69349E-0548 49 22 0.05 6.56647E-0550 42 17 0.01 4.59948E-0754 50 24 0.02 1.58725E-0644 43 0 0.00 046 44 22 0.05 6.56647E-0561 57 104 0.11 0.01168426664 57 2907 1.23 4836.38788659 64 81 0.09 0.00090522191 64 2374 1.00 383.312853169 60 395 0.30 0.41551081863 58 481 0.52 8.12908946663 59 90 0.10 0.00132789260 61 463 0.50 5.902535166

262

Link

StartNode

EndNode



% Time Delay

62 60 455 0.49 2.87333963563 62 531 0.58 12.3920524465 60 74 0.08 0.00175698168 65 74 0.08 0.00175698191 69 1395 1.51 20715.3498466 69 0 0.00 065 66 147 0.16 0.0115012667 66 80 0.17 0.01208509168 67 340 0.72 7.87699474570 91 3 0.00 2.01103E-1074 70 156 0.12 0.00294343773 74 2430 1.85 48043432.827 70 583 0.63 7.16158678537 32 191 0.21 0.37698402345 22 34 0.06 0.00068776522 45 57 0.09 0.00115016178 81 606 0.66 36.4856065981 78 338 0.37 5.65676207648 47 272 0.57 4.86029146847 48 0 0.00 061 58 82 0.17 0.17347398658 61 481 1.01 192.770246461 58 78 0.16 0.01077357258 61 78 0.16 0.010773572

263

Level of Service of Road Network Links for (LRT) Scenario in Target year2030.

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

1 9 A 13 12 B9 1 B 10 11 A2 5 B 11 10 B5 2 B 14 13 B3 5 B 13 14 B5 3 B 10 9 B6 7 A 9 10 A7 6 A 14 15 B7 8 A 15 14 B8 7 A 15 16 A8 4 A 16 15 A4 8 B 86 87 F8 29 B 87 86 B

29 8 A 87 88 F4 3 B 88 87 B3 4 B 88 89 B2 9 A 89 88 F9 2 B 89 86 B3 10 B 86 89 B

10 3 B 14 86 B9 15 A 86 14 B

15 9 B 15 87 B10 14 B 87 15 B14 10 A 88 25 B4 11 A 89 23 B

11 4 B 13 18 B11 12 B 20 22 B12 11 B 20 86 B12 13 B 22 89 B

264

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

51 23 B 54 47 B18 20 B 54 47 B22 51 B 47 46 B22 51 B 46 45 F22 51 B 45 51 F11 12 A 55 72 B11 12 A 72 68 B12 13 B 68 73 F18 17 A 73 26 A18 17 A 72 27 B87 19 B 24 27 B88 21 B 24 27 A17 19 A 27 26 B19 21 A 26 28 B17 19 A 24 25 A19 21 A 28 25 B21 24 A 25 16 A21 24 A 1 83 B23 25 B 5 85 B25 24 B 16 83 A25 24 B 83 85 B17 16 A 85 84 B17 16 A 84 29 B51 52 B 77 84 B52 53 B 77 78 B52 55 B 78 79 B55 53 B 79 80 B53 54 B 80 81 B24 55 A 79 76 B

265

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

76 29 B 56 57 B75 80 B 41 43 B75 30 A 43 40 B29 30 B 41 44 B30 31 B 44 50 B31 32 B 42 49 B6 7 A 49 48 B

31 34 B 42 50 B4 34 A 50 54 B

11 34 A 43 44 B33 34 B 44 46 B33 32 B 57 61 F34 90 B 57 64 A90 40 B 64 59 F90 35 B 64 91 A33 36 B 60 69 A90 36 B 58 63 B36 37 B 59 63 F37 38 B 61 60 B38 56 B 60 62 B38 39 A 62 63 B12 35 B 60 65 B35 40 B 65 68 B40 39 B 69 91 B39 41 B 69 66 F41 42 B 66 65 B42 56 A 66 67 F32 82 A 67 68 F82 57 B 91 70 F

266

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

70 74 F 24 25 B74 73 B 24 25 B70 27 A 16 17 A32 37 B 16 17 A25 88 F 52 51 B23 89 B 53 52 B18 13 B 55 52 B22 20 B 53 55 F86 20 B 54 53 B89 22 B 55 24 B23 51 B 47 54 B20 18 B 47 54 B51 22 B 46 47 A51 22 B 45 46 B51 22 B 51 45 B12 11 A 72 55 B12 11 A 68 72 B13 12 B 73 68 B17 18 A 26 73 B17 18 A 27 72 B19 87 B 27 24 A21 88 B 27 24 A19 17 A 26 27 B21 19 A 28 26 B19 17 A 25 24 A21 19 A 25 28 B24 21 A 16 25 A24 21 A 83 1 A25 23 B 85 5 B

267

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

83 16 A 56 38 B85 83 B 39 38 A84 85 A 35 12 B29 84 B 40 35 B84 77 B 39 40 B78 77 A 41 39 B79 78 B 42 41 B80 79 B 56 42 B81 80 B 82 32 A76 79 B 57 82 B29 76 B 57 56 F80 75 B 43 41 B30 75 A 40 43 B30 29 B 44 41 B31 30 B 50 44 B32 31 B 49 42 B7 6 A 48 49 B

34 31 B 50 42 B34 4 B 54 50 B34 11 B 44 43 A34 33 A 46 44 B32 33 B 61 57 A90 34 B 64 57 F40 90 B 59 64 B35 90 B 91 64 F36 33 B 69 60 B36 90 B 63 58 B37 36 B 63 59 B38 37 B 60 61 B

268

LinkStartNode

EndNode

(LOS)

62 60 B63 62 B65 60 B68 65 B91 69 B66 69 F65 66 A67 66 B68 67 B70 91 B74 70 B73 74 A27 70 F37 32 B45 22 B22 45 B78 81 B81 78 B48 47 B47 48 B61 58 A58 61 B61 58 F58 61 B

269

Mean Noise Level (dB(A)) Produced from LRT scenario in 2030.

Link LinkStartNode

EndNode

Mean NoiseLevel dB(A).

StartNode

EndNode


1 9 0.0 13 12 55.49 1 61.1 10 11 0.02 5 57.8 11 10 62.95 2 46.4 14 13 45.13 5 63.1 13 14 45.15 3 64.1 10 9 61.16 6 0.0 9 10 0.07 7 0.0 14 15 55.67 8 0.0 15 14 65.38 7 0.0 15 16 0.08 4 0.0 16 15 0.04 8 69.8 86 87 67.08 29 69.8 87 86 56.6

29 8 0.0 87 88 68.54 3 62.3 88 87 56.63 4 64.1 88 89 56.42 9 0.0 89 88 74.59 2 57.8 89 86 67.03 10 52.1 86 89 54.0

10 3 55.3 14 86 54.09 15 0.0 86 14 52.4

15 9 57.8 15 87 55.610 14 56.1 87 15 57.814 10 0.0 88 25 52.74 11 0.0 89 23 66.8

11 4 66.2 13 18 68.911 12 66.4 20 22 68.9

12 11 67.6 20 86 56.612 13 68.9 22 89 63.2

270

Link LinkStartNode

EndNode


StartNode

EndNode


51 23 70.8 54 47 54.218 20 68.9 54 47 54.222 51 68.4 47 46 64.522 51 57.9 46 45 70.522 51 57.9 45 51 65.911 12 0.0 55 72 66.811 12 0.0 72 68 66.812 13 55.4 68 73 71.018 17 57.9 73 26 65.718 17 57.9 72 27 55.987 19 56.6 24 27 65.788 21 56.4 24 27 0.017 19 0.0 27 26 65.719 21 0.0 26 28 54.717 19 0.0 24 25 40.419 21 0.0 28 25 40.421 24 0.0 25 16 0.021 24 0.0 1 83 61.123 25 71.7 5 85 63.425 24 40.4 16 83 0.025 24 40.4 83 85 61.117 16 0.0 85 84 71.517 16 0.0 84 29 67.051 52 63.7 77 84 62.752 53 54.5 77 78 72.152 55 64.2 78 79 64.655 53 63.1 79 80 68.953 54 65.0 80 81 66.724 55 0.0 79 76 53.8

271

Link LinkStartNode

EndNode


StartNode

EndNode


76 29 58.8 56 57 67.875 80 58.0 41 43 52.775 30 0.0 43 40 58.829 30 63.6 41 44 64.330 31 63.6 44 50 52.131 32 63.6 42 49 64.56 7 0.0 49 48 64.5

31 34 52.7 42 50 68.44 34 0.0 50 54 67.8

11 34 0.0 43 44 60.433 34 67.9 44 46 65.133 32 63.1 57 61 73.134 90 56.4 57 64 69.990 40 70.0 64 59 108.590 35 70.7 64 91 55.333 36 57.0 60 69 0.090 36 67.8 58 63 59.536 37 67.4 59 63 108.537 38 67.1 61 60 64.338 56 67.1 60 62 52.738 39 63.0 62 63 52.712 35 55.4 60 65 67.135 40 58.8 65 68 77.540 39 60.9 69 91 45.139 41 63.7 69 66 68.341 42 63.7 66 65 65.942 56 60.2 66 67 88.632 82 66.4 67 68 69.382 57 70.9 91 70 120.8

272

Link LinkStartNode

EndNode


StartNode

EndNode


70 74 268.5 24 25 40.474 73 60.7 24 25 40.470 27 60.2 16 17 0.032 37 63.2 16 17 0.025 88 69.4 52 51 61.523 89 61.5 53 52 62.018 13 58.0 55 52 62.222 20 57.9 53 55 65.586 20 56.6 54 53 68.489 22 61.5 55 24 65.323 51 55.9 47 54 54.220 18 57.9 47 54 54.251 22 57.9 46 47 0.051 22 57.9 45 46 53.851 22 57.9 51 45 59.912 11 0.0 72 55 64.312 11 0.0 68 72 72.913 12 55.4 73 68 68.017 18 57.9 26 73 70.117 18 57.9 27 72 65.719 87 56.6 27 24 0.021 88 56.4 27 24 0.019 17 0.0 26 27 65.721 19 0.0 28 26 70.619 17 0.0 25 24 40.421 19 0.0 25 28 69.824 21 0.0 16 25 0.024 21 0.0 83 1 0.025 23 40.4 85 5 64.0

273

Link LinkStartNode

EndNode


StartNode

EndNode


83 16 0.0 56 38 67.085 83 50.4 39 38 58.284 85 59.2 35 12 69.529 84 71.2 40 35 69.084 77 67.9 39 40 59.378 77 0.0 41 39 52.179 78 58.8 42 41 64.580 79 53.8 56 42 68.881 80 58.0 82 32 69.076 79 67.0 57 82 69.129 76 67.0 57 56 89.180 75 58.0 43 41 52.730 75 0.0 40 43 60.430 29 68.9 44 41 52.731 30 68.8 50 44 53.432 31 64.5 49 42 52.77 6 0.0 48 49 53.8

34 31 66.7 50 42 52.734 4 66.3 54 50 54.234 11 66.7 44 43 0.034 33 0.0 46 44 53.832 33 67.1 61 57 60.590 34 67.5 64 57 102.040 90 45.1 59 64 59.535 90 45.1 91 64 74.336 33 65.0 69 60 66.336 90 62.2 63 58 66.837 36 62.2 63 59 59.938 37 65.5 60 61 66.7

274

LinkStartNode

EndNode


62 60 66.863 62 67.065 60 59.168 65 59.191 69 122.766 69 0.065 66 62.067 66 59.468 67 65.370 91 45.174 70 62.373 74 268.527 70 67.737 32 63.245 22 55.722 45 57.978 81 66.881 78 65.448 47 64.547 48 0.061 58 59.558 61 65.361 58 59.358 61 59.3

Appendix (E)

Public Transport Scenario

275

Trip Assignment on Road Network Links for Year 2030.

Link

StartNode

EndNode



% Time Delay

1 9 408 0.31 0.4107939 1 136 0.10 0.0115832 5 61 0.05 0.0004855 2 5 0.00 2.88E-093 5 277 0.21 0.3865255 3 511 0.39 1.2105946 7 0 0.00 07 6 0 0.00 07 8 0 0.00 08 7 0 0.00 08 4 327 0.09 0.0019524 8 1704 0.49 9.7848528 29 1704 0.49 9.784852

29 8 327 0.09 0.0019524 3 288 0.22 0.4279633 4 647 0.49 3.8637462 9 33 0.02 5.74E-069 2 61 0.05 0.0004853 10 47 0.04 5.85E-05

10 3 171 0.13 0.0068189 15 33 0.02 5.74E-06

15 9 61 0.05 0.00048510 14 349 0.27 0.84064914 10 4 0.00 1E-094 11 361 0.10 0.002706

11 4 700 0.20 0.14055511 12 1405 0.40 2.74892512 11 595 0.17 0.13758412 13 1568 0.45 5.936011

276

Link

StartNode

EndNode



% Time Delay

13 12 10 0.00 1.11E-0910 11 272 0.57 3.51145411 10 474 1.00 74.6576614 13 28 0.02 3.07E-0613 14 4 0.00 9.56E-1010 9 136 0.10 0.0115839 10 408 0.31 0.410793

14 15 775 0.59 13.6632515 14 449 0.34 4.27956415 16 306 0.23 0.13834916 15 0 0.00 086 87 572 1.20 384.923387 86 46 0.10 0.00129487 88 600 1.26 1261.87988 87 46 0.10 0.00129488 89 48 0.10 0.00156689 88 672 1.42 2525.34189 86 872 0.94 152.542586 89 64 0.07 0.00167814 86 64 0.07 0.00167886 14 487 0.53 5.57984115 87 470 0.99 77.6407687 15 61 0.13 0.02841988 25 21 0.04 0.00023789 23 490 1.03 370.593613 18 1564 0.45 5.86960620 22 1563 0.45 5.84760220 86 187 0.39 0.7709622 89 320 0.67 76.52902

277

Link

StartNode

EndNode



% Time Delay

51 23 1972 0.57 20.0195918 20 1564 0.45 5.86960622 51 1442 0.41 4.05240422 51 125 0.04 2.51E-0522 51 125 0.04 2.51E-0511 12 29 0.01 7.15E-0811 12 29 0.01 7.15E-0812 13 112 0.03 1.64E-0518 17 125 0.10 0.00123418 17 125 0.10 0.00123487 19 381 0.80 24.468388 21 48 0.10 0.00156617 19 306 0.33 0.56677119 21 686 0.74 33.0707717 19 0 0.00 019 21 30 0.03 1.77E-0521 24 269 0.29 0.6193321 24 0 0.00 023 25 2291 0.66 48.9238825 24 273 0.08 0.0052225 24 2 0.00 7.8E-1317 16 6 0.01 2.68E-0817 16 24 0.03 7.36E-0651 52 233 0.49 4.13082352 53 103 0.22 0.20731652 55 558 1.17 771.955455 53 303 0.64 55.0869953 54 405 0.31 3.31250424 55 543 1.14 1621.044

278

Link

StartNode

EndNode



% Time Delay

54 47 32 0.04 2.28E-0554 47 32 0.04 2.28E-0547 46 310 0.65 6.57323846 45 633 1.33 682.721645 51 593 1.25 109.404255 72 1083 0.82 36.365672 68 1366 1.04 356.180868 73 1982 1.51 36127.7673 26 803 0.61 22.3311972 27 210 0.16 0.01181924 27 420 0.18 0.02592724 27 22 0.01 1.02E-0727 26 630 0.27 0.17484126 28 64 0.03 8.08E-0624 25 2 0.00 2.22E-0928 25 2 0.00 1.96E-1225 16 24 0.01 9.25E-081 83 136 0.29 0.6803565 85 338 0.71 80.17552

16 83 6 0.00 3.37E-1083 85 142 0.05 0.00078585 84 2485 0.90 325.757384 29 947 0.34 2.23556977 84 240 0.18 0.02806277 78 970 1.05 771.494378 79 291 0.31 0.60830379 80 996 0.42 5.81865580 81 996 1.08 922.01479 76 23 0.01 1.39E-07

279

Link

StartNode

EndNode



% Time Delay

76 29 136 0.06 0.00016475 80 80 0.06 0.00020375 30 0 0.00 029 30 301 0.11 0.04351530 31 301 0.13 0.08004931 32 301 0.11 0.0435156 7 0 0.00 0

31 34 22 0.02 4.45E-064 34 26 0.03 9.59E-06

11 34 6 0.00 5.18E-0933 34 1606 1.22 1073.74933 32 555 0.42 2.83075234 90 72 0.08 0.0032390 40 1144 1.24 9123.79190 35 1586 1.21 5575.03933 36 8 0.01 2.18E-0890 36 1544 1.18 3617.99736 37 616 0.47 10.8276437 38 989 1.07 901.507138 56 988 1.07 892.642738 39 381 0.29 0.34089612 35 286 0.12 0.00875835 40 207 0.09 0.00215840 39 384 0.16 0.04891839 41 590 0.25 0.34359941 42 510 0.22 0.21785942 56 364 0.28 0.38971232 82 684 0.25 0.78137482 57 1488 1.02 1038.921

280

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% Time Delay

56 57 1351 1.03 1223.9241 43 47 0.10 0.00145843 40 5 0.01 2.22E-0741 44 307 0.65 9.69829944 50 113 0.24 0.0937642 49 310 0.65 6.57323849 48 310 0.65 6.57323842 50 855 0.65 24.0122850 54 968 0.74 95.6247443 44 128 0.27 0.81514644 46 323 0.68 12.468757 61 1222 1.32 5827.69957 64 1465 0.62 56.2472864 59 1879 2.03 241063264 91 218 0.09 0.00250960 69 0 0.00 058 63 103 0.11 0.02044859 63 1879 2.03 241063261 60 308 0.33 1.63368660 62 158 0.17 0.0681562 63 158 0.17 0.0681560 65 1229 1.33 1005.07565 68 1474 1.60 20369.2569 91 17 0.02 1.9E-0669 66 1186 1.28 363.493566 65 581 0.63 7.61163166 67 1022 2.15 101739.567 68 973 2.05 33592.2291 70 2675 2.04 6903429

281

Link

StartNode

EndNode



% Time Delay

70 74 3498 2.66 5.78E+1674 73 159 0.12 0.00318170 27 646 0.70 6.5043732 37 392 0.42 4.48031325 88 649 1.37 1539.12123 89 170 0.36 2.30816918 13 10 0.00 1.11E-0922 20 195 0.06 0.00031786 20 46 0.10 0.00129489 22 381 0.80 24.4158523 51 39 0.01 2.42E-0720 18 10 0.00 1.11E-0951 22 125 0.04 2.51E-0551 22 125 0.04 2.51E-0551 22 125 0.04 2.51E-0512 11 29 0.01 7.15E-0812 11 29 0.01 7.15E-0813 12 112 0.03 1.64E-0517 18 125 0.10 0.00123417 18 125 0.10 0.00123419 87 46 0.10 0.00129421 88 417 0.88 43.0624619 17 6 0.01 2.68E-0821 19 6 0.01 2.68E-0819 17 0 0.00 021 19 30 0.03 1.77E-0524 21 6 0.01 2.68E-0824 21 0 0.00 025 23 2 0.00 7.82E-13

282

Link

StartNode

EndNode



% Time Delay

24 25 2 0.00 7.8E-1324 25 2 0.00 7.8E-1316 17 306 0.33 0.56677116 17 24 0.03 7.36E-0652 51 175 0.37 2.36671653 52 325 0.68 4.31400755 52 278 0.58 21.7539253 55 561 1.18 577.131154 53 886 0.67 28.9318455 24 426 0.90 53.3618947 54 27 0.03 3.68E-0547 54 32 0.04 2.28E-0546 47 27 0.06 0.00052745 46 32 0.07 0.00111951 45 5 0.01 2.22E-0772 55 310 0.24 0.81771868 72 1460 1.11 1697.40473 68 942 0.72 55.8137226 73 1734 1.32 18547.427 72 1087 0.83 46.4399627 24 22 0.01 1.02E-0727 24 22 0.01 1.02E-0726 27 1087 0.46 3.7295128 26 1914 0.81 131.830325 24 2 0.00 2.22E-0925 28 1390 0.50 11.2543716 25 24 0.01 9.25E-0883 1 408 0.86 26.3754685 5 511 1.07 94.48355

283

Link

StartNode

EndNode



% Time Delay

83 16 24 0.01 9.25E-0885 83 408 0.15 0.02107284 85 150 0.05 0.00117729 84 2409 0.87 166.95284 77 970 0.74 73.1845678 77 240 0.26 0.11479479 78 510 0.55 3.29863780 79 20 0.01 9.7E-0881 80 20 0.02 4.2E-0676 79 1196 0.50 5.94317129 76 1196 0.50 5.94317180 75 80 0.06 0.00020330 75 0 0.00 030 29 1581 0.57 14.9095631 30 1581 0.67 29.038132 31 904 0.33 0.6624017 6 0 0.00 0

34 31 699 0.76 55.5249834 4 681 0.74 79.81334 11 1355 1.03 177.510634 33 6 0.00 5.18E-0932 33 1234 0.94 69.6290590 34 1180 1.28 4199.05240 90 5 0.01 1.55E-0835 90 6 0.00 5.53E-0936 33 928 0.71 34.5974536 90 199 0.15 0.12471837 36 191 0.15 0.10236438 37 392 0.42 5.896396

284

Link

StartNode

EndNode



% Time Delay

56 38 771 0.83 48.7037439 38 79 0.06 0.00019735 12 1034 0.44 7.20626940 35 1013 0.43 3.7935539 40 175 0.07 0.00055141 39 30 0.01 4.1E-0742 41 228 0.10 0.00395756 42 1246 0.95 72.2790782 32 2138 0.77 68.0324557 82 2006 1.38 17065.9157 56 2017 1.54 74351.1143 41 47 0.10 0.00145840 43 128 0.27 0.81514644 41 47 0.10 0.00145850 44 25 0.05 0.00010749 42 41 0.09 0.00081948 49 25 0.05 0.00011150 42 41 0.03 1.4E-0554 50 32 0.02 5.57E-0644 43 5 0.01 2.22E-0746 44 5 0.01 2.22E-0761 57 623 0.67 40.7297964 57 4036 1.70 196227359 64 117 0.13 0.00388391 64 3711 1.57 196932.969 60 829 0.63 8.88265563 58 794 0.86 57.0894363 59 150 0.16 0.01050660 61 1146 1.24 3357.392

285

Link

StartNode

EndNode



% Time Delay

62 60 938 1.02 48.3960163 62 1049 1.14 1442.91665 60 458 0.50 8.14390768 65 334 0.36 3.20072791 69 2015 2.18 357968366 69 0 0.00 065 66 212 0.23 0.05048967 66 205 0.43 0.85328968 67 980 2.06 24604.7970 91 17 0.01 4.65E-0774 70 214 0.16 0.01055473 74 3498 2.66 5.78E+1627 70 1469 1.59 3601.35737 32 219 0.24 0.49813745 22 40 0.07 0.00106122 45 27 0.04 0.0001978 81 929 1.01 371.431881 78 470 0.51 21.8914448 47 310 0.65 6.57323847 48 0 0.00 061 58 103 0.22 0.29316858 61 794 1.67 15242.8861 58 98 0.21 0.02687258 61 98 0.21 0.026872

286

Level of Service of Road Network Links for Public Transport Scenario forTarget year 2030.

Link Link

StartNode

EndNode (LOS)

StartNode

EndNode (LOS)

1 9 B 13 12 B9 1 B 10 11 B2 5 B 11 10 B5 2 B 14 13 B3 5 B 13 14 B5 3 B 10 9 B6 7 A 9 10 B7 6 A 14 15 B7 8 A 15 14 B8 7 A 15 16 B8 4 B 16 15 A4 8 B 86 87 F8 29 B 87 86 B

29 8 B 87 88 F4 3 B 88 87 B3 4 B 88 89 B2 9 B 89 88 F9 2 B 89 86 B3 10 B 86 89 B

10 3 B 14 86 B9 15 B 86 14 B

15 9 B 15 87 B10 14 B 87 15 B14 10 B 88 25 B4 11 B 89 23 F

11 4 B 13 18 B11 12 B 20 22 B12 11 B 20 86 B12 13 B 22 89 B

287

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

51 23 B 54 47 B18 20 B 54 47 B22 51 B 47 46 B22 51 B 46 45 F22 51 B 45 51 F11 12 B 55 72 B11 12 B 72 68 F12 13 B 68 73 F18 17 A 73 26 B18 17 A 72 27 B87 19 B 24 27 B88 21 B 24 27 B17 19 B 27 26 B19 21 B 26 28 B17 19 A 24 25 A19 21 B 28 25 B21 24 B 25 16 A21 24 A 1 83 B23 25 B 5 85 B25 24 B 16 83 A25 24 B 83 85 B17 16 B 85 84 B17 16 B 84 29 B51 52 B 77 84 B52 53 B 77 78 F52 55 F 78 79 B55 53 B 79 80 B53 54 B 80 81 F24 55 F 79 76 B

288

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

76 29 B 56 57 F75 80 B 41 43 B75 30 A 43 40 B29 30 B 41 44 B30 31 B 44 50 B31 32 B 42 49 B6 7 A 49 48 B

31 34 B 42 50 B4 34 B 50 54 B

11 34 B 43 44 B33 34 F 44 46 B33 32 B 57 61 F34 90 B 57 64 B90 40 F 64 59 F90 35 F 64 91 A33 36 B 60 69 A90 36 F 58 63 B36 37 B 59 63 F37 38 F 61 60 B38 56 F 60 62 B38 39 B 62 63 B12 35 B 60 65 F35 40 B 65 68 F40 39 B 69 91 B39 41 B 69 66 F41 42 B 66 65 B42 56 B 66 67 F32 82 A 67 68 F82 57 F 91 70 F

289

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

70 74 F 24 25 B74 73 B 24 25 B70 27 B 16 17 B32 37 B 16 17 B25 88 F 52 51 B23 89 B 53 52 B18 13 B 55 52 B22 20 B 53 55 F86 20 B 54 53 B89 22 B 55 24 B23 51 B 47 54 B20 18 B 47 54 B51 22 B 46 47 B51 22 B 45 46 B51 22 B 51 45 B12 11 B 72 55 B12 11 B 68 72 F13 12 B 73 68 B17 18 A 26 73 F17 18 A 27 72 B19 87 B 27 24 B21 88 B 27 24 B19 17 B 26 27 B21 19 B 28 26 B19 17 A 25 24 A21 19 B 25 28 B24 21 B 16 25 A24 21 A 83 1 B25 23 B 85 5 F

290

Link LinkStartNode

EndNode

(LOS) StartNode

EndNode

(LOS)

83 16 A 56 38 B85 83 B 39 38 A84 85 A 35 12 B29 84 B 40 35 B84 77 B 39 40 B78 77 B 41 39 B79 78 B 42 41 B80 79 B 56 42 B81 80 B 82 32 B76 79 B 57 82 F29 76 B 57 56 F80 75 B 43 41 B30 75 A 40 43 B30 29 B 44 41 B31 30 B 50 44 B32 31 B 49 42 B7 6 A 48 49 B

34 31 B 50 42 B34 4 B 54 50 B34 11 F 44 43 B34 33 B 46 44 B32 33 B 61 57 B90 34 F 64 57 F40 90 B 59 64 B35 90 B 91 64 F36 33 B 69 60 B36 90 B 63 58 B37 36 B 63 59 B38 37 B 60 61 F

291

LinkStartNode

EndNode

(LOS)

62 60 F63 62 F65 60 B68 65 B91 69 F66 69 A65 66 B67 66 B68 67 F70 91 B74 70 A73 74 F27 70 F37 32 B45 22 B22 45 B78 81 F81 78 B48 47 B47 48 A61 58 B58 61 F61 58 B58 61 B

292

Mean Noise Level (dB(A)) Produced from Public Transport Scenario in2030.

Link LinkStartNode

EndNode


StartNode

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1 9 66.5 13 12 50.49 1 61.7 10 11 64.52 5 58.2 11 10 65.15 2 47.4 14 13 54.83 5 64.8 13 14 46.45 3 67.4 10 9 61.76 6 0.0 9 10 66.57 7 0.0 14 15 68.67 8 0.0 15 14 66.78 7 0.0 15 16 65.28 4 65.5 16 15 0.04 8 72.2 86 87 68.18 29 72.2 87 86 57.0

29 8 65.5 87 88 77.64 3 64.9 88 87 57.03 4 68.3 88 89 57.22 9 55.6 89 88 86.49 2 58.2 89 86 67.63 10 57.1 86 89 58.4

10 3 62.7 14 86 58.49 15 55.6 86 14 67.0

15 9 58.2 15 87 65.010 14 65.8 87 15 58.214 10 46.4 88 25 53.64 11 66.0 89 23 67.3

11 4 68.8 13 18 72.011 12 71.7 20 22 72.012 11 68.1 20 86 63.112 13 72.0 22 89 63.3

293

Link LinkStartNode

EndNode


StartNode

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51 23 72.4 54 47 55.418 20 72.0 54 47 55.422 51 71.7 47 46 64.922 51 61.3 46 45 72.122 51 61.3 45 51 65.911 12 55.0 55 72 69.311 12 55.0 72 68 71.612 13 60.9 68 73 134.118 17 61.3 73 26 68.418 17 61.3 72 27 63.687 19 65.1 24 27 66.688 21 57.2 24 27 53.817 19 65.2 27 26 68.419 21 67.4 26 28 58.417 19 0.0 24 25 43.419 21 55.1 28 25 43.421 24 64.6 25 16 54.221 24 0.0 1 83 61.723 25 72.3 5 85 63.525 24 64.7 16 83 48.225 24 43.4 83 85 61.917 16 48.2 85 84 73.817 16 54.2 84 29 70.051 52 63.8 77 84 64.252 53 60.5 77 78 75.052 55 72.6 78 79 65.055 53 63.4 79 80 70.153 54 66.3 80 81 76.624 55 79.9 79 76 54.0

294

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EndNode


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76 29 61.7 56 57 80.875 80 59.4 41 43 57.175 30 0.0 43 40 47.429 30 65.2 41 44 64.830 31 65.2 44 50 60.931 32 65.2 42 49 64.96 7 0.0 49 48 64.9

31 34 53.8 42 50 68.64 34 54.5 50 54 68.0

11 34 48.2 43 44 61.433 34 80.2 44 46 64.933 32 67.7 57 61 101.134 90 58.9 57 64 70.290 40 107.9 64 59 211.590 35 101.5 64 91 63.833 36 49.4 60 69 0.090 36 95.0 58 63 60.536 37 67.7 59 63 211.537 38 76.4 61 60 65.238 56 76.3 60 62 62.438 39 66.2 62 63 62.412 35 64.9 60 65 78.435 40 63.5 65 68 122.640 39 66.2 69 91 52.739 41 68.1 69 66 71.041 42 67.4 66 65 67.642 56 66.0 66 67 150.132 82 68.7 67 68 129.782 57 79.5 91 70 232.7

295

Link LinkStartNode

EndNode


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70 74 660.6 24 25 43.474 73 62.4 24 25 43.470 27 68.1 16 17 65.232 37 66.1 16 17 54.225 88 80.1 52 51 62.723 89 62.6 53 52 65.318 13 50.4 55 52 63.822 20 63.3 53 55 70.486 20 57.0 54 53 68.689 22 65.1 55 24 64.923 51 56.3 47 54 54.720 18 50.4 47 54 55.451 22 61.3 46 47 54.751 22 61.3 45 46 55.451 22 61.3 51 45 47.412 11 55.0 72 55 65.212 11 55.0 68 72 84.813 12 60.9 73 68 68.317 18 61.3 26 73 121.717 18 61.3 27 72 69.119 87 57.0 27 24 53.821 88 65.0 27 24 53.819 17 48.2 26 27 70.521 19 48.2 28 26 70.919 17 0.0 25 24 43.421 19 55.1 25 28 71.224 21 48.2 16 25 54.224 21 0.0 83 1 65.325 23 43.4 85 5 65.2

296

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83 16 54.2 56 38 67.585 83 66.5 39 38 59.484 85 62.1 35 12 70.129 84 72.1 40 35 70.284 77 68.2 39 40 62.878 77 64.2 41 39 55.179 78 67.3 42 41 64.080 79 53.4 56 42 69.381 80 53.4 82 32 71.776 79 70.8 57 82 120.929 76 70.8 57 56 147.380 75 59.4 43 41 57.130 75 0.0 40 43 61.430 29 71.6 44 41 57.131 30 71.1 50 44 54.432 31 69.9 49 42 56.57 6 0.0 48 49 54.4

34 31 67.0 50 42 56.534 4 66.6 54 50 55.434 11 69.7 44 43 47.434 33 48.2 46 44 47.432 33 69.3 61 57 66.890 34 96.0 64 57 211.040 90 47.4 59 64 61.135 90 48.2 91 64 167.936 33 68.7 69 60 69.136 90 63.4 63 58 67.537 36 63.2 63 59 62.138 37 66.0 60 61 92.6

297

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62 60 68.463 62 81.565 60 66.668 65 65.491 69 219.266 69 0.065 66 63.667 66 63.468 67 124.270 91 52.774 70 63.773 74 660.627 70 94.737 32 63.845 22 56.422 45 54.778 81 70.181 78 66.148 47 64.947 48 0.061 58 60.558 61 114.961 58 60.358 61 60.3

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كلیھ الھندسةجامعة طنطا

ةشغال العامقسم ھندسھ األ

طنطا مدینة-ریةاالحضعملیة تخطیط النقل للمناطق .كحالة دراسة

رسالة علمیة مقدمة مـن

أحمد محمد عبد الحمید الكافوريمعید بقسم ھندسة األشغال العامة

جامعة طنطا–كلیة الھندسة )2004(لوریوس الھندسة اإلنشائیةابك

رسالة كجزء من متطلبات الحصول على درجة الماجستیـرمقدمھ

"األشغال العامـة"ندسـة المدنیـة في الھ

إشرافتحت

علي محمدمحمد الشبراوي/ د.أأستاذ ھندسة الطرق والمرور

. قسم ھندسة األشغال العامةالمنصورةةجامع-كلیھ الھندسة

محمد حافظ فھمي/ د.أالحدیدیة والنقللسكك أستاذ ھندسة ا

. قسم ھندسة المواصالترئیس مجلس اإلسكندریةةجامع-كلیھ الھندسة

حافظ عباس عفیفي/ د.م.أ.قسم ھندسة األشغال العامة

طنطاةجامع-كلیھ الھندسة

2012

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