CONSULTANCY SERVICES FOR THE CONCEPTUAL DESIGN OF A … · 2014. 11. 12. · figure 14 example map...

UNITED REPUBLIC OF TANZANIA Prime Ministers Office for Regional

Administration and Local Government

The Dar es Salaam City Council

CONSULTANCY SERVICES FOR THE CONCEPTUAL DESIGN OF A LONG TERM INTEGRATED DAR ES SALAAM BRT SYSTEM AND DETAILED DESIGN FOR THE INITIAL CORRIDOR

ANNEX VOLUME 3 FIELD SURVEYS AND DATA COLLECTION Final Report

Dar es Salaam June, 2007

Field Survey and Data Collection

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TABLE OF CONTENTS

1. ACKNOWLEDGEMENTS--------------------------------------------------------------------------------6 1.1. OBJECTIVE ----------------------------------------------------------------------------------------------------- 6 1.2. SURVEYS INFORMATION FUTURE UTILIZATION ----------------------------------------------- 6 1.3. COMPUTER FILES AND CONVENTIONS ------------------------------------------------------------- 7

1.3.1. DARTDBS --------------------------------------------------------------------------------------------------------------7 1.3.2. MS-EXCEL FILES WITH MACROS ----------------------------------------------------------------------------8

2. INTRODUCTION TO MODELING --------------------------------------------------------------------9 2.1. GENERAL-------------------------------------------------------------------------------------------------------- 9 2.2. A MODELING EXAMPLE WITH MANAGING ------------------------------------------------------11 2.3. TRANSPORTATION MODEL -----------------------------------------------------------------------------13

2.3.1. MAP -------------------------------------------------------------------------------------------------------------------- 13 2.3.2. POINTS---------------------------------------------------------------------------------------------------------------- 29 2.3.3. LINKS ----------------------------------------------------------------------------------------------------------------- 33 2.3.4. ROUTES -------------------------------------------------------------------------------------------------------------- 36 2.3.5. RESTRICTIONS ---------------------------------------------------------------------------------------------------- 39

2.4. TRANSPORTATION MODEL VALIDATION AND PROCEDURES ----------------------------44 3. DATA COLLECTION ACTIVITIES------------------------------------------------------------------ 47

3.1. WORK PREPARATION-------------------------------------------------------------------------------------47 3.1.1. FIELD PREVIEW--------------------------------------------------------------------------------------------------- 47 3.1.2. COMPILATION OF DATA ALREADY SURVEYED ------------------------------------------------------ 47 3.1.3. PLANNING FIELD SURVEYS, LOCATIONS, AND TEAM SIZING ---------------------------------- 48 3.1.4. PLANNING & DEVELOPING DATA ENTRY: SOFTWARE AND PROCEDURES --------------- 49

3.2. MAP SURVEYS ------------------------------------------------------------------------------------------------50 3.2.1. PASSING THRU NODES ----------------------------------------------------------------------------------------- 50 3.2.2. O/D NODES: CENTROIDS AND ZONES --------------------------------------------------------------------- 50 3.2.3. LINKS ----------------------------------------------------------------------------------------------------------------- 52 3.2.4. ROUTES ITINERARIES ------------------------------------------------------------------------------------------ 54

3.3. TRAFFIC SUPPLY AND DEMAND SURVEYS -------------------------------------------------------54 3.3.1. OPTIONS AND JUSTIFICATIONS----------------------------------------------------------------------------- 58

3.4. FORMS AND PROCEDURES ------------------------------------------------------------------------------61 3.4.1. O/D SURVEY (ODSU) --------------------------------------------------------------------------------------------- 62 3.4.2. FREQUENCY AND VISUAL OCCUPANCY SURVEY (FVOSU) --------------------------------------- 66 3.4.3. CLASSIFYING COUNTING SURVEY (CCSU) ------------------------------------------------------------- 68 3.4.4. VELOCITY, BOARDING AND ALIGHTING SURVEY (VBASU)-------------------------------------- 71 3.4.5. DIRECTIONAL FLOW (CLASSIFYING) COUNTING (DFSU) ---------------------------------------- 73 3.4.6. STATION BOARDING AND ALIGHTING SURVEY------------------------------------------------------ 75

3.5. SURVEY ACTIVITIES---------------------------------------------------------------------------------------77 3.5.1. TRAINING ----------------------------------------------------------------------------------------------------------- 80 3.5.2. MONITORING AND EVALUATION -------------------------------------------------------------------------- 84

4. DATA PROCESSING, ANALYSIS AND RESULTS----------------------------------------------- 87 4.1. FREQUENCY AND VISUAL OCCUPANCY -----------------------------------------------------------87

4.1.1. PEAK HOUR IDENTIFICATION ------------------------------------------------------------------------------ 88 4.1.3. PEAK EXPANSION FACTORS --------------------------------------------------------------------------------- 89 4.1.4. MASTER POINT – POINT 10 MOROGORO ROAD JANGWANI AREA ---------------------------- 89


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4.1.5. BUS ROUTES FREQUENCIES LIST -------------------------------------------------------------------------- 91 4.1.6. BUS ROUTES ITINERARIES ----------------------------------------------------------------------------------- 92

4.2. ORIGIN AND DESTINATION -----------------------------------------------------------------------------94 4.3. MOST IMPORTANT ZONES ------------------------------------------------------------------------------94 4.4. ANALYSIS BY MUNICIPALITY--------------------------------------------------------------------------95

4.4.1. ILALA MUNICIPALITY------------------------------------------------------------------------------------------ 95 4.4.2. KINONDONI MUNICIPALITY --------------------------------------------------------------------------------- 97 4.4.3. TEMEKE MUNICIPALITY -------------------------------------------------------------------------------------- 98

4.5. VELOCITY BOARDING AND ALIGHTING -------------------------------------------------------- 100 4.6. CLASSIFIED COUNTING’S ----------------------------------------------------------------------------- 102

4.6.1. POINT 10 CLASSIFIED COUNT RESULTS ----------------------------------------------------------------105 4.7. DIRECTIONAL FLOW RESULTS---------------------------------------------------------------------- 108 4.8. STATION BOARDING AND ALIGHTING RESULTS--------------------------------------------- 116 4.9. CBD TRANSPORTATION ZONES UPDATE -------------------------------------------------------- 118 5. RECOMMENDATIONS-------------------------------------------------------------------------------------- 120

APPENDIX ...................................................................................................................... 121

APPENDIX A FVO FREQUENCIES LIST.......................................................................I

APPENDIX B FVO ROUTE ITINERARIES ................................................................. II

APPENDIX C FVO PEAK FLOWS ...............................................................................III

APPENDIX D OD SURVEY SAMPLES.........................................................................IV

APPENDIX E VBA SURVEY RESULTS ........................................................................V

LIST OF TABLES

TABLE 1 EXAMPLE OF NODES TABLE – EASTINGS AND NORTHINGS CAN BE ANY CONVENIENT XY COORDINATES SYSTEM.NODES TABLE – EASTINGS AND NORTHINGS CAN BE ANY CONVENIENT XY COORDINATES SYSTEM....................................................................................................................................18 TABLE 2LINKS TABLE –LENGTH MIGHT BEGREATER THAN THE DISTANCE BETWEEN THE NODES ..................19 TABLE 3 NODES TABLE TO MODEL MAP AS EXAMPLE .....................................................................................24 TABLE 4LINKS – ALTERNATIVE 1:DECLARING IF LINKS ARE TWO WAYS ........................................................24 TABLE 5 TABLE OF LINKS – ALTERNATIVE 2: DECLARING EVERY ONE-WAY LINK. ........................................25 TABLE 6 LINKS WITH ATRIBUTE TYPE OF TRAFFIC..........................................................................................27 TABLE 7-DATA EXAMPLE FOR ONE O/D NODE ...............................................................................................32 TABLE 8SIMPLE LINKS TABLE EXAMPLE, FOR“NETWORKSTARTUP.XLS”.......................................................35 TABLE 9 GEO-REFERRED LINKS’ TABLE EXAMPLE (FROM JAKARTA MODEL DATA)........................................35 TABLE 10 -EXAMPLE OF ROUTES DATA ..........................................................................................................38 TABLE 11 ROUTES ITINERARIES TO GEO-REFERENCED DATA (FROM JAKARTA DATABASE)............................38 TABLE 12 SURVEY POINT LIST .......................................................................................................................57

TABLE 13 -SAMPLE SIZE FOR ORIGIN-DESTINATION SURVEYS BASED ON PASSENGER TRAFFIC.......................61 TABLE 14VELOCITY BOARDING AND ALIGHTING SURVEY ROUTE SELECTION ..............................................73 TABLE 15 STATION BOARDING AND ALIGHTING SURVEY POINTS ALONG MOROGORO ROAD .......................77 TABLE 16 PASSENGER VOLUMES ON MAJOR SURVEY POINTS .......................................................................90 TABLE 17PEAK FACTORS FOR THE 5 FULL DAY SURVEYED POINTS (MORNING PEAK)....................................90 TABLE 18 PEAK FACTORS FOR THE 5 FULL DAY SURVEYED POINTS (EVENING PEAK).....................................90 TABLE 19 MOROGORO ROAD (POINT 10) ROUTES FREQUENCIES...................................................................92 TABLE 20 TOP TEN TRIP PRODUCTION WARDS ..............................................................................................96


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TABLE 21 TOP TEN TRIP ATTRACTION WARDS...............................................................................................96 TABLE 22ILALA MUNICIPALITY TRIP PRODUCTION AND ATTRACTION ..........................................................97 TABLE 23 KINONDONI MUNICIPALITY TRIP PRODUCTION AND ATTRACTION ................................................98 TABLE 24 TEMEKE MUNICIPALITY TRIP PRODUCTION AND ATTRACTION......................................................99 TABLE 25 SELECTED ROUTES OPERATIONAL PARAMETERS.........................................................................102 TABLE 26 MORNING PEAK PROPORTIONS FOR TRANSPORTATION MODES ..................................................103 TABLE 27 MORNING PEAK PROPORTIONS FOR TRANSPORTATION MODES ON POINT 10 .............................106 TABLE 28 POINT 10 CLASSIFIED COUNTING’S RESULTS...............................................................................107 TABLE 29 MOROGORO ROAD WAY 1 BOARDING AND ALIGHTING RESULTS ...............................................117 TABLE 30 MOROGORO ROAD WAY 2 BOARDING AND ALIGHTING RESULTS ...............................................118 LIST OF FIGURES

FIGURE 1 SATELLITE PICTURE: DAR ES SALAAM, FROM GOOGLE EARTH.......................................................14 FIGURE 2 DETAIL FROM SATELITE PICTURE ....................................................................................................15 FIGURE 3MAP FROM DAR, SHOWING CONSTRUCTIONS ...................................................................................15 FIGURE 4MODEL WITH EXTREME LEVEL OF DETAIL (CONSIDERING EACH BUILDING).....................................16 FIGURE 5 MODEL WITH VERY HIGH LEVEL OF DETAIL (CONSIDERING THE ADDRESSES) ................................16 FIGURE 6MODEL WITH HIGH LEVEL OF DETAIL, CONSIDERING THE INTERSECTIONS ......................................17 FIGURE 7 MODEL WITH PRACTICAL LEVEL OF DETAIL, WITHOUT LAST WALKING LINK TO REACH ORIGINOR DESTINATION ..................................................................................................................................................18 FIGURE 8 NODES OF “NETWORKSTARTUP.XLS”, WITHOUT IDENTIFICATION. .................................................20 FIGURE 9 NODES OF “NETWORKSTARTUP.XLS”, WITH IDENTIFICATION ........................................................21 FIGURE 10 “NETWORKSTARTUP.XLS”CONSTRUCT! – ADDING TWO NEW LINKS............................................22 FIGURE 11 ROAD NETWORK WITHOUT DUMMY NODES FIGURE 12 ROAD NETWORK WITH DUMMY NODES…22 FIGURE 13 MAP WITH NODES IDENTIFIED AND GRID.......................................................................................26 FIGURE 14 EXAMPLE MAP SHOWING ONLY ONE WAY LINKS ..........................................................................26 FIGURE 15 EXAMPLE MAP – THE GREEN LINKS EXISTS, BUT ONLY PEDESTRIANS...........................................28 FIGURE 16 ROUTE DRAW ON MAP NETWORK ..................................................................................................31 FIGURE 17 PASSING THRU NODE - INTERSECTION EXAMPLE ...........................................................................31 FIGURE 18 NODE WITHOUT MODELING INTERSECTION, AND NODE MODELING INTERSECTION …….……......43 FIGURE 19 FLOW OF PASSENGERS ON A POINT OF CONTROL............................................................................45 FIGURE 20 TRANSPORTATION MODELING PROCEDURE ..................................................................................45 FIGURE 21 DSM NETWORK – PASSING THRU NODES......................................................................................50 FIGURE 22 CENTROIDS AND ZONES BOUNDARIES ..........................................................................................51 FIGURE 23 DSM MODEL STREET LINKS .........................................................................................................52 FIGURE 24 MODEL LINKS BETWEEN CENTROIDS AND ROAD NETWORK. ........................................................53 FIGURE 25 SURVEY POINTS LOCATION IN DSM..............................................................................................56 FIGURE 26 EXAMPLE OF SURVEY DETAILED POINT LOCATION ......................................................................61 FIGURE 27 OD SURVEYOR FORM....................................................................................................................63 FIGURE 28 OD SURVEYOR KATAA AND MTAA CARD (TEMEKE) ...................................................................64 FIGURE 29 ODSURVEYOR KATAA AND MTAA CARD(ILALA AND KININDONI)...............................................65 FIGURE 30 FVOSU FORM................................................................................................................................67 FIGURE 31a. LIGHT DUTY/PASSENGERS VEHICLE FORM ………………………………………….69 FIGURE 31b HEAVY DUTY VEHICLES FORM AND CCSU FORM ............. ............ . .............................70 FIGURE 32 VBA GENERIC FORM....................................................................................................................71 FIGURE 33 VBASU FORM FILLED WITH REFERENCES .....................................................................................72 FIGURE 34 DFSU FORM...................................................................................................................................74 FIGURE 35 SBASU LOCATIONS (EXCEPT FIRE) ..............................................................................................75 FIGURE 36 SBASU FORM................................................................................................................................76 FIGURE 37 SURVEYS FINAL SCHEDULE ...........................................................................................................78 FIGURE 38 VBA SURVEYS FINAL SCHEDULE .................................................................................................79 FIGURE 38a CLASSIFIED COUNTS PHASE 1 A SCHEDULE ..................................................................................79 FIGURE 39 FIELD DATA COLLECTION WORK FLOW .........................................................................................86 FIGURE 40 FULL DAY SURVEYED POINTS PAX/H PROFILE – PEAK HOUR DEPICTION....................................89 FIGURE 41 POINT 10 PEAK PASSENGERS VOLUME PER HOUR ........................................................................91 FIGURE 42 POINT 10 FULL DAY PASSENGER VOLUME PROFILE – HOURLY ANALYSIS..................................91 FIGURE 43 KIMARA – POSTA ROUTE ITINERARY............................................................................................94 FIGURE 44 KIMARA – KARIAKOO ROUTE ITINERARY.....................................................................................94


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FIGURE 45 ILALA MUNICIPALITY WARDS ORIGIN/DESTINATION MATRIX PARTICIPATION............................97 FIGURE 46 KINONDONI MUNICIPALITY WARDS ORIGIN/DESTINATION MATRIX PARTICIPATION...................99 FIGURE 47 TEMEKE MUNICIPALITY WARDS ORIGIN/DESTINATION MATRIX PARTICIPATION .....................100 FIGURE 48 DSM ORIGIN DESTINATION MATRIX DISTRIBUTED BY ZONES ..................................................101 FIGURE 49 KIMKOO LOAD PROFILE PM PEAK HOUR ...................................................................................103 FIGURE 50 MORNING PEAK MODAL SPLIT ...................................................................................................104 FIGURE 51 WAY 1 FULL DAY SURVEYED POINTS ALL TRAFFIC PROFILE........................................................104 FIGURE 52 WAY 1 FULL DAY SURVEYED POINTS DALADALA TRAFFIC PROFILE.........................................105 FIGURE 53 WAY 2 FULL DAY SURVEYED POINTS ALL TRAFFIC PROFILE ....................................................105 FIGURE 54 WAY 2 FULL DAY SURVEYED POINTS DALADALA TRAFFIC PROFILE.........................................106 FIGURE 55 MORNING PEAK MODAL SPLIT ON POINT 10 ..............................................................................107 FIGURE 56 POINT 10 WAY 1 TRAFFIC ALL PROFILE.....................................................................................107 FIGURE 57 POINT 10 WAY 2 ALL TRAFFIC PROFILE.....................................................................................108 FIGURE 58 POINT 10 WAY 1 DALADALA TRAFFIC PROFILE ........................................................................108 FIGURE 59 POINT 10 WAY 2 DALADALA TRAFFIC PROFILE ........................................................................108 FIGURE 60 POINT 38 – MANDELA RD/SAM NUJOMA RD MOROGORO RD INTERSECTION............................109 FIGURE 61 POINT 39 -SHEKILANGO RD MOROGORO RD INTERSECTION.......................................................110 FIGURE 62 POINT 45-MABIBO RD MOROGORO RD INTERSECTION ..............................................................110 FIGURE 63 POINT 40 -KAWAWA RD MOROGORO RD INTERSECTION ...........................................................110 FIGURE 64 POINT 41 -UNITED NATIONS RD MOROGORO RD INTERSECTION ...............................................111 FIGURE 65 POINT 42-MSIMBAZI RD MOROGORO RD INTERSECTION ...........................................................111 FIGURE 66 POINT 43 -LUMUMBA STREET MOROGORO RD INTERSECTION..................................................111 FIGURE 67 POINT 44 -BIBI TITI RD MOROGORO RD INTERSECTION ...........................................................112 FIGURE 68 POINT 53-OHIO STREET BIBI TITIRD INTERSECTION ................................................................112 FIGURE 69 POINT 54 -MAKTABA STREET BIBI TITI RD INTERSECTION.......................................................112 FIGURE 70 POINT 55-UHURU STREET X BIBI TITI RD INTERSECTION ........................................................113 FIGURE 71 POINT 56 -NKRUMAH STREET BIBI TITI RD LUMUMBA STREET INTERSECTION .......................113 FIGURE 72 POINT 57 – KAWAWA RD – BAGAMOYO RD INTERSECTION ........................................................113 FIGURE 73 POINT 58 –KAWAWA RD – DUNGA STREET INTERSECTION .....................................................................114 FIGURE 74 POINT 59 –KAWAWA RD – KINONDONI RD INTERSECTION ......................................................................114 FIGURE 75 POINT 60 –KAWAWA RD - MWINYIJUMA STREET INTERSECTION ..........................................................114 FIGURE 76 POINT 61 –KAWAWA RD – MLANDIZI STREET INTERSECTION .................................................................115 FIGURE 77 POINT 62 –MSIMBAZI STREET - SWAHILI STREET INTERSECTION ..........................................................115 FIGURE 78 POINT 63 –MSIMBAZI STREET - MAFIA STREET INTERSECTION ..............................................................115 FIGURE 79 POINT 64 –MSIMBAZI STREET - UHURU STREET INTERSECTION ..............................................................116 FIGURE 80 POINT 65 –MSIMBAZI STREET - LINDI STREET INTERSECTION ..................................................................116 FIGURE 81 POINT 66 –MSIMBAZI STREET – NYERERE ROAD INTERSECTION .............................................................116 FIGURE 82 CBD ORIGINAL DIVISION ..........................................................................................................119 FIGURE 83 NEW TRANSPORTATION ZONES CBD DIVISION ........................................................................120


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ACRONYMS AND ABBREVIATIONS

BRT: Bus Rapid Transit CCSu: Classified Count Survey CBD: Central Business District DART: Dar Rapid Transit DFSu: Directional Flow Survey DBMS: Database Management System DCC: Dar es Salaam City Council DSM: Dar es Salaam FVOSu: Frequency and Visual Occupancy GIS: Geographic Information System ITDP: Institute for Transportation and Development Policy NMT: Non-Motorized Transport ODSu: Origin Destination Survey SBASu: Station Boarding and Alighting Survey VBASu: Velocity Boarding and Alighting Survey


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1. ACKNOWLEDGEMENTS

After a feasibility study on the possibilities of implementing a modern urban transportation

system in the city of Dar es Salaam, formal/contractual agreements between the Dar es

Salaam City Council, through the DART Project Management Unit (PMU), Logit

Consultancy (LOGIT), Inter Consult Ltd. (ICL) and The Institute for Transportation and

Development Policies (ITDP) for the implementation of such a system were set under the

sponsorship of the World Bank.

1.1. OBJECTIVE

This report provides recent information acquired on traffic and demand analysis modeling

surveys conducted in DSM for a period of four months (from April to July 2005). Also it

describes and evaluates the activities developed while doing so as well as providing an

inventory with resources for mutual use by transportation engineers/planners,

environmentalist and other concerned parties.

The main objective to conduct the surveys is to obtain information for assembling the

Transport Model for DSM, calibrate and validate it.

Results presented herein, are a summary of the synthesized surveys information that

might be useful for the understanding of the model.

The completeness of this report is totally dependent on information as reported in the

surveys.

1.2. SURVEYS INFORMATION FOR FUTURE UTILIZATION The organization of the presented surveys information makes it very useful for future

utilization mostly in the urban development plans for DSM.

Future uses of the organized information among others are:

Coordinated subsystems

Usage in establishing new corridors for the road network so as to reach out to all

the DSM citizens, cost effectively.

Usage in allocating appropriate areas for city’s density increase while sustaining


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both urban land use and the transportation system (economical per say and the

overall system) with great attention to the environmental impacts as well.

The data can also be used to assist in extra calibration and validation of the model

in the near future when required; this is to avoid the cost accompanied with

conducting new surveys. Constant updating needs to be carried out for

maintaining the model validity.

1.3. COMPUTER FILES AND CONVENTIONS

Most of the products developed for the model are useful when manipulated with

computers; and without that, there would be no reason for displaying it at the level of

detail they were arranged. Therefore, summary of the information - mostly made to check

coherence of the information or to present the pattern that can be understood at

aggregate level – shall be presented here. Examples (a few rows) of the long tables

delivered together shall be also shown.

Graphics though are a very powerful tool to have understanding of the detailed

information and it would make sense to include all of them as annex to this text.

Files names will be referred between double quotation marks and the three letters

extension shall indicate the format, as the appropriated software to view it.

As many tables will be presented and referred in excel format (“.xls”) a file name

between brackets followed by a name with no space in between and an exclamation sign

after indicates the sheet name in that file. If followed by a capital letter and a number (and

eventually a colon and again capital letter and other number) then it is making reference

to a cell (or a range of cells) within that sheet.

As an example [“Headway.xls”]Simple!B2:H2 is a mention to the conjunct of the cells on

the second row, from column 2 (or B) to column 8 (or H) in the sheet named “Simple” in

the file named “Headways.xls”.

1.3.1. DARTDBS

The custom developed tool for entering and manipulating the surveys data has a

separate manual for users and developers (Volume 3.1 and 3.2), where in the first is of

particular interest the section 7, for the display of results using the “Results module”


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1.3.2. MS-EXCEL FILES WITH MACROS

A few included excel files may contain macros. Unless the software is enabled to open all

macros when opening those, you shall be prompted to confirm if you want to enable

macros: press "Enable Macros...", the source is reliable. If you are not asked that, then...

with excel opened select on your menu bar: Tools... Options... Find the "Security Tab", on

the bottom of it there is something like Macro Security and a button with the same label,

when clicking on that button choose Security level and select the middle level option

(“prompt to enable macros”).


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2. INTRODUCTION TO MODELING

2.1. GENERAL

A model is a mathematical representation of the reality: therefore an abstract thing: a set

of conjuncts, rules and procedures that have coherence. As everything in mathematics,

models also can be expressed in terms of diagrams, operations and formulas. This

usually leads us to confuse one model (or one instance of a model) with the model

technique (or with the technique used for creating that instance of model).

A map, for instance, is a very good example of a model, as it is the representation of

reality. We are used to say that the physical instance of the map - the drawn paper sheet,

the hardcopy - is the model itself (again, as it is the representation of reality). But the

model includes also the set of rules we use to read it, as scale, and the ability to identify

different figures in a legend. So it includes the set of rules we use to write it as well.

Why one creates a mathematical model? Why represent reality?

Cause it makes understanding the reality easier. By assuming simplifications in a

complex phenomenon one can select the most relevant aspects for that observation and

assuring that the relation among those characteristics are set in a way it looks like the

reality (yet are not exactly the same but correlate well with ones understand of it) one can

use it to evaluation and planning.

Either long or short term planning: if it is for very short term planning it will look like a tool

to make operational decisions.

Whereas planning becomes an evaluation of different alternatives where in each

alternative there is a setting for certain aspects (a scenario) and observes what effect of it

will produce in the others aspects (all selected as relevant and the rule of relation known).

A model will also allow one to make evaluations in certain aspects of reality without

directly measuring it all the time. It is to say, for creating the model one measures two

aspects and learns a rule that relate them (even that with some simplifications). As


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long as one believes that this relation rule is still valid he can measure only one and

estimates the values for the other one.

Which is the adequate level of simplifications?

Simplification is assumed whatever model is being constructed, even though we may

dare calling a model as simulator.

As for the definition of model: the lesser are the simplifications – both of the

characteristics selected themselves and of the relations among them – the better will be

the reality representation, therefore better the model. In a better model, less simple, one

can observe more detail.

So the adequate level of simplification is the level that will provide enough detail to

observe (plan, evaluate or forecast) the characteristics of interest, under the purpose it

was designed for.

This level is directly related to:

-the costs to constructing and operating the model,

-the costs that errors (in statistical meaning: deviation) on its use will cause

during its lifetime.

Computers and software development has being made possible to go to higher levels of

detail under reasonable efforts, as complex set of rules and procedures are very

appropriated to be manipulated, then evaluated or operated, and again manipulated by a

computer. So we tend to call model output diagrams, or the files used and even the

software that operates it of “the model”. In doing so “the model” becomes an even more

intangible thing and even harder to understand.

So to describe the process to create the a model (the Transport Model for Dar es

Salaam) we shall remember that we are talking of an abstract entity and focus in the

reality we are trying to represent, on its purposes and the simplifications we are applying

to compose it.


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What is the life time of a model?

The immediate and obvious answer is; a model is useful as long as it maintain contact

with reality for the purpose it will be used.

Again we should look to the model considering the two things that compose them: the model technique, which involves the characteristics selected and the definitions of

relationships among them; and the information presented as the model itself, i.e. the

values that fill the selected characteristics.

Using maps for example, a street map in Dar es Salaam will probably become older for

the purpose of finding addresses on the outskirts earlier than it will be considered older to

find addresses downtown. That is because the technique remains the same, but the

information for one purpose gets out of date sooner than the other.

Phases of modeling

Under these points of view there are two distinct phases in the modeling: the construction

phase and the use phase. Yet, one model with very specific purposes may be adapted

(constructed or reconstructed during its use), as the first trial uses may point its

deficiencies and weakness to certain purposes. In the same way, it might be constantly

tested and updated too for longer life.

The first phase of modeling requires an exhaustive exam of reality, and it shall be

extensively hold on surveys. These surveys, which are subject to errors (both errors and

deviations), shall be checked regarding its coherence under certain expected

relationships, while other must be learned.

The second phase requires that, the conditions proposed on the first are being attended,

i.e. the use of the model is on the validity conditions of its creation.

2.2. A MODELING EXAMPLE WITH MANAGING

Let us look for the characteristics of a math model looking in tools that managers are

used to deal with to make a simple and understandable comparison.


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A managing model would select as objects to represent: activities, people, resources, etc.

The relevant characteristics to the modeling would be time duration of tasks, capacity and

productivity of people, prices (salaries), and the relationships among those things: who is

able to do which tasks (or how much time each person would need to accomplish each

task), what resources each task require, what are the required sequence of this tasks.

We may, properly consider a PERT (Project Evaluation Review Technique) with

appointed resources as a management modeling technique. Its objective is planning /

monitoring / evaluate the organization of resources during a project implementation.

A PERT model can be represented by precedence diagrams (where the activities are the

nodes or the milestones are the nodes) and Gantt charts. Activities for each human

resource can be presented as an schedule, yet the diagrams themselves are not the

model, the model is the relationship among the activities.

For a 10 months project overview, for instance, it is not necessary to point buying pencils

an activity, it would be a too high level of detail. While in the other hand, stating 6 months

to accomplish infrastructure it would be too much simplification. The steps in a

controllable size and where precedence can be pointed are the adequate level.

The time needed to accomplish a certain activity shall be based on experience (or in

surveys of productivity) for doing that task. The same way restrictions of precedence shall

be based on the availability of a certain resource, instead of in the need of a task done, it

is the modeler knowledge of reality (and also of the modeling technique) that will decide

among these things.

The model shall last as long as the project, and it might be eventually applied to similar

projects with few changes. Also, during the process of using the model the manager may

modify the model, by changing the allocation of resources, the time needed for a task, or

even inserting a new one.

Software, like the MS-Project among others, may be an adequate tool to manipulate and

perform operations on a managing model, yet they may lack certain characteristics a

manager would like to directly represent. (S)he can find ways to include them there. So it

can be called a modeling tool, yet the model is represented there, is not the tool that is

the model, nor the computer files that compose it.


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Similarly, the EMME2, the TransCad, the Tranos or the Saturn are software that create

the proper environment to compute the types of rules that apply to a Transportation

model and to use it, like a pencil and a paper and a ruler are appropriate to draw a map,

or later mark a path on it and estimate its distance.

The model itself is more the selection of characteristics we choose to observe in the

transportation system and the measures we have got.

2.3. TRANSPORTATION MODEL

The most relevant entities to a Transportation model are the people and the places where

they are going to develop their daily activities, the transport demand; and how they

behave when confronted with different transport conditions, the transport supply.

The demand for transport is the so called Origin/Destination Matrix (O/D Matrix). Another

characteristic of the demand as the human activities change daily, so does the O/D

Matrix. But before moving to the simplifications and restrictions to construct this model,

let us look to some basic characteristics of interest. While on the scope of the model

developed we limited ourselves to Public Transportation, on the first approach we will be

considering people needs to move around: walking, by bicycle, by private vehicles or

commuters, and the need for moving goodies or loads shall be represented by one

person need to move along with it. How these aspects were incorporated and reflected in

the use off streets on our model shall be discussed after.

2.3.1. MAP

A transportation model is constructed upon a map, which is also, the better way to

represent most of its aspects.

The map will basically represent the road network, which by its turn is composed by two

key elements: points and links.

Points should represent physical locations in the city where people develop their

activities, places where they want to come from and go to.

Links are the paths people (buses and cars) can use to go from one point to another.


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In the maximum extreme detailing situation, we would create one point for each existing

house, store, school, office, park, or for each buildings and facility in the city. But in a

more practical level of detail situation, the points end up being each corner between two

streets, the end of a street and eventual points created in the middle of too long streets or

blocks.

Figure 1 Satellite picture: Dar es Salaam, from Google Earth.


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Figure 2 Detail from satelite picture

Figure 3 Map from Dar, showing constructed areas


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Figure 4 Model with extreme level of detail (considering each building)

Figure 5 Model with very high level of detail (considering the addresses)


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Figure 6 Model with high level of detail, considering the intersections

Figure 7 Model with practical level of detail, without last walking link to reach origin or destination


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So, whatever is the level of detail, the points input into the model become more or less

defined like the junction of two links, while links are supposed to be the “link between

points” And then it is very common to refer to the road network as one thing, calling the

points nodes, and defining a link by mentioning two nodes. Using streets intersections is

a common way to easy refer to the nodes (yet after working a while with a model one

start to refers by heart by the numbers nodes), and it is as natural as describing an

address. For instance, let us say: Morogoro Road street, between Sokoine Drive and

Samora Avenue points a link (the link between the nodes ‘Morogoro Road X Sokoine

Drive and Morogoro Road X Samora Avenue). At the same time it is easy to point places

where people are coming and going to by providing the near by corner. According to the

convenience of the model purpose and construction, eventually representing only major

streets (and corners between those) may be appropriate

The geometric visualization of this is a map, as we are used to see: the difference is only

that the intersections are given names (and/or numbers) to identification. Considering a

Geographic Information Software (GIS), the required basic input to have this map

inserted, so to in future development become able to make reference to its entities, we

are creating the two following tables:

Table 1 Example of Nodes table – Eastings and Northings can be any convenient XY coordinates system.

ID NAME EASTINGS NORTHINGS 1 N001 526326.300 9247643.250 2 N002 526236.280 9246833.070 3 N003 526861.200 9246938.090 4 N004 527991.670 9246863.080 5 N005 529247.950 9245780.340 6 N006 530147.150 9246050.400 7 N007 530967.330 9246280.450 8 N008 530857.300 9246920.590 9 N009 531397.420 9247370.690

10 N010 531707.490 9247040.620


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Table 2 Links table – Length might be greater than the distance between the nodes

ID NODE_A NODE_B LENGTH 1 N001 N002 843.750 2 N002 N003 397.729 3 N003 N004 1900.187 4 N004 N005 1666.688 5 N005 N006 974.890 6 N006 N007 887.362 7 N007 N008 627.977 8 N008 N009 715.838 9 N009 N010 471.725

10 N010 N011 486.766 11 N011 N014 163.060 12 N014 N015 665.128 13 N015 N007 464.451 14 N098 N097 1090.167 15 N097 N095 1026.991 16 N095 N038 1922.292

The above table examples refer to Dar es Salaam, and can be found on

“NetworkStartUp.xls” (that was developed by Aisha on July/04). Plotting all the given

nodes, we will have the graphic visualization, on the next pages, where one, familiar with

the city the main roads can complete the links on his mind:


20

Figure 8 Nodes of “NetworkStartup.xls”, without identification.


21

Figure 9 Nodes of “NetworkStartup.xls”, with identification.


22

In [“NetworkStartup.xls"].Constructor! one interesting exercise can be made to construct

Dar es Salaam road network, by adding in columns B and C origin and destination nodes,

as the graphic there is automatically updated.

An exercise to understand this constructor is to fill row 35 filling cell B35 with N099 and

the cell C35 with N100 (capital N is required) to create a link that represents Old

Bagamoyo Road, and/or N074 (in B36) and N076 to C36 to create the link in the

Peninsula. The graphic chart shall change from the left figure to the right figure:

Figure 10 “NetworkStartUp.xls”Construct! – Adding two new links

Observation must be made, about the fact that the extension of a link is eventually bigger

than the distance between the nodes, as the path between the NODE_A and the

NODE_B, may not be straight. When it comes to improve the visualization (the map), it is

useful sometimes include some dummy nodes. Find bellow visualization of a road

network with and without dummy nodes.

Figure 11 Road Network without Dummy nodes Figure 12 Road Network with Dummy nodes


23

In fact, to correctly describe this information to a model it is necessary to provide/create

one link per way of moving, having a starting node and an ending node (one common

way to deal with it is use the vector notation, calling link minus x, the link that goes

opposite way to link x).

To make it clear, let us imagine, in the above example, that a few streets are only one

way, as shown on the figure on the right, let us then reproduce how to input this into a

database for modeling.

Let us first insert numbers to identify the nodes and then one grid, to point the nodes

positions, as in the enlarged picture bellow:

The table with coordinates to create the nodes to the model is as bellow:


24

Table 3 Nodes table to model map as example

Node_ID X_Coordinate Y

Coordinate 13 16430 26375 14 16835 26370 17 17212 26440 23 16433 26090 24 16835 26090 27 17339 26185 28 17580 26300 33 16435 25810 34 16835 26805 127 17110 26060 128 17540 25930 134 17285 25675

To input all the visible links correctly (considering that some are only one way) we could

use both the next two tables:

Table 4 Links – alternative 1: declaring if links are two ways

Link Node_A Node_B Two

ways? 1 13 14 Y 2 14 17 Y 3 13 23 Y 4 14 24 Y 5 17 27 Y 6 23 24 Y 7 24 127 N 8 127 27 N 9 27 28 N 10 23 33 Y 11 24 34 Y 12 33 34 Y 13 28 128 N 14 128 134 N 15 134 34 N


25

Table 5 Table of links – alternative 2: declaring every one-way link.

Link Node_A Node_B

1 13 14 2 14 13 3 14 17 4 17 14 5 13 23 6 23 13 7 14 24 8 24 14 9 17 27 10 27 17 11 23 24 12 24 23 13 24 127 15 127 27 17 27 28 17 23 33 18 33 23 19 24 34 20 34 24 21 33 34 22 34 33 23 28 128 25 128 134 27 134 34

If considered the right table, the proper visualization would be like the following picture:


26

The matter with the previous network is that it considers only vehicles movements, in

practical the models always need to have the links going and returning, cause

pedestrians can go in both ways, yet sometimes we may find some links where

pedestrians are not allowed to go, like toll roads, tunnels or bridges). So, when declaring

links we shall in fact declare both ways them, but then add the information of what kind of

traffic will be allowed on that link. The table and map for describing, declaring existing

links on our network shall be like the following figures:


27

Table 6 Links with atribute type of traffic

Link Node_A Node_B Type

of traffic

1 13 14 V,P 2 14 13 V,P 3 14 17 V,P 4 17 14 V,P 5 13 23 V,P 6 23 13 V,P 7 14 24 V,P 8 24 14 V,P 9 17 27 V,P 10 27 17 V,P 11 23 24 V,P 12 24 23 V,P 13 24 127 V,P 14 127 24 P 15 127 27 V,P 16 27 127 P 17 27 28 V,P 18 28 27 P 17 23 33 V,P 18 33 23 V,P 19 24 34 V,P 20 34 24 V,P 21 33 34 V,P 22 34 33 V,P 23 28 128 V,P 24 128 28 P 25 128 134 V,P 26 134 128 P 27 134 34 V,P 28 34 134 P

The table is then an example table of links declaration, including the attribute ‘type of

traffic’:

P stands for pedestrian

V stands for vehicles.

When assembling the road network, we are thinking of possible ways to people move

around the city, and considering that we set properties (or attributes) to the links to play

with it (and so we can use computer to make calculations), like the type of traffic allowed

there.

Properties or attributes we will add to links and nodes will be discussed next, but still

keeping in mind the map system, let us familiarize with the simple way to point paths or

itineraries upon the map with this codification: a possible itinerary upon this map


28

may be described with a list of links, once it is assured that the ending point of a link on

the list is the starting point of the next link on the list;

or alternatively with a list of points, assured there is a existing link between one point and

the next point on the list.

So the visualization of an itinerary will be a continuous line from a starting point to an

ending point passing thru links and points. The way of codify a bus route will be present

the list of links it goes thru, like in the following example, upon the previous picture:

Imagine this route, named “Route EX” comes from north from point 14, makes a loop in

this area and then return, as the next picture shows:

The part of its itinerary that goes in this part of the road network we create can be

described as or using the previous link table.


29

In the same way we may describe an itinerary from one person move from point 34 to

point 17 as a succession of links (20, 8 and 3) or points (34, 24, 14 and 17).

Regarding routes and itineraries, one more thing shall be considered before moving to

the characteristics of the model entities: it is the matter that one route usually has two

starting/ending points, i.e. they represent in fact two routes (unless they are circular lines)

or itineraries: one for going (for instance: from Mwenge to Kariakoo) and one for returning

(from Posta to Kariakoo). The most common thing is that the going itinerary is the

opposite of the returning one (as they are planed to serve the same public), but there

may be differences, regarding some streets (specially one way streets). So when

referring to routes, we shall, in most of the cases divide them in two to point out their

characteristics.

With this things organized we can proceed to describe the characteristics that matter to

be described (selected, quantified and associated) to each map element.

2.3.2. POINTS

The characteristics that matter to one point are the trips that will start or end there during

the time modeled. It can be thought of the number of people who live there or go there to

develop any activity and that pass thru the point on their way, during the period of time

the model intend to represent. The number of people who passes thru one point, though

is an information in which we are interested in, is not as important as trips generated or

attracted to the point, cause it is not a user need pass that point; computer tools upon the

model shall estimate the flowing traffic in every point once other characteristics are

known and that shall be compared to some known flows to check if the tool is working

well.

As we could see in the example pictures, we have 2 different functions for the nodes in

the model:

Passing thru nodes,

Origin and/or destination nodes.

Models can be constructed in 2 alternative ways:

allowing nodes to have the 2 functions (as in Figure 6 Model with high level of detail, considering the intersections and Figure 7 on page 17).


30

in a way that a node will have only one of the two functions.

The first alternative will require less nodes to be created and would have lower level if

compared with the second alterative, for a given same area (compare top and bottom

pictures on pages 17).

But if the comparison parameter is the total number of nodes, which is usually a

computer software and hardware restriction, than you would have higher detail using the

first alterative (this is: with the same number of nodes you can include more intersections

on your model, as you don’t have to include nodes to inside the blocks).

The second type is easier to control, though.

2.3.2.1. PASSING THRU NODES

The characteristics of interest for a passing thru node would be the number of people

and/or cars that could pass thru the point, at total, no matter the direction, this would

represent traffic conflicts, managed or not by traffic lights.

A table with the percentage of green times for each approaching link would be the data

necessary to represent it, considered the traditional 4 phases traffic lights programming

we usually see in Dar. If there are distinct movements in the same approach (and more

complex traffic management on the intersection), then the information to be stated is the

green light time percentage for each allowed vehicles flow, i.e. the pair (approaching link;

departing link) on that intersection. In this last case, information regarding the number of

lanes to permit that flow movement, if that is defined, would also be necessary. This

information would be used to estimate the total number of cars that can turn (or go

straight) in each direction (i.e. capacity and not demand), so alternatively this information

could be provided to the model, like in the following picture.


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Figure 17 – Passing thru node – intersection example

Though this information can be easily gathered and included in a model, processing it

requires a reasonable amount of time, as it corresponds in creating more 3 nodes and 12

links at each intersection, as shown next:

Figure 18 – Node without modeling intersection, and node modeling intersection

So unless the model is for traffic management purposes, it is easier to group the capacity

of the approach link, regardless of how the flow will split at the intersection. And this will

be considered when selecting link attributes. Considering that, a passing thru node has

as attribute only its location (coordinates) yet is the basic element for the construction of

the network.


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2.3.2.2. ORIGIN AND/OR DESTINATION NODES

For an origin and/or destination node, the needed information is the number of trips that

will start and/or end at that point.

Before considering any restriction or simplification, the wanted information would be

every people who comes from and goes to the area the point represents, where these

people will come from and where these people will go to, and how much people are

paying or willing to pay for these trips and for last when each trip will happen along.

Before considering any restriction or aggregation we could imagine that for each given

point of the model, we would like to know a list of the trips that start there: when they will

happen and where they will lead to.

So, ideally we should have a list like the following presented for two points (the first would

be a house, the second an office), for every and each origin point on the network:

Table 7- Data Example for one O/D node

ORIGIN HOUR DESTINATION MODE PURPOSE TRAVEL TIME

FARE PRICE

06:45 POINT 11234 PUBLIC BUS SCHOOL 00:25 200

06:50 POINT 23490 WALKING SCHOOL 00:10 0 07:30 POINT 04450 PUBLIC BUS WORK 00:45 250

POINT 3420

08:25 POINT 07325 PUBLIC BUS MEDIC 00:30 200

12:20 POINT 03450 WALKING SHOPPING 00:10 0

11:45 POINT 11345 WALKING LUNCH 00:05 0

12:00 POINT 13454 PUBLIC BUS SHOPPING 00:15 100

12:35 POINT 12304 WALKING LUNCH 00:07 0

POINT 17:15 POINT 45236 CHARTER BUS BACK HOME 01:40 250

17:15 POINT 03420 PUBLIC BUS BACK HOME 00:55 250


19:00 POINT 05237 PRIVATE CAR BACK HOME 00:35 150

11234


This information put together for every point will compose what we call the

Origin/Destination Matrix (O/D Matrix), which can be aggregated or split in several

different matrix (by purpose, mode, time intervals or grouped points within an area).

Despite the fact we call it a matrix, it is much easier to represent it as a list. See or

represent the O/D Matrix in a matrix format it would be in fact a waste of space (either in

paper or memory) because the matrix would be full of empty spaces (or zeros), i.e.


33

representing this as a matrix would be write on the top row of the table every existing

possible Origin point and on the left column every possible Destination point; and inside

the matrix, the number of trips that occur from a given O/D pair (for the selected period

that the matrix would represent), as there are trips between many points that does not

occur (and we create a space to store every possible pair), there shall be many empty

spaces.

When restrictions for the model start to be applied, and according to the purpose of the

model the selection of the proper aggregating level becomes clear. When we start to

aggregate nodes, each node becomes the representation of a zone.

2.3.3. LINKS

The final characteristic of interest for a link is the travel time to cross it. That is, after all

what will affect the transport decisions for users. But the time (or given a length, the

speed) to cross the same link depends of:

-mode of transport used (and if public transport, the vehicle and the route),

-the day and the hour of the day.

Additionally, it is necessary to consider the intersections where a link ends, once it was

decided to ignore the intersections problems at the nodes, as commented before.

The time (or the speed) is related to the number of people using the road, or it could be

said that capacity is related to the speed. The information to be provided to a model,

regarding links, has to be such that, for every possible number of vehicles on it, the time

waste for ride along it can be estimated.

The capacity-velocity curve is, among other things, function of:

-distance (length of the link),

-width of the road (which is refereed as width of the link),

-the pavement (and the condition of the pavement),

- parking allowance,


34

-land use in the surroundings.

Except for the distance, that is set by the model the other characteristics are more or less

defined with the use function, or class, of the road: local, collector, arterial, highway, and

in the absence of better survey or information (or because the level of detail can be

lower), that classification, or a similar one may be enough.

Alternatively, when these characteristics, or the relation of those with the speeds are not

very clear, it would have exactly the same effect, furnish a table for different measured

volumes, or for different hours of the day, the time required to cross the link, according to

the type of vehicle (and/or route). As routes goes thru different links, the appropriate

place to attach the speed for a route in a link is be the route. The information to be

provided with a link would then be, if not all mentioned above:

origin node and destination node,

distance

pedestrians average speed or time to run thru all the link,

cars average speed or time to run thru all the link,

public routes average speed to run thru all the link,

if any other mode is available, an average speed for that model.

Where the last four items shall take in account time waiting to pass thru the destination

intersection, providing speeds is a way to provide information about the allowed traffic

(giving zero speed where it is not allowed). Regarding pedestrians, we may assume an

average speed for walking (4 km/hour), and implement extra links for pedestrians only to

represent pedestrian bridges or crossway paths thru an intersection. On account of need

to simplify the network or the information to input, the same procedure can be adopted to

cars (with speeds defined for each road class) and buses. For the same reason links that

represents the last walking distance to reach a location may have other distances than

the real ones to try to represent the average distance to reach locations.

An example table with information that will allow to determinate road class for the links,

and then set average speeds for each type of traffic is shown below:


35

Table 8 Simple Links table example, for “NetworkStartup.xls”.

ID NODE_A NODE_B BIDIRECTIONAL LANES PAVED LENGTH RDNAME 1 N001 N002 Y 1 N 843.750 Mburahati 2 N002 N003 Y 1 N 397.729 Luhanga 3 N003 N004 Y 2 N/Y 1900.187 Kigogo 4 N004 N005 Y 4 Y 1666.688 Kawawa 5 N005 N006 Y 2 Y 974.890 Uhuru 6 N006 N007 Y 2 Y 887.362 Uhuru 7 N007 N008 N 4 Y 627.977 TitiMohamed 8 N008 N009 N 4 Y 715.838 TitiMohamed 9 N009 N010 N 4 Y 471.725 Maktaba/Azikiwe

10 N010 N011 N 4 Y 486.766 Azikiwe 11 N011 N014 N 2 Y 163.060 Posta 12 N014 N015 N 2 Y 665.128 Sokoine 13 N015 N007 N 2 Y 464.451 Uhuru 14 N098 N097 Y 2 N 1090.167 Kisukulu 15 N097 N095 Y 2 N/Y 1026.991 Kimanga 16 N095 N038 Y 2 Y 1922.292 Kimanga/Tabata 17 N095 N096 Y 2 Y 2418.832 Tabata/Segerea 18 N038 N039 Y 4 Y 2082.442 Mandela 19 N039 N044 Y 4 Y 2165.310 Uhuru 20 N044 N041 Y 2 Y 704.139 Uhuru 21 N041 N005 Y 2 Y 75.621 Uhuru 22 N041 N042 Y 2 Y 338.713 Tabora 23 N042 N043 Y 2 Y 833.815 Lindi 24 N043 N044 Y 2 Y 580.231 Bungoni 25 N039 N040 Y 4 Y 621.725 Mandela 26 N040 N082 Y 4 Y 1896.293 Nyerere 27 N082 N084 Y 4 Y 56.278 Nyerere 28 N084 N085 Y 2 N/Y 2402.104 Bombom 29 N084 N086 Y 4 Y 1472.586 Nyerere 30 N086 N087 Y 2 Y 695.138 Jet/Lumo 31 N087 N088 Y 2 N/Y 1790.130 Jet/Lumo 32 N088 N085 0 0 N 1076.434 Lumo/Relini 33 N084 N083 Y 2 N 1112.379 Vingunguti

Another way to display this information, more GIS (geo-referred) like is exemplified

bellow (from the Jakarta city).

Table 9 Geo-referred links’ table example (from Jakarta model data)

On the above table, each group of 5 rows provide information for one street (if more

detailed information is to be provided, like the building numbers on that corner or speed

for each type of traffic, a group of more rows could be used). These format


36

organize links by the street name (which for the model itself is not an important

information, but very useful for people that operates the model), informing the

coordinates of the points that form that street, alternatives names for that street (these

clean names, are useful for reading and processing O/D interviews), the length of that

link and the road class: on this example a negative road class is the code to inform that

the link is one-way for vehicles. The example also shows two streets with the same name

in different locations. (EN: raia means large)

2.3.4. ROUTES

Under a network perspective, as mentioned before, routes are a conjunct of links, which

passengers may select to board in order to going from one place to another.

The elements that describe a route to a model are those that a passenger will take in

account at the moment to select how they will do their trip from the origin to a destination.

Points where the route passes and the time of travel among them (speed for the

route in each link).

Points where the route may stop.

Price of the ride: which may eventually change based on the distance

Frequency or headway: number of buses that pass per hour or interval between

one bus and the next one.

Type of bus that does the service: which is basically defined by the size of the

buses, equal the number of passengers may travel seated and/or standing.

Usually a route has two ways, unless it is a circular route (and some times even a circular

route may have 2 ways: clockwise and counterclockwise), one for going and one for

returning. In the real world there is no need to a bus (a vehicle) be assigned to a single

route, but it usually it happens like this for regulatory and control needs, and for

operational reasons the number of trips going is commonly expected to be equal the

number of trips returning. But a second look on reality shows that this things change from

place to place, as well as the frequency is usually different for the same time of the day)

and in modeling exercise it is more practical to split a complete route that goes and return

in two (one forwards and one backwards), yet it is good to maintain the relationship


37

between them somewhere in the database, so to be able to trace that changes in one

may affect the other. This one-way route could be called sub-route, or route-way, but as it

becomes a much more important for manipulation in the model, it is “route” how it will be

common designated the sub-route. A good procedure is to use a same key name to two

sub-routes that share the same itinerary, but going and returning, which can be identified

by an extra letter on the name, like F (for forward) and B (for back).

It is also common that under the same known route, different itineraries may be applied

(the route, via one place or via another place), sometimes it happens like this even the

regulations and licenses does not permit it. Driven by profit, drivers and conductors run

out of their assigned path to avoid traffic once the bus is full, in order to speed-up their

return. It is also common that “unauthorized operational returns” happens in the way to

the finish point, avoiding areas without many customers. If these situations can be

identified, these alternative routes shall figure in the model as different sub-routes too

(the matter is always identify when and where this things happens, and if they are

significant enough to be inserted in the model).

Another problem that is that frequencies (or headways) change along the day and are not

always constant (in fact, these things use to be very irregular), and sometimes there are

different types (sizes) of buses providing the service on the route. One way to fix this, is

provide the average frequencies for an interval, and a regularity coefficient (1 is regular, 0

is all buses of the interval passing together) and the composition of the fleet (or assume

an intermediary bus size that would represent the composition). Speeds and fares may

change along the time, too. When to consider this will depend on constrains to the model.

The table bellow shows an example of data provided to the model, regarding the routes,

this table would be valid only for a certain part of the day, and in this example the speeds

would be associated with the links, not with the routes.


38

Table 10 - Example of routes data

ROUTE_ID ROUTE_NAME LENGTH FROM TO DESCRIPTIO MODE VEHT HEADWAY AV_SPEED LINKS

545 PR001N 12.55 MBAGALA RANG

BUGURUNI MR3BUG b 1 2.94 15 17874 15310 15334 14890 14882 16059

544 PR001S 12.55 BUGURUNI MBAGALA RA

BUGMR3 b 1 2.94 15 59909 14130 13730 15438 15430 15486

85 PR002E 9.55 BUGURUNI KIVUKONI BUGKIV b 1 2.85 15 59711 12804 12940 15995 16003 15979

86 PR002W 7.31 KIVUKONI BUGURUNI KIVBUG b 1 2.85 15 59909 13746 59725 14170 59724 14186

87 PR003N 15.57 BUGURUNI KAWE BUGKAW b 1 10.68 15 47425 47505 47553 47927 47863 47959

88 PR003S 15.57 KAWE BUGURUNI KAWBUG b 1 10.68 15 36536 43362 43466 43482 43506 43934

90 PR004N 7.83 MTONI MTONGA

BUGURUNI MTOBUG b 1 2.29 15 45689 45721 45745 46329 46289 46401

89 PR004S 7.83 BUGURUNI MTONI MTON

BUGMTO b 1 2.29 15 52213 52253 59705 52293 52157 52357

91 PR005N 6.71 BUGURUNI MUHIMBILI BUGMUH b 1 2.00 15 39138 39130 39282 39226 39234 39306

92 PR005S 6.71 MUHIMBILI BUGURUNI MUHBUG b 1 2.00 15 40928 41850 40928 41866 40960 41048

93 PR006E 8.32 BUGURUNI POSTA BUGPOS b 1 2.85 15 39282 39130 39138 39146 40023 40031

94 PR006W 6.12 POSTA BUGURUNI POSBUG b 1 2.85 15 59705 52253 52213 52197 46875 46771

96 PR007N 5.95 TANDIKA BUGURUNI TANBUG b 1 2.41 15 45745 45721 45689 45593 44054 44006

95 PR007S 5.71 BUGURUNI TANDIKA BUGTAN b 1 2.41 15 43466 43362 36536 36472 43386 43250

97 PR008N 35.15 KARIAKOO BUNJU KOOBUN b 1 116.29 15 47553 47505 47425 44658 22534 22518

98 PR008S 35.15 BUNJU KARIAKOO BUNKOO b 1 116.29 15 8831 9705 10822 10598 59922 59935

100 PR009N 24.29 MWENGE BUNJU MWEBUN b 1 1.30 15 19343 19423 59920 59921 19864 21037

99 PR009S 24.29 BUNJU MWENGE BUNMWE b 1 1.30 15 21742 22068 22204 22212 22196 22494

446 PR010N 25.96 GONGO LA MBO

KAWE GONKAW b 1 11.88 15 59724 14170 59725 14890 14882 16059

447 PR010S 25.96 KAWE GONGO LA M

KAWGON b 1 11.88 15 16003 15995 12940 15438 15430 15486

Transform the information into this format (in what concern giving link numbers) is a task

that can have great help from GIS, if the links are entered with street names reference or

from modeling software, if there is not very good geo-coding (assuming the data enterer

has knowledge of street names).

So, having the data gathered like the following table, is good information for developing

the model.

Table 11 Routes itineraries to geo-referenced data (from Jakarta database).

No Rute MED079 MED079 MED020 MED020 MED085 MED085

LEBAK BULUS - BLOK M

BLOK M - LEBAK BULUS

LEBAK BULUS - SENEN

SENEN - LEBAK BULUS

LEBAK BULUS - KALIDERES

KALIDERES - LEBAK BULUS

FROM LEBAK BULUS BLOK M LEBAK BULUS SENEN LEBAK BULUS KALIDERES TO BLOK M LEBAK BULUS SENEN LEBAK BULUS KALIDERES LEBAK BULUS

1 TERMINAL LEBAK BULUS

TERMINAL BLOK M TERMINAL LEBAK BULUS

TERMINAL SENEN TERMINAL LEBAK BULUS KALIDERES

2 PASAR JUMAT SULTAN ISKANDARSIAH

PASAR JUMAT STASIUN SENEN PASAR JUMAT DAN MOGOT

3 CIPUTAT RAIA MELAWAI CIPUTAT RAIA KRAMAT BUNDER CIPUTAT RAIA RAWA BUAIA 4 KARTINI PANGLIMA POLIM KARTINI SENEN RAIA KARTINI BOJONG RAIA 5 METRO PONDOK

INDAH FATMAWATI TB SIMATUPANG KWINI 1 METRO PONDOK INDAH KEMBANGAN UTARA

6 SEKOLAH DUTA RAIA TEROGONG RAIA WR JATI BARAT 1 DR ABDUL RACHMAN SA

SULTAN ISKANDAR MUDA KEMBANGAN RAIA

7 TEROGONG RAIA SEKOLAH DUTA RAIA MAMPANG PRAPATAN KWITANG TEUKU NIAK ARIF PESANGRAHAN

8 FATMAWATI METRO PONDOK INDAH HR RASUNA SAID ARIEF RACHMAN

HAKIM HAJI KELIK SRENGSENG

9 PANGLIMA POLIM KARTINI HOS COKROAMINOTO TUGU TANI SRENGSENG HAJI KELIK

10 BARITO PASAR JUMAT JOHAR MENTENG PESANGRAHAN TEUKU NIAK ARIF

11 MELAWAI TERMINAL LEBAK BULUS SRIKAIA CIKINI KEMBANGAN RAIA SULTAN ISKANDAR MUDA

12 SULTAN ISKANDARSIAH KIAI HAJI WAHID

HASIM MOCH IAMIN KEMBANGAN UTARA METRO PONDOK INDAH

13 HASANUDIN PRAPATAN HOS COKROAMINOTO BOJONG RAIA KARTINI

14 PALAT SENEN RAIA HR RASUNA SAID RAWA BUAIA CIPUTAT RAIA

15 PALETEHAN DR WAHIDIN MAMPANG PRAPATAN DAN MOGOT PASAR JUMAT

16 SUNAN KALIJAGA TERMINAL SENEN WR JATI BARAT 1 KALIDERES TERMINAL LEBAK BULUS

17 SULTAN HASANUDIN TB SIMATUPANG

In the table above, each row refers to 1 route to be modeled, where in top row the

number of the route appear (as in the bus, so you find going and returning, two different

routes for model appear with same number), the name of the route appears in the second

row, then origin and destination in the FROM and TO rows. The following rows show the


39

names of reference points of the itinerary. A GIS application can transform this table in

itineraries expressed by links and/or points.

For Dar es Salaam, Karl together with Enoch, Aisha and Simon first developed an

access database where they booked resumed these information (among others, like

owners and fleet) as registered by the DRTLA manually, in a very self explanatory

template (“Daladala – NODEL.mdb”), from where it is copied the following itinerary. (The

main roads were divided in numbered segments, and some streets were grouped in

areas).

2.3.5. RESTRICTIONS

Defining the limits of a transport model is a hard task, both in level of detail and in the

size of the area the time to be covered. Computer and software power, survey capacity

against costs and terms to produce the model have to be taken in account, and of course

the purpose of the model. As trips changes along the time the model will be valid to use

to represent reality of a certain area, during a certain period.

Provided the previous information, before considering any constrains, the model

operation will be basically a tool that is able to reproduce the user decisions, i.e. select

best way among many options, to go from one place to another, considering what is

called generalized cost: time and distance to walk, time waiting for buses that attend the

destination, fare (converted to time based on the value of time for the user), time in the

bus. The model shall be able to add up probably decisions would be made by all users of

the transport system determine the final condition of the whole system.

The part of problem that selects the best alternative to a single user, under a known

condition, for a single trip is called “shortest path assignment” and the algorithm to solve

this kind of problem is very clear: it is about to trace the options to reach a location. It can

consider inclusive probabilistic problems like: “if I walk to street A, that will take 7

minutes, I can catch routes 1, 2 and 3, that have higher frequency, so I will probably wait

less than 2 minutes and if I walk to street B, which will take 3 minutes, I can catch only

route 4, and will have to wait, in average 6 minutes”. And plus make considerations about

the chance of this user be traveling seated or standing to make this decision, and the

chances of being in route 1, 2, 3 or 4.

The part of adding all these decisions from all users in the system may eventually change


40

the initial conditions of the system, and the decisions need to be reviewed. So there is

need to perform iterative simulations that reach the final situation, where the decisions

taken don’t unbalance the system’s initial conditions (when the decisions contribute to

describe the conditions of the system, it is balanced).

As the computer program techniques are, more or less, known and available the main

limiting factor is the capability to input data: organize, survey and process the information.

2.3.5.1. SPACE RESTRICTIONS

The model shall limit itself to a certain area as well as it shall be limited to a certain level

of space detail. Shall one origin/destination node represent each building? Shall it

represent a block? Or a whole neighborhood can be represented? What is the size of the

zone the node will represent?

As the area considered in the model broads, more people get involved as travelers to that

area and a broader area has to be involved, and so on. For instance, in Dar es Salaam,

could the model be limited to abridge only the surrounding areas where potential BRT

corridors will be constructed? No, it shall not be that way, as there are people traveling

there that comes from all around the city, and the BRT will affect and be affected by a

much larger area.

If our transport model is, for instance, to study the pedestrianization of a downtown area,

inside that area and in a very near surrounding we would be interested in having one

node representing each building (at least each large building). Away from downtown,

after a certain limit we could create one node for each road that is used to access the

downtown.

In a transit model for a whole city, a good criterion to determine the size of zone is more

related to the population of users that will be found there than to the physical size itself.

Statistically it is interesting to have the populations the same size, yet maintain same

characteristics regarding use of soil, to be able to survey samples, to expand samples

and project growth for the future. Near outside cities may be created as zones and/or

alternatively consider trips from there as starting in an intercity terminal (for public

transport users).

In the model we will detail bellow, we involved completely the Municipality limits, but as

we get away from the city center, the level of detail start to diminish (links get longer, and

nodes represents larger areas). As surveys have shown that the users of public transport


41

outside this area had no expression (less than 0,5%) were ignored. In the city center we

reach the maximum level of detail.

Before moving to the problem of limiting time, it is useful to consider an integration of two

different models, with different levels of detail, as for instance: one with a macro view that

does not consider intersections at detail and one with a closer (micro) view that considers

intersections. The second could be applied to a single intersection or for a smaller area,

and does not need to involve (therefore be feed with) information regarding the whole

city, while the first one, used to understand the use of routes and transit lines would

require that. Once it is possible to foreseen (calculate) the outputs in a intersection for the

second one (which is basically a delay on travel time) without really knowing what is

really happening there (queues, optimized cycle time, turning lanes), the first one may

provide input for the second. When running the more detailed model, feed back may also

be provided. In the same way models regarding bus operations, station simulators can be

fed and feed back.

2.3.5.2. TIME RESTRICTIONS

The intention or need to do a trip is associated with a time during the day, that one

person will do that trip. As human activities require that people be at the same places at

the same time there is a concentration of demand for transport in certain times: this

causes the visible and known phenomenon of the peak traffic hours in the morning and in

the afternoon.

Regarding the flow of time, limits too, have to be established. Even before the first

question to determine the valid time limits to a model (the period it represents on a

selected area), the matter to be thought is the most relevant matter about transport: that

traveling is not immediate, once the decision to travel is made and the travel starts, that

user/traveler will be using different resources available at different times (at

7:00 it will be using the roads near home, at 8:00 will be reaching roads downtown).

Then, the (visualization of) use of resources (user loads on links, buses, stations,

intersections) is dependent of time.

The level of detail for our model, under the time perspective is regarding the definition of

times intervals (time steps). Let us assume that given a starting time for a trip and its

origin and destination, it is easy to determinate the exact instant when each resource

will be utilized (instead of dealing with the fact that it is the most probable instant that is


42

easily calculated), shall the model have storage space to retrieve and represent the

status of the transport system each second? Each minute? Will be enough if we can

display it every 15 minutes? Then, shall we consider the use of one resource at 7:00, as

simultaneous with the use of the same resource at 7:14? Shall this use be added up? If

so, a user that alight from a bus, walk one block and board another one that goes thru 5

links during this interval will be simultaneously found consuming all this resources… it will

be like have one person at many places on the same time.

The tool to minimize difficulties of this nature is take out the time dimension

(adimensionalize) from the selected characteristics, as we already exemplified,

expressing most of the characteristics divided by time, expressing them in buses per

hour, vehicles per hour, passengers per hour, both to offer and demand. Even if

measures are split in 15 minutes or 5 minutes intervals during surveys, that data shall be

expressed in the equivalent per hour, showing productivity. Like that, one user “being in

two places at the same time” is to be compared with the availability of the resource during

an interval he will be effectively there.

On the other hand, if we can get a picture of the status of the system in whatever instant

we want (if a dynamic simulator tool is available, it would be able to perform calculations

for that), what instants should be requested for analysis? The loads on the system will

look like moving waves in the morning, flowing to the city center (imagine pictures

forming a movie). In the ideal modeling situation we should request it every minute or

second (or every instant the use changes, as doing a graphic), and locate the total and

maximum loads during the studied period (relationships between the area and the values

of the graphic) in every component. Then the matter becomes: what are the relationships

of interest.


43

P02 - KIVUKONI FRONT

67 8910 11 time

Figure 19 Flow of passengers on a point of control.

Above is shown an example of graphic of instant flow of passengers all along the

morning where the relationships between intervals must be established.

Still on the subject of relating different times, the model shall relate how we compare the

data at 7:02 in the outskirts with the data at 7:55 downtown?

Then, consideration is to be made about the kind of the simulation will be made, static

(user in all places at the same time or instant trip) or dynamic (moving pictures for every

state, every time any element in the system status changes), and how the information

was gathered, what about its precision and deviations?

The period of validity of the model, or the moment of the day, of the week, or of the year

that the model shall be valid, the matter is the data that will be gathered regarding

speeds, headways and O/Ds, cause the geographic features (links, zones and itineraries)

are the same for the city.

To design and improve operational aspects, the hours of interest are more the peak

hours of peak days, cause this hours are determinant to size of fleet, streets, sidewalks,

intersections and stations. To size the operations evaluation of the whole day, and

weekends during the busiest season is the interest. To financial studies, an average day

for the year is the interest.


44

Looking for a model that represent the typical day (work day, during school time), the first

simplification will be made is to assume that activities are similar for this days: even

conditions of supply (times of travel, comfort conditions and costs) changes a little daily,

and people does not go to the same places every day. That is because a large part of the

activities and trips repeat daily, during the week days (to go to work, school and back

home) while those that are not repeated by the same person, shall be done by another

person in very similar conditions (go to shopping, hospital, dentist, courses, for

commercial contacts). So the total number of trips from and to a certain area shall be

similar every day and strongly related to the soil use on those areas, and slowly change

as the use of soil characteristics change there. And the density and social and economic

characteristics of an area shall be characteristics we will use to control a transport model.

And so, to use the model for foreseeing future scenarios, the city development shall be

observed.

2.4. TRANSPORTATION MODEL VALIDATION AND PROCEDURES

As shown thus far a transportation model is like any other model an abstract

representation accompanied by a set of conjuncts, rules and procedures that have

coherence in order to help understand the reality easier. A model can be made up of a

number of models so as to gain its completeness while reducing its complexness as well.

Thus modeling should be a process to using all the available information in order to

represent the required concept mathematically (with numbers, equations, geo-data) to

allow for the best alternative selection for the topic in question.

A model simplifies the examination of a universal matter by putting into a simple

representation. In precise a model facilitates a complete and detailed analysis of the

problem domain.

The most relevant entities to a Transportation model are the people and the places where

they are going to develop their daily activities, the transport demand; and how they

behave when confronted with different transport conditions, changes in the underlying,

transport network.


45

A transportation model is constructed on a map of the particular; the DART system model

is developed on the map of the city of DSM.

Four steps/models, trip generation, trip distribution, mode choice and route choice are

used to develop a transportation model.

A transportation Model just like any other model, has to be valid in order to present what

it should in a reliable way. In order for this to be possible, accurate information should be

used to assemble the particular model.

An Incremental validation approach can be the best in validating a model, which is,

applying evaluation and reasonableness checks during the process of calibrating each

individual modeling step.

After each of the four model/sub-models of a basic transit model is validated, the overall

model is validated and best transportation system set-up can then be brought about after

evaluations of the available alternatives.

Thus to ensure the accurateness of the overall model, the constituting 4 sub-models

should be validated (against reality data) separately while the process of their

establishment and calibration is underway. This cannot be data apart from the actual

field survey data.


46

With which this suggest that each of the sub-models of the transportation model should

have its own set of survey data to calibrate and validate with.

For the processing carried out, the team supported the analysis on software packages

such as Database Processors, Spreadsheets, emme2, TransCAD, etc.


47

3. DATA COLLECTION ACTIVITIES

Once it is understood what we expect to get to and from the model, the

development of the survey work plan took place, taking in consideration the

following requirements: -Surveys were in the critical link for the project

development, and the time assigned to conclude the field work was 2 months.

-The field surveys would require figures around 100 people and 20 working

days, this people would need to be trained, hired in a temporary basis, so paid

by work day (at maximum on a week basis) and mobility was essential to

-The planning for surveys were dependant on previous surveys results,

therefore fast data entry and processing was essential

-For managerial purposes, responsibilities had to be very clear and the

complete infrastructure set before the ‘field activities’ start.

3.1. WORK PREPARATION Activities to be developed:

-Field Preview

-Compilation of available data

- Planning field surveys: locations and procedures

-Planning & developing data entry: software and procedures

- Field checking: locations, team sizing and pilot surveys

- Field survey: hiring, training, transport, paying, controlling outputs

- Data entry: cleaning, processing: computer network

-Model assembling, calibrating: more survey?

3.1.1. FIELD PREVIEW

Had as objective point out relevant points of interest, set background on transportation

and establish contact first contact with reality and foresee surveys difficult,

3.1.2. COMPILATION OF DATA ALREADY SURVEYED

For roads it was already available previous PMU work, developed along 2004, in GIS

files, from aerial photos available and maps.


48

For Routes a database developed with corridor was already developed, from the Dala-

dala journal and PMU itinerary field survey

For FVOSu & CCSu, there were available surveys counting passenger and vehicle in

peak hours per made in June/2004 and a pilot survey of passengers per route made in

July. A pedestrian and bicycle counting were available to.

For O/D a bicycle O/D survey was available, but not of utility for the model to be

developed.

3.1.3. PLANNING FIELD SURVEYS, LOCATIONS, AND TEAM SIZING

Street surveys:

Frequency and Visual Occupancy Surveys (FVOSu): were to be conducted so

as to know the number of public transportation (daladalas) travelers and the

frequency at which operating bus routes can be seen at the survey points and

variations of the peak flow of passenger’s from point to point within the system.

o 20 to 24 key locations ->two ways were supposed to produce a screen

line, that should start first than od to determine sample (at least 1 week,

if data entry is set);

o 6 Locations closing the CBD would have morning and afternoon survey,

other points only in the morning

o 8 surveyors team per shift;

o 2 supervisors per shift;

o first 3 letters from route origin and first 3 letters from route destination

would identify the route in most of cases, but special marks regarding

routes with same name would be necessary.

Classified Counts Surveys (CCSu): this survey should be done to understand

the proportions of volumes of the available road traffic and modal distribution: o the same 20-24 locations, should be used

o can go together with FVOSu to save supervisor

o 2 or 4 surveyors team using boards with counters for the larger

volumes.


49

On Board surveys:

Origin and Destination Surveys (O/DSu): to determine the population travel

desire patterns during their daily itineraries/trips using public transportation,

and a summary of the purpose of their trips (work, study, other):

o 50 surveyors, only morning shift, assume opposite trip in return

o 6 or 8 supervisors

o on the same locations but one week after the FVOSu, so teams could

be sized to achieve expected sample

Velocity boarding and Alighting Surveys (VBASu): used to determine the bus

loads and routes operational parameters such as passengers per kilometer

index, turnover index, operational speeds and dispatch frequencies to

understand the system’s effectiveness and service quality: o 30 routes, so to cover all the network o 4 surveyors per shift, without supervisor along the route.

3.1.4. PLANNING & DEVELOPING DATA ENTRY: SOFTWARE AND PROCEDURES

DARTDBST: the in-house (custom) developed databank system to assist the typing of

surveys data from the survey forms while performing a number of checks to ensure they

conform to the defined relationship and stored checking procedures that are in the

databank system.

The databank has also a module to display the results.

The main purpose of the DART Data Bank is to maintain the data integrity and preserve

its consistence as long as the data last in it.

With a total cooperation of the above software’s and to its possibilities as databank,

information was developed, manipulated and simulated to ensure the city of Dar es

Salaam could be put into viable models to supplement plans of the different aspects of

the DART system design, the DART system emme/2 model in specific.

The inventory with the entire set of this information is included as digital appendixes to

this report (General surveys databank system “Dartdbs.mdb”) and DART Databank User

Manual and Technical Manual prepared as a general guide for new users and developers

willing to access information.


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3.2. MAP SURVEYS

3.2.1. PASSING THRU NODES

From the already existing GIS Map, all intersections were selected as passing thru

nodes and numbered, the given coordinates for the network can be found in

“Network.xls”, the graphic bellow correspond to the central area.

In the sheet Nodes, the 2591 nodes are shown, first column presents the numbers to

refer to those nodes, second and third columns contains latitude and longitude.

3.2.2. O/D NODES: CENTROIDS AND ZONES

The existing structure of Kataa and Mtaa was used to aggregate all the information

relating possible origins and destinations within the city. In the outskirts, Kataas were

considered an enough level of aggregation, approaching the CBD, the Mtaa were used.

Later, a sub-divisions were included in Kivukoni area. This selection allow both having

mechanisms of control of expansion of the survey sample and costs of time (as data

regarding population and use of soil was already surveyed) and to obtain clear answers


51

during the O/D surveys, that will be discussed later. (Kataas are electoral zones for the

City Council)

The zones boundaries and centroids may also be observed in “Network.xls”, the central

area shown on the graphic bellow

The boundaries were drawn upon paper maps, based in surveys in loco, together with

the registered descriptions got in each Municipality of Dar es Salaam. This draws were

plotted in the map in AutoCad and then formatted and fixed in GIS. The points that form

each frontier can be seen on [“Network.xls”]Zone Areas_mif! Just

coordinates are provided there, not associated with the centroid nodes numbers, which

can be seen in [“Network.xls”]Centroids!


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3.2.3. LINKS

Links were automatically created in both ways from the same GIS maps, as in

“Network.xls”. Despite main roads were identified, links were not classified according to

that, only speeds were added based on Velocity Surveys, discussed later.


53

Additionally were created links representing the walking distance from centroids to the

road network, one centroid may be linked to several points in the network, these points

were selected to points were there is bus services, and the length registered on these

links (shown in pink on the figure bellow) are not the real distance, but were estimated to

represent an average distance that someone living (or working) in the area would have to

walk to reach the street system.

In [“Network.xls”]Links! All the links are shown, being used the code 17 to represent street links and code 13 to represent walking links to the centroids.


54

3.2.4. ROUTES ITINERARIES

Upon the previous maps, based on the regulatory agency (DRLTA) information, from the

first database assembled plus knowledge of almost everybody in the PMU (special

thanks to Simon) declared and official routes were drawn one by one, and every passing

node registered.

During traffic surveys, different itineraries were verified – routes were not passing were

they were supposed to, routes were passing were they were not suppose to pass, routes

that were not known were seen, known routes disappeared. This was expected, and as

the city is changing every day, so new routes are created and change very fast.

So to be able to “close” the model, it was necessary to “freeze” the itineraries at a certain point.

This point is to be representative of the middle period of the surveys, and from that frozen

situation data related to unknown or unexpected routes are thrown to others, considered similar.

Despite we cannot really know the level of missing routes or mistaken routes; we believe that

90% of the routes were captured accordingly, being the missing ones the less important.

The routes can be found in “Public_Routes.xls”, which can be converted in Emme2

(“routesm2.txt”) or TransCad format for best visualization.

3.3. TRAFFIC SUPPLY AND DEMAND SURVEYS

As the road network basis was defined, points all around the city were selected to bring

forward information about traffic demand (and automatically some about supply too) on

those locations.

The most hard and precious information to be gathered, after the already commented,

was the O/D data. In this way the other surveys were developed as auxiliary surveys

around it, nevertheless the other data was not considered less important.

The matter is that while O/D data is gathered as sample the other data is regarded as

counting, i.e., entire universe is surveyed. The other data is used to expand (inflate) the

O/D sample.

As the focus for this transportation model we developed, is for Public Transit design,

which are expected to represent more than 90% of the people of Dar es Salaam (refer to

http://www.itdp.org/programs/dar/junesurvey.aspx), the O/D surveys were made only with


55

present users of this system, in peak hours. This would save survey time, survey costs

and processing efforts.

Strategic points were selected to reach bus passengers and proceed to a very short

interview. Originally the plan was to do interviews inside the bus, during the trips, in

selected segments of the road, but considered that buses were absolutely crowded in

peak hours and the difficult to have surveyors boarding an doing interviews in such

conditions lead to an alternative scheme – counting with the cooperation of transit police

– where the daladalas were stop briefly (always for less than 2 minutes) and several

surveyors would interview people inside the bus or thru the windows. Questions were

very brief, just enough to situate on that particular travel that was being interrupted, what

were origin and destination zones, among those already predefined.

This alternative of survey, if compared with the traditional home based method shows the

following differences:

Less information regarding the user is surveyed, especially regarding the value of time,

but the information about the trip is much more precise, as it is catch on the moment of

the trip.

Every interview is on the targeted public, therefore useful, but there is less control of the

non-targeted public (non-public transit trips: NMT trips, private cars trips).

It is harder to expand the sample, but it is faster to obtain it.

The shorter number of questions makes it easier to process.

The selection of locations to interrupt the trips (34 points) as in the following picture was

done in a way such that every potential user of the BRT system would be count at least

once. And would have the same chance of being interviewed as any other user of the

system, considered each time he/she would pass thru one of this points. So, on the same

locations where the O/D was done, two other surveys were also done: Classifying Counts

(CCSu) and Frequency and Visual Occupancy Surveys (FVOSu). These plus the surveys

that were developed in other locations, or along the routes (inside the daladala) -

Velocity, Boarding and Alighting Surveys (VBASu), Directional Flow Counting (DFSu) and

Station Boarding and Alighting Surveys (SBASu) are detailed next.


56

With this clear panorama a general schedule was issued and the surveying period began

on daily basis usually from 05:00 to 11:00 and on some special locations from 05:00 to

21:00.


57

Table 12 Survey Point List Code SurveyType Location Landmark

1 CC/OD Kivukoni Front/Ocean Front Ferry Boat Ticket Office 2 FVO/CC/OD Sokoine Drive/Zanaki Street DMI 3 FVO/CC/OD Bibititi Mohamed Road/Ohio Street Parthenon Hall/AMREF Med Office 4 FVO/CC/OD Bibititi Mohamed Road/Zanaki Street CBE 5 FVO/CC/OD Uhuru Street/Samora Avenue Agent Karibu Textil 6 FVO/CC/OD Ali Hassan Mwinyi Road/United Nations Road Selander Bridge 7 FVO/CC/OD United Nations Road/Fire Station Road Fire Station/Tree/Comercial Poster 8 FVO/CC/OD Msimbazi Street/Mafia Street Duka La Dawa Farmacy 9 FVO/CC/OD Bandari Road/Gerezani Steet Railway/Roundabout

10 FVO/CC/OD Morogoro Road/Jangwani Street 11 FVO/CC/OD Uhuru Street/Sulemani Rubama Street Kituo Cha Taxi Car Park 12 FVO/CC/OD Nyerere Road/Between Msimb & Shaurim Samsung Comercial Poster 13 FVO/CC/OD Bagamoyo Road/Btwn Chato & Ursino Street AAR - Health Center 14 FVO/CC/OD Kawawa Road/Livingstone Street Lilian CAF 15 FVO/CC/OD Mlandizi Street/Mchinga Street Bal Con Rest House 16 FVO/CC/OD Morogoro Road/Kawawa Road Municipal Council/NMB 17 FVO/CC/OD Kawawa Road/Old Kigogo Road Bus Stop/OMO Comercial Poster 18 FVO/CC/OD Old Kigogo Road/Kawawa Road Sunset Bar 19 FVO/CC/OD Uhuru Street/Buguruni Area Jaffar Auto Parts/Stream 20 FVO/CC/OD Nyerere Road/Before Chang'ombe Road Quality Plaza Hotel 21 FVO/CC/OD Chang'ombe Road/Petrol Station Petrol Station 22 FVO/CC/OD Nelson Mandela Road Saruji Cement 23 FVO/CC/OD Kilwa Road/Nelson Mandela Road TIA 24 FVO/CC/OD Old Bagamoyo Road/Feza Street The Arcade Bar 25 FVO/CC/OD Bagamoyo Road Lugalo Military Base 26 FVO/CC/OD Shekilango Road Thinga House Video 27 FVO/CC/OD Sam Nujoma Road/University Road University of Dar es Salaam 28 FVO/CC/OD Morogoro Road Transmission Tower 29 FVO/CC/OD Nelson Mandela Road/Morogoro Road Bridge 30 FVO/CC/OD Nelson Mandela Road Tiot Petrol Station 31 FVO/CC/OD Nyerere Road Safasha Place 32 FVO/CC/OD Nelson Mandela/Mbagala Road Steel Masters Ltd 33 FVO/CC/OD Temeke Road Police Station 34 FVO/CC/OD Kilwa Road Oil Com Petrol Station 35 CC/OD Samora Avenue/Morogoro Road Classified Count Survey Only 36 CC/OD Morogoro Road/Samora Avenue Classified Count Survey Only 37 OD Mbagala Road Near JCT Mandela 38 DF JCT Morogoro Rd & Nelson Mandela Rd 39 DF JCT Morogoro Rd & Shekilango Rd 40 DF JCT Morogoro Rd & Kawawa Rd 41 DF JCT Morogoro Rd & United Nations Rd 42 DF JCT Morogoro Rd & Msimbazi St 43 DF JCT Morogoro Rd & Lumumba St 44 DF JCT Morogoro Rd & Bibi Titi Road 45 DF JCT Morogoro Rd & Mabibo Entrance 46 FVO/CC Kawawa Road/Kondoa Street Kanisa Lutheran 47 FVO/CC Morogoro Road Tip Top Fabu Pharmacy 48 FVO/CC Morogoro Road Tanzania China Tanzania China Gate 49 FVO/CC Morogoro Road Ubungo Plaza Limited Build 50 CC Kivukoni Front - Ohio Street 51 CC Sokoine Drive - Ohio Street 52 CC Samora Avenue - Ohio Street 53 DF JCT Bibititi Road - Ohio Street 54 DF JCT Bibititi Road -Maktaba Street 55 DF JCT Bibititi Road - Uhuru Street 56 DF JCT Bibititi Road - Nkurumah Street

57 DF JCT. Kawawa Rd./Bagamoyo Rd. 58 DF JCT. Kawawa Rd./Dunga St.


58

59 DF JCT. Kawawa Rd./Kinondoni Rd. 60 DF JCT. Kawawa Rd./Mwinyijuma Rd. 61 DF JCT. Kawawa Rd./Mlandizi St. 62 DF JCT. Msimbazi Rd./Swahili St. 63 DF JCT. Msimbazi Rd./Mafia St. 64 DF JCT. Msimbazi Rd./Uhuru St. 65 DF JCT. Msimbazi Rd./Lindi St. 66 DF JCT. Msimbazi Rd./Nyerere Rd. 67 DF JCT. Msimbazi Rd./Narung’ombe St. 68 DF JCT. Msimbazi Rd./Kariakoo St. 69 CC Morogoro Rd. – Kimara Oryx Gas Station 70 CC Morogoro Rd. – Kibo Rombo Bus Station 71 CC Kawawa Rd. – Kinondoni A BP Gas Station 72 CC Kawawa Rd. – Magomeni Magomeni Hospital 73 CC Msimbazi Rd. – Kariakoo Tanzania Postal Bank 74 CC Msimbazi Rd. – Gerezani Gerezani Police Post

3.3.1. OPTIONS AND JUSTIFICATIONS

1. Make a O/D, onboard survey.

This OD should mind only public transport by bus, which is the targeted public. It offers

the advantage of getting the user at the moment of its trip, less subject to error than

house hold surveys, much faster and cheaper.

2. Perform the O/D onboard survey only in the morning and counting all day only in key locations. - While the sizing of the system is focused in the peak hours and the demand forecast and technical and financial feasibility are not, the demand, and net benefits of the corridor to be chosen is highly dependent on actual congestion, that seems to have the same patterns on different corridors. - Use one peak period and expand results for all day would be precise. Together with that comes the fact that surveyors do not need to work with breaks. The surveyors perform best in shifts of six hours, so we can count five hours straight in the morning maintaining quality. - Perform the survey in the afternoon peak in some of the control sections in the evening peak would cause double counting differences even harder to manage, due to uncertainties in the conditions of control. - The choice of work off-peak will cost less to obtain the desired amount of interviews, once we will have surveyors hired for the peak hours available.

3. Apply the O/D onboard survey to the entire city instead of concentrate in the candidates’ corridor legs. We expected that demand on other corridors could be attracted to the chosen one, or

maybe discover another corridor, that would have higher benefits. Knowledge about Dar

es Salaam public transport was very few, and got much higher at the end of this project,

but the faster, more trustable and replicable way to obtain was through the surveys we


59

planned. The matrix was made, then, available for the global planning of future corridors.

4. Concentrate the O/D onboard survey on specific points, instead of extending it to all itineraries off all lines. Basically in this method, there are not obtaining data about short trips, outside studied

area, so we are not obtaining a matrix of the total bus public transport, but a matrix of

people that are entering the city, that is the public for the future BRT network.

The alternative method of extending the research over all the itineraries of all lines is

more complete, but would need more time to be planned and executed. It would need

much more resources and time do be concluded.

5. Obtain predefined coded zones.

This method is much easier and faster to survey and process data. It is very useful to

choose between alternative macro itineraries, despite is not good to define bus stop

locations. To define that, boarding and alighting surveys along the corridor (onboard or

on bus stops) were required (and executed) as a complement. 6. A Sample of 30 000 interviews. This is a minimum sample volume compatible with Dar es Salaam size. It is something about 3% of 1 million daily bus trips on surveyed area. See next section about this point. The sample collecting was systematical in order to control expansion, so inside a selected sample, the collecting had to be random. This means that we aimed for having around the same weight factor to expand the results of each interview, meaning maximum the use of each interview.

7. Extend velocity surveys for all the day and corridors. It is a logical consequence of expanding the O/D survey, so we can calibrate and model for distinct periods and for the whole planned network. There also shall be noted that the velocity survey is to be applied to the public transport, and not to the general traffic.

3.3.2. O/D SAMPLE SIZE

Errors in the planning process derived for a small sample, results on a project that are

worse than that would be obtained by a perfect OD Matrix

This difference can be written in an expression like the following:

P = K × , U .

N -11


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where: P: Prejudice by project wrong choice due to errors on O/D Matrix

K: constant,

U: universe,

N: sample size.

The survey cost can be expressed like:

CTS= CP * N + B, where: CTS: OD survey cost,

CP: unitary marginal O/D survey cost

B: constant.

Assuming that each additional resource spent on survey, shall give a minimum of R

times the return in social benefits (R is a factor of lack of money, less money

available greater is R), we reach for optimal sampling size:

; the formula is pretty good, but it is difficult to estimate the Value of

K ×U , that depends on what are the matrix is been used for and what are the options to

be evaluated.

For Dar we estimate: K ×U = 2.5E9 US dollars (it means 2.5 billion dollars)

CP = 0.30 dollars

R = 10 (meaning that each marginal dollar spent in survey

must return 10 dollars on social benefits)

With these values we obtain: N opt ~ 29 000

If others values for R, CP and K ×U are used, N will be in a range from 10,000 to 100,000.

The lower value would be recommended in an absolute absence of resources, and the

higher in ideal conditions. Considering that this data will be useful for planning the future

of the transport, it is prudent to apply the resources in survey now.

The sample size for intercept surveys depends on the accuracy required and the population of

interest. The error for an intercept OD survey is a function of the number of possible zones


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that a passenger might travel to when passing through a particular point. As a simple rule,

Ortúzar and Willumsen (2001) suggest the following table for a 95% confidence in an error of

10% for given passenger flows:

Table 13 -Sample size for origin-destination surveys based on passenger traffic.

Expected Passenger flow (passengers/period) Sample size (%)

900 + 10.0 % 700-899 12.5 % 500-699 16.6 % 300-499 25.0 % 200-299 33.0 % 1-199 50.0 %

Usually, on BRT corridors, the flows are much greater than 900, so 10% of the total

passenger flow at any given survey point is a reasonable rule of thumb. In the case of

Dar es Salaam, the average traffic flow at the peak hour was around 10,000, so 1000

passengers were surveyed at each of the 34 survey points, or some 34,000 surveys.

3.4. FORMS AND PROCEDURES

A detailed sketch for each location can be seen on the files “Dar_Point**.jpg”, in the

folder “Present_points_draws” with the attachments, as the example on the next picture:

Figure 26 Example of the Survey detailed point location

With the exception of Points 2 and 4, all the other survey locations had surveys

happening on both sides of the street, to make clear separation between one point of


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survey and a survey location, where a location would be called sometimes “point-way”, a

“point-way code” was used as a survey location code with 5 digits where the first was P

and the fourth was W would indicate the location of the survey. So P06W1 would indicate

a survey conducted in Point 6, way 2. Where it was clear, the flow TO town was called W1

and the flow AWAY from town was called W2.In order to prevent mistakes from

disorientation or failure in pointing the correct location on the map a physical reference on

the same sidewalk the surveyor was standing was always requested. Digital photos of

every location were taken during the days of survey to clarify doubts that could appear

later (find this photos in the folder SurveyPhotos).

3.4.1. O/D SURVEY (ODSU)

The personnel assigned to one location (point-way) was composed by 1 police officer

and 2 survey teams, where each survey team had 1 supervisor and 6 surveyors.

The police officer was responsible to stop the first coming vehicle (daladala) when he

was informed by one of the 2 supervisors that his team was ready. Simultaneously police

officer, supervisor and surveyor would request driver, conductor and passengers’

cooperation.

Once the vehicle was stopped, the supervisor was responsible to take note of time and

the route of the stopped car and to provide more detailed information regarding the use of

the survey, if requested and further possible contacts to those who required that. He was

also responsible to organize that surveyors would reach people on form every part of the

bus, in a randomic way (the back as well as the front, the left and the right, people seated

or standing) and to interrupt surveyors doing their interviews in order to prevent the

daladala to be stopped for more than 2 minutes or less, if only one surveyor and one

respondent was detaining the bus. He would then make sure that every surveyor could fill

the forms and get ready for interview passengers on the next bus.

The surveyors were responsible to present themselves with a very brief way, mentioning

that they were doing a survey for better understand the daladalas’ users transport needs

by getting a large sample of users origins and destinations that are being interviewed

during their trip, and that they wish not to delay the bus, before jump to the 6 simple

questions. They were responsible to make sure the respondent had got


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the meaning of the questions and eventually crosscheck it. They were supposed to know

the questions by heart (which was reasonable after the second day) and make notes on

the answers that would allow them to finish to fill the forms after release the bus.

The questions and the form can be found in “Final_Forms.xls” and is pasted bellow. One

form has space for 4 interviews (is to say 4 buses, together with the route, the time was

also taken and written on the form).

Figure 27 OD Survey Form

The questions are divided in 2 groups: the first 3 ones (11 to 13) regarding the origin and

the last 3 ones (14 to 16) regarding the destination.

The first one of each group (“where did you started this journey and where are you

going? home, work place, market place, school, other?”) were mostly important to make

sure the respondent would not provide intermediate destinations as a bus stop or a

terminal.

To the second one the surveyor was to obtain a clear answer up to the level were the

Kata and the Mtaa (translated as Ward and Sub-ward) could be identified,


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accordingly to the predefined zones. To shorten the process, the questioning should be

mad form bottom to top: city, municipality, ward, subward: if the user interviewed was

able to identify the ward without any suggestion (suggestions were not to be given at this

point), then alternatives about the sub-ward could be presented, if necessary. So each

surveyor had one plastic card with all the wards and sub-wards attached to its belt or to

its board. The card is shown on next page (front and back) and can be found in

“WARD_SUBWARD.xls”.

All the existing Kataa and Mtaa in Dar es Salaam were included in the card despite the

fact a few Mtaa were not of interest to does not incentive surveyors to stop questioning

before obtaining the Mtaa information.

The third question is to check transfer level and, of more importance, the problem of

“double counting” during the sample expansion process. The answer is of overall of

interest, but the main reason to be included is to split those who might answer the

alternative 2, to question 3, 6 or both (users who need to be ride another daladala for one

trip).

The route (njia) where the survey was being made was not asked, nor if the passenger

was seated or standing as this could be observed and marked later.

Figure 28 OD Surveyor Kata and Mtaa Card (Temeke)


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Figure 29 OD Surveyor Kata and Mtaa Card (Ilala and Kinondoni)


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3.4.2. FREQUENCY AND VISUAL OCCUPANCY SURVEY (FVOSU)

These surveys are to be done at the exactly same locations the ODSu, the objective is to

obtain the total number of passengers using daladalas, per route on that location during

the time of survey, per controlled intervals of 15 minutes these surveys were at least

carried out during the same hours of the day the ODSu was made. So it is possible to

compose a partial O/D Matrix to that location by simply considering that each interview

done represents a random sample from the total passengers for that route. It is to say

that each sample will be multiplied by the total number of passengers on that route

divided by the total number of interviews made on that location.

To one survey location (point-way) the survey team was supposed to be formed by one

supervisor (who was also responsible to supervise the Classifying Counting Survey on

that location to occur at the same time) and a team up to 4 surveyors, depending on the

traffic volume of the road.

The supervisor was responsible to split the team accordingly to the place of survey in a

way such that for each passing bus one and only one surveyor would take note of the

route, the size of the bus and the number of passengers inside the bus. The number of

passengers inside the bus was to be estimated by the surveyor based on the size of the

bus and his perception of the level of occupancy. The supervisor should also keep track

of time and assure that the surveyors to take note of the moment each quarter of hour

was completed.

The form used is shown bellow and in “Final_Forms.xls”


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Figure 30 FVOSu Form

Space is left for more than one surveyor in the header because the trainings have shown

that one of the easiest ways of filling it was have surveyors working in groups: with only

one taking notes, keeping his eyes on the board, while other (or others) would look to the

street.

Despite there is space for entering time for every entry, the time was just took only when

a quarter hour was reached, all the following buses (up to the row a nom empty row is

reached) are assumed to be on the same quarter hour.

But every time a new column was started, the time was supposed to be written on the

first row, as an extra control mechanism. So, the first time an hour appears it is informing

that the bus route marked at its side was the first one after


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that quarter hour was reached, the following buses were observed in the same interval.

The second time or third time the same hour appears is just informing that the final of that

quarter hour was not reached by the time the bus in the route o its side as taken.

3.4.3. CLASSIFYING COUNTING SURVEY (CCSU)

These surveys, carried out at the same locations as the ODSu, were made in conjunct

with the FVOSus, (at the same time in the same place). But 3 surveyors, no matter the

flow of vehicles volume, composed the team, which shared the supervisor with the

FVOSu, for each location, yet it was not a requirement

They have as objective count the total number of vehicles, according to classes

(categories) named as: Cars, Taxis, Trucks, Small Daladalas, Large Daladalas, Buses

and Motorcycles. This is bring to compose the picture of the present level of use for that

road, to be related with the Velocity Surveys allowing to calibrate the relationship

between capacity and speed.

CCSu also got the number of pedestrians and bicycles, as these were also information of

interest to the project, yet not inserted directly in to the demand forecast model. This data

was took accordingly to the side walk of the traffic and not with the direction of the flow.

The volume provides an idea of the needs for sidewalks on the locations and for most of

the points the pedestrians and bicycle flows are “commuter traffic” as those points were

not near dense origin and destination (residential or commercial) areas.

Making this survey together with FVO is useful to check the to the accuracy of that

survey, by crossing the total number of daladalas booked in each survey.


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The forms for one of the three surveyors is shown bellow, the other two are also in “FinalForms.xls”. The surveyor was to just count the flow of each of the three classes of “vehicles” assigned him on the correspondent space (9 to 11), tallying or making boxes. For the surveyors counting larger flows, (in most locations private cars and daladala’s going to town, but some places pedestrians were higher) mechanical counting devices fixed to the boards were provided (we got only 8 of those). If more space was required the surveyor should move to the next box, writing on the left the same hour and minute. After 15 minutes, the surveyor would pass to the next box and register the time. At the end of the shift (or during a moment without traffic) the total number should be written down in the gray square (11 to 14).

Furthermore, more points were surveyed after the addition of Kawawa and Msimbazi Street as well as a stretch of Morogoro Road from Ubungo up to Kimara. Here we had Survey conducted in 6 points, 2 on each corridor. This was done for the whole week, that is, 12 hours for 5 days and 24 Hours for 2 days (One on the week and the other on the Weekend). The survey teams were set in such a way that one team on the side of the road, taking the details of the vehicles heading downtown and the other for the upcountry vehicles. Whereby, on each team, one is noting the light weight vehicles and the other heavy duty vehicles.

Fig 31a. Light Duty/Passengers Vehicles.


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Figure 31b. Heavy Duty Vehicles.

There were 5 members on each team in 6 points, which make a total of 45 surveyors. 30 surveyors were working for 7 days (day shift) and 15 surveyors were working for 4 days (night shift).

Figure 31c CCSu Form


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3.4.4. VELOCITY, BOARDING AND ALIGHTING SURVEY (VBASU) Teams assigned to selected routes, which together would cover the entire road network,

should make these surveys on board all along the route, from starting point to ending

point, back and forth. During the time period covered by this survey, a 45 minutes interval

was expected between the passage of a surveyor thru a point riding on a certain route

and the next surveyor riding on the same route.

During each trip, the surveyor should tally the number of passengers that boarded and

alighted before reaching a selected and known point of reference, as well as record the

moment the bus reached the reference point.

By the locations of each point, and knowing the distance between those points, once the

itinerary is followed, these surveys would provide the public transport speeds. The main 5

corridors were at least covered by 3 routes, up to 6.

A standard form should be like the following, where references were to be filled up for

each route-way:

Figure 32 VBA Generic Form


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The developed form for each survey day can be found inside the folder VBA Reviewed,

inside of the day of VBA survey, under the route Name, one example is shown bellow.

Figure 33 VBASu form filled with references


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VBASu was conducted on 28 daladala routes. VBA Surveys bus routes used the first

three characters from each of the strings forming a route name (MWEPOS represents the

itinerary from Mwenge to Posta). The VBA Surveys routing system mostly followed the

DSM Public transportation sector system for naming routes.

Table 14 Velocity Boarding and Alighting Survey Route Selection

CODE Path PR001 MBAGALA RANGI TATU-BUGURUNI PR006 BUGURUNI-POSTA PR008 KARIAKOO-BUNJU PR012 GONGO LA MBOTO-KIVUKONI PR024 KIBAMBA-KARIAKOO PR025 KIBAMBA-MUHIMBILI PR028 KIBADA-KIGAMBONI PR031 VIJIBWENI-KIGAMBONI PR036 KILUVYA-KARIAKOO PR037 KIMARA-KARIAKOO PR042 MABIBO-KARIAKOO PR045 MABIBO-POSTA PR050 MBAGALA RANGI TATU-KARIAKOO PR059 UBUNGO-MSASANI PR060 MTONI MTONGANI-KARIAKOO PR065 BUGURUNI-MWANANYAMALA PR070 MWANANYAMALA-STESHENI PR078 MWENGE-POSTA PR082 UBUNGO-MWENGE PR084 PUGU KAJIUNGENI-MWENGE PR087 SINZA-KARIAKOO PR090 TANDIKA-SINZA PR093 TABATA MAWENZI-MUHIMBILI PR098 TANDIKA-KAWE PR110 TEMEKE-KAWE PR119 MTONI MTONGANI-UBUNGO PR121 UBUNGO-MWANANYAMALA PR132 VINGUNGUTI-KIVUKONI

The next two surveys described were developed after preliminary results became

available in order to improve information along Morogoro Road.

3.4.5. DIRECTIONAL FLOW (CLASSIFYING) COUNTING (DFSU) These Surveys are to provide detailed information on intersections, by measuring the

volume of vehicles that comes and goes from and to each direction and every possible

combination.

The procedure is exactly the same as the CCSu, but the classification of vehicles was

simpler: Cars (including taxis), Daladalas and Trucks (including larger buses).


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The location codes followed the previous surveys codes, new points were numbered from

P38 beyond to P45 (as during processing special uses were applied from 35 to 37), as

follows (all along Morogoro Road Intersections):

P39 - Mandela Rd.

P40 - Shekilango

P41 - Kawawa Rd.

P42 - Msimbazi St.

P43 - Lumumba St.

P44 - Bibi Titi St.

P45 - Mabibo Rd.

As well the survey go through new points P57 to P66 for the added corridors of Kawawa

Road and Msimbazi Street.

For Kawawa Road we have:

P57: Bagamoyo Rd

P58: Dunga St

P59: Kinondoni Rd

P60: Mwinyijuma St

P61: Mlandizi St.

Msimbazi Street points are:

P62: Swahili St

P63: Mafia St

P64: Uhuru St

P65: Lindi St

P66: Nyerere Rd

Then to ways the same

convention applied, with the new directions

added:

W1 – From West (to town) W2 – From East W3 – From North W4 – From South

Figure 34 DFSu Form


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Then another letter was added to describe the flow movement when reaching the

intersection:

A – Left Turn

B – Straight

C – Right Turn

So, each survey point could have 12 locations, except for the “T” intersections (P39 and

P45), with only 6.

One surveyor was pointed to each location, and one supervisor pointed to each

intersection.

3.4.6. STATION BOARDING AND ALIGHTING SURVEY

With teams placed in the stations, the procedure is similar of the FVOSu, but the

surveyors only considers the daladala that park on the stations, and instead of estimating

the number of passengers inside the buses, they count the number of passengers

boarding and alighting on that station.

For calibrating the model, this information is as useful as the FVOSu, as we have the

number of passengers (even counting those who alight to transfer) that goes thru a

particular link of the system (which eventually has to be created between the street, a

boarding platform and an alighting platform).

Figure 35 SBASu Locations (except Fire)

The number of people assigned to each station was based on the expected movement

for each place, ranging from 2 to 8 (counting one supervisor per location).


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The intervals used for keeping track of time to these surveys was of 5 minutes instead of

15, like the other surveys.

A special direction flow was introduced for identifying the path surveyed; general

movements and vehicles moving from West to East (CBD bound) or North to South will

be labeled on “WAY ONE””(W1). Contrary movements will be labeled “WAY TWO” (W2).

This identification is applicable for all the surveys.

A total of 13 stations where surveyed, these are located along Morogoro Road. S01 to

S13 are station locations codes used for station boarding and alighting surveys.


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Table 15 Station Boarding and Alighting Survey Points along Morogoro Road

STATION NAME S01 FIRE S02 JANGWANI S03 MAPIPA S04 USALAMA S05 MWEMBE CHAI S06 KAGERA S07 ARGENTINA S08 BAKHRESA S09 MANZESE DARAJANI S10 MAHAKAMA YA NDIZI S11 URAFIKI S12 SHEKILANGO S13 UBUNGO

3.5. SURVEY ACTIVITIES

The work plan changed a little from the beginning till the end, some surveys were bring

forward, some were delayed, and yet a few changes in the schedule happened, the

executed activities were after all executed as the following tables (from

“Survey_Workplan_Revised.xls”).


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Figure 37 Surveys final schedule


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Day Of WEEK WEEKDAY SURVEYS Survey HOUR/SHIFT TYPE

OFSURVEY ROUTES

10 09/may Monday 11 10/may Tuesday 12 11/may Wednesday

VBA SURVEY TRAINING SUPERVISORS AND SURVEYORS

PR065-PR070

MWA-BUG / MWA-STE 13 12/may Thursday 6:00 - 11:00

15:00 -20:00 VBASu PR006-PR045

BUG-POS / MAB-POS

PR098-PR110

KAW-TAN / KAW-TEM

14 13/may Friday 6:00 - 11:00 15:00 -20:00 VBASu PR050 MR3-KOO

PR059-PR082

UBU-MSA / UBU-MWE

14/may Saturday 15 16/may Monday CANCELLED

PR087-PR090

SIN-KOO / TAN-SIN 16 17/may Tuesday 6:00 - 11:00

15:00 -20:00 VBASu PR025-PR042

KIB-MUH-MAB-KOO

PR012-PR132

GON-KIV / VIN-KIV 17 18/may Wednesday 6:00 - 11:00

15:00 -20:00 VBASu PR119-PR121

MTO-UBU / UBU-MWA

PR078-PR084

MWE-POS / MWE-PUG 18 19/may Thursday 6:00 - 11:00

15:00 -20:00 VBASu PR001-PR061

BUG-MBA / MTO-KOO

PR008-PR024

BUN-KOO / KIB-KOO 19 20/may Friday 6:00 - 11:00

15:00 -20:01 VBASu PR036-PR037

KIL-KOO / KIM-KOO

PR028-PR031

KIB-KIG / VJI-KIG 20 23/may Monday 6:00 - 11:00

15:00 -20:02 VBASu PR093 TMA-MUH

Figure 38 VBA Surveys final schedule

Figure 38a Classified Counts Survey Phase 1A Schedule


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3.5.1. TRAINING

The following were the requirements to the training process

field surveys -> training place + fields locations, transport personnel enlistment previous surveyors preferentialy university students, but not necessarily must be clearly informed of the salary and conditions, before anything else it is a temporary job they will be dismissed at the smallest suspicious of “cheating” they will work on a daily basis (this allow us to stop, whenever we want). we will pay half day for the field sessions we pay more for odo than fvo. training sessions

in doors: OD: gather more than 100 people, dismiss half FVO & CC: the remaining of previous team shall do VBA: shall be done by the best surveyors (supervisor level)

field training

first day is pilot, not take most important locations supervisors need to do survey + training for supervising coordinators as well


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Training Session for Surveyors and Supervisors

Practical (On Field Training) “the surveys”

high personnel turn over transport two coordinators coordinators & supervisors shall be regularly going to survey transport, water


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gather people in the morning (or meeting point where everybody can meet),

return at night

transport: 1 small bus (fvo) 2 medium buses 1 car for coordinators

Positions of Surveyors for CCSu and FVOSu

Conducting ODSu

coordinators:

o 1 for execute so

o 1 for personnel recruiting & payments

payments:

o shall be on time

o every Monday

o the highest possible, (suggest 15US$/day for ODO, 10US$/day)


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conditions to the surveyors:

o they must inform all mistakes they do, so we may correct

o shall be fired immediately, when suspicious “cheating”

o need have breaks and water

For achieving accurate results, a thorough training program took place with the number of

surveyors selected for performing the task of data collection and interviewing

transportation users. In fact the number of surveyors trained was always higher than the

number needed to work This training was scheduled and conducted one to two days prior

to the execution of the actual survey. A post-training trial session was also included in

this program intended for acquaintance between the surveyors and the field work.

No matter how much training you give, you can go do survey on the field, there will be

still things to be corrected, adjusted and verified on the field. So usually the first day on

the field will be like a real training. The objective of the survey, that is input information to

the model have to be understood by the surveyors, so they can do the right decisions.

For supervisors, this understanding has to be even more complete.

Motivation shall be given on the training too, but this is more important during the survey

days, as in the beginning there is a natural enthusiasm for the job. The training is useful

to create the concepts that will be reminded on the field, with just a few words.

More attention shall be given to the process of gathering the information, than explaining

the procedures themselves, as the procedures will be experienced on practice, but the

rest of the process will not. Ideally one presentation with all background of the project is

required; in our particular case we do not have this material available so far, so we

covered it without that material.

As new surveyors join the team, new class sessions were to be given, and the

experienced surveyors might join again. Everyone evolved in the process have to know

how to do the job, supervisors, coordinators, planners.

With about 150 people involved during the practice (surveyors, supervisors, survey

coordinators, engineers and analysts) the data collection activity was underway.


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About forms:

- Despite the photos appeared really good on the copies here, special care is to be taken with this matter, to avoid have copies with black squares instead of pictures, black and white drawings are safest alternative for designing forms. - Specially for fixed locations, detailed information on the header tends to not be filled, and it is waste of time and space repeat them so it is better to have a cover sheet, with index for an exclusive number that will be repeated on every following form for that location that will be bounded to the survey forms still on the field. Other way is do a summary to be with the supervisor that will clearly indicate the index for a group of forms, usually appointing the surveyor. After that, the survey forms shall have only two fields the index (usually the surveyor name or ID) and a sheet number that, the other fields shall be fields that change from one form to another. For example, if the surveyor is supposed to change location (cross the street), than the box “( ) into ( ) away” shall be on the form, other wise it is not necessary, as that can be referred on the index. - Having instructions and remarks on the form is always good thing, if it is not to big, bit it shall always be short and clear language. It is preferably to have the forms translated and checked the meaning, with examples of doubtable situations: a good form cannot substitute training and supervision. - The hour format shall always be from 00:00 to 23:59. [For surveys that cross midnight in

the same shift, like in bus depots, the format 24:00 up to 30:00 (being this 6:00 in the

morning) can be used linking time to the date when the shift started to avoid confusion.]

About planning surveys: - Surveys must be planned by those who will use the information, plus those will conduct the survey and process the results, besides the obvious discussions about the procedures and the unclear cases, the matter of locations shall be clearly specified, and the requester shall visit the site to be sure that the task is accomplishable and that the information he expects to obtain can be obtained on that particular place. - A written description of the procedures, step by step, as a survey manual is desirable, too. 3.5.2. MONITORING AND EVALUATION

The main activities developed in the process can be pointed as: - Planning Field Surveys,

- Executing Field Surveys,

- Data Processing,

- Data Entry (into computer manageable format),

- Monitoring and evaluation.


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Most of these activities were carried out between February and August of 2005 and

directly involved the hard work of almost 200 people: more than 100 surveyors, more

than 20 supervisors, more than 30 typists, 6 ICL Engineers, 4 ICL Engineering Trainees,

5 PMU Engineers plus 2 PMU staff, 6 ITDP staff, 6 Logit staff and hired consultants.

Planning of the field surveys was one of the most essential activities of the DART project

data collection & model calibration stage. The four (4) basic models (trip generation, trip

distribution, modal choice and transit/auto assignments) making up a transportation.

System model need information as

summarized below to be established.

Based on the requirements of identifying

the existing situation, a set of surveys

were programmed and carried out, all of

which represented a crucial and useful

task when structuring a transportation

model. Surveys are grouped in two

general areas, transportation demand

surveys and traffic flow surveys.

The need for assembling the model fast,

broad and reliable model lead to pursuit

available data and city structures that

were already defined and social and

economic data was already known. Figure 39 Field data collection work flow

The approach proposed was to get global information in a less detailed level and proceed

to get detailed data where and when gaps become identified to solve aspects of interest.

Coordintator and survey designer were at every location to check the survey was going

on the right place.

Station Boarding and Alighting (SBASu): used to determine the volume of passengers

where people boarding and alighting daladalas at bus stops along Morogoro road was

necessary to make the model mor comprehensive.


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For identifying the traffic behavior along Morogoro Road and CBD streets, the Directional

Flow Survey (DFSu) was structured to collect information from every single intersection

currently in operation along this major corridor and some important streets giving access

to the CBD.

DFSu was then used to determine the volumes of vehicles (cars, daladalas and trucks)

that make the available turning movements at important intersections.

Besides the practical need of checking the development of the work, giving feed back to

the surveyors is the most important thing, in fact giving attention to their work is what

maintains the quality. So having the “bigger bosses” showing his faces on the field once

and a while is very good for the quality.

Positioning on the field shall have some liberty, the surveyors are too be grouped, so one

may help the other in the busiest moments.

After a number of field trial surveys by the surveyors and supervisors it was well assumed

that the trainings had been successful and it was about time the actual DART project field

data collection exercise should start.

Related Activities in Data Collection


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4. DATA PROCESSING, ANALYSIS AND RESULTS

With information flowing in, the next step relied on data check and depuration. The filled

survey forms information had to be verified for obvious errors, before the data was typed

into the database. Strong supervision took place during this stage. Screening process for

the information was followed by the processing an analysis stage where the awaited

results were going to be produced. After analyzing and processing the raw surveys data,

the final results import files could be prepared and put into the databank for examining

the results.

Raw and processed survey data digital files in “.xls” format are part of this report as

attachments (see appendixes).

Since the information flow containing the results for the entire analysis carried out is

considerably long and detailed, for reporting purposes, the present document will depict

the major and most important findings and results for each survey and as appendixes,

the mother database and analysis files will constitute the entire results delivery. Also

considering the complexity of the database and the training required for its manipulation,

a user and technical manuals were prepared for future users and developers. These

documents are attached as annex material.

4.1. FREQUENCY AND VISUAL OCCUPANCY

The results for FVOSu (passenger per hour volumes, graphic volume profiles per point,

daladala operational frequencies, itineraries, among others) can be examined by using

the FVO surveys databank results section (Databank User Manual, Chapter 7) or the

appendix:

Appendix A – FVO Frequency List

Appendix B – FVO Route Itineraries

Appendix C – FVO Peak Flows

Analyzed information and useful to be presented include the following items.


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4.1.1. PEAK HOUR IDENTIFICATION

Information collected from this survey in particular is greatly influential for the entire

modeling process. From the comparison between the busiest hour period and the entire

daily passenger movements the Day Expansion Factor is calculated. All this is possible

after the identification of this critical peak hour period for public transportation passenger

demand.

From the frequency and visual occupancy surveys the morning peak has observed to be

between 07:00 and 08:00 and evening peak is between 17:00 and 18:00 giving a figure

of 10.7 of representation for the morning peak hour in the whole day results.

Figure 40 Full day Surveyed point Pax/H Profile – Peak Hour Depiction

4.1.2. PASSENGER LOADS ON MAJOR SURVEY POINTS

From the surveys databank it was observed that the following points have the highest

flow of passenger’s during the peak hours:


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Table 16 Passenger Volumes on Major Survey Points

Morning Peak Evening Peak Survey Point Corridor Pax/Hour Survey

Point Corridor Pax/Hour

10 Morogoro Road 13,096 10 Morogoro Road 13,554

16 Morogoro Road 9,700 12 Nyerere Road 8,029

46 Kawawa Road 9,598 11 Uhuru Street 7,129 11 Uhuru Street 9,365 9 Bandari Road 6,268

4 Bibi Titi Road 8,820 6 Ali Hassan Mwinyi Road 3,906

4.1.3. PEAK EXPANSION FACTORS

From the raw interval FVOSu data the total passenger’s per time interval were totalized

and compared with the Peak hour flow of passenger’s for the observed important points

as in the ones with the highest passenger volumes per hour flow and full day surveyed to

compute their peak factors.

Table 17 Peak factors for the 5 full day surveyed points (Morning Peak)

POINT Point 6

Selander Bridge

Point 9 Bandari Road

Point 10 -Morogoro Rd

Point 11 Uhuru St

Point 12 Nyerere Rd

Daily Total 06:00 22:00 41,105 48,723 112,369 64,522 63,395

Peak hour 07:00 08:00 4,617 6,073 13,096 9,365 8,320

Ratio 11.2% 12.5% 11.7% 14.5% 13.1%

Table 18 Peak factors for the 5 full day surveyed points (Evening peak)

POINT Point 6

Selander Bridge

Point 9 Bandari Road

Point 10 -Morogoro Rd

Point 11 Uhuru St

Point 12 Nyerere Rd

Daily Total 06:00 22:00 37,728 53,858 127,180 74,001 60,284

Peak hour 18:00 19:00 3,906 6,268 13,554 7,129 8,029

Ratio 10.4% 11.6% 10.7% 9.6% 13.3% 4.1.4. MASTER POINT – POINT 10 MOROGORO ROAD JANGWANI AREA

Point 10 on Morogoro Road has been observed to have the highest passenger flow per

hour during both peak hours, 13,096 during the morning peak, and 13,554 passengers

during the evening peak, thus being the heaviest loaded point and corridor in the entire

DSM road network.


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Figure 41 Point 10 Peak Passenger Volume per Hour

Figure 42 Point 10 Full day Passenger Volume Profile – Hourly Analysis


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Passenger volumes on the peak hour, results and profile graphics for the remaining

points can be obtained FVO surveys databank results section (Databank User Manual,

Chapter 7) or the appendix:

Appendix C – FVO Peak Flows

4.1.5. BUS ROUTES FREQUENCIES LIST

The bus routes frequency at survey points was observed to vary greatly from point to

point. However, an average frequency for bus routes was computed for each individual

route. From this analysis the daladala route Kariakoo – Mwenge (KOOMWE) showed the

highest frequency of 69 buses per hour, while the routes; Bunju – Kariakoo (BUNKOO),

Jet – Muhimbili (JETMUH), Kibamba – Manzese (KIBMNA), Kigogo Luhanga – Kariakoo

(KILKOO), Kariakoo – Tabata Segerea (KOOTSE), Sinza – Vingunguti (SINVIN),

Mbagala Rangi Tatu – Tegeta (MR3TEG). Few routes (Accessed through the FVO

surveys databank results section) have a frequency of 1 bus per hour, situation that could

be explained on the lack of control and service control when frequencies and dispatch

management is at stake. The entire system has an average frequency of 15 buses per

hour.

Detailed analysis is presented for those routes operating along Morogoro Road.

Table 19 Morogoro Road (Point 10) Routes Frequencies

ROUTE frequency KAWKOO 43 KAWMUH 2

KIBKIV 3 KIBKOO 8 KIBMNA 14 KIBMUH 15 KIBMWE 3 KIBPOS 4 KILKOO 1 KIMKIV 20

KIMKOO 39 KIMMUH 7 KIMPOS 33 KIVMAB 6 KIVMBE 9 KIVMBU 12

ROUTE frequency KIVMWA 5 KIVMWE 1 KIVUBU 18 KONTAN 30 KOOMAB 24 KOOMAN 21 KOOMBE 8 KOOMWA 20 KOOMWE 69 KOOSIN 28 KOOTAN 60 KOOTEG 38 KOOUBU 48 MABMUH 9 MABPOS 21 MANMUH 2

ROUTE Frequency MANPOS 4 MBEMUH 18 MBEMWE 19 MBEPOS 5 MBUMUH 25 MBUPOS 20 MR3MUH 5 MUHMWA 7 MUHMWE 23 MUHSIN 1 MUHTAN 35 MUHUBU 6 MWEPOS 67 POSSIN 19 POSTAN 36 POSUBU 36


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The entire list of frequencies of operating bus routes can be accessed from the surveys

databank FVOSu results section, Visit the surveys databank user manual on how to get

this information (Databank User Manual, Chapter 7) or the appendix:


4.1.6. BUS ROUTES ITINERARIES

From the field surveys monitoring and analysis of the acquired FVO surveys data, it has

been observed that several daladala routes are operating outside the authorized or usual

corridors and seen at points on corridors where they are not suppose to be operating.

Based on assumptions, given the current lack of authority and control on daladala

operation, the explanation for this facts might answer to several possible reasons ranging

from public transportation buses getting back to their depots or garages to dodging their

valid itineraries for reasons such as better roadways condition, passenger attraction,

avoiding road traffic congestion and to cut down the path getting ahead competitors.

This was identified by identifying those routes with frequencies below 3 buses per hour in

certain points. In spite of this assumption, it does not necessary imply that a bus route

with a frequency of one bus per hour has actually violated its pathway or regular itinerary.

Following this analysis, average frequencies for the bus routes are being presented in the

results annexed to the present document and heavily used for designing and modeling

purposes.

Explaining the previous fact, Kimara – Posta route was seen once at Ali Hassan Mwinyi

road (point 06) with occupancy of 3 passenger’s during the early morning time. This

might imply the bus was going to start the service coming from depot, the route was

running on another’s route itinerary, or simply the vehicle was out of service and simply

running within the city.


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Similar situation was observed with route Kimara – Kariakoo, being identified running

along Kilwa Road Corridor, area completely out of the normal itinerary path, usually along

Morogoro Road.

Figure 44 Kimara – Kariakoo Route Itinerary

The entire list of frequencies of operating bus routes can be accessed from the surveys

databank FVOSu results section, Visit the surveys databank user manual on how to get

this information (Databank User Manual Chapter 7) or the Appendix:


Appendix B – FVO Route Itineraries


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4.2. ORIGIN AND DESTINATION

Among the average demand characteristics one of the most influential is the uncertainty

that human behavior has when it comes to daily activities, although weekly routine

restraints the uncontrollability of it, it actually changes and modulates and so does the

Origin Destination Matrix.

A few things are essential to note for the simplification purpose, as people do not go to

the same places every day, but for a large part of the activities and trips repeat daily,

during the week days (going to work, school and other common trip purposes). The

transport demand matrix is equilibrate mostly by the fact that when one does not go to

this area there is always the probability that someone else might do such movement

under a similar way, as human activities require that people be at the same places at the

same time, there is a concentration of demand for transport in certain times: this causes

the visible and known phenomenon of the peak traffic hours in the morning and in the

afternoon. Thus the total number of trips from and to a certain area shall be similar every

day.

Population density as well as social and economic characteristics of an area are the

characteristics often used to articulate a transport model. Complimentary to these,

transportation surveys are required to calibrate the model to match the simulated

scenario with the present situation.

A total of 35,534 passengers were surveyed across 34 points of which 10,577 (30 %) of

the interviews were taken during the peak hour. File of the origin and Destination

samples can be found in the Appendix:

Appendix D – OD Survey Samples

4.3. MOST IMPORTANT ZONES

From analyzing the information concerning origin and destination more important zones,

the following figures aroused as valuable for DSM transportation patterns:


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Table 20 Top Ten Trip Production Wards

Ward Total Trips Generated % of the Total Kariakoo 7727 6.3% Buguruni 6090 4.9% Tandika 5786 4.7% Ubungo 4898 4.0%

Kijitonyama 4422 3.6% Magomeni 3997 3.2%

Kawe 3775 3.1% Sinza 3334 2.7%

Tabata 3310 2.7% Ilala 3105 2.5%

From the table, it is clear that are densely populated wards the ones that contribute the

biggest amount of trips in the city. Kariakoo ward represents a very important and

influential ward both generating and attracting trips, being a busy and intense commercial

and residential area.

Table 21 Top Ten Trip Attraction Wards

Ward Total Trips Generated % of the Total Kariakoo 15167 12.3% Kivukoni 10672 8.7% Ubungo 6213 5.0%

Upanga Magharibi 6072 4.9% Kijitonyama 5839 4.7%

Tandika 4545 3.7% Buguruni 4302 3.5% Jangwani 3628 2.9% Magomeni 3590 2.9% Kinondoni 3552 2.9%

As expected from the existing DSM passenger demand behavior, Kariakoo Ward stands

as the highest and most important attractor of trips with 12.3% of the total trip attraction in

DSM, being the most visited ward in the city. Followed closely, and also as expected,

Kivukoni ward stands with 8.7% of the total trips generated.

4.4. ANALYSIS BY MUNICIPALITY

4.4.1. ILALA MUNICIPALITY

Ilala contributes with 31.4% of the trip generation total and 46.6% of the total trip

attraction.


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Table 22 Ilala Municipality Trip Production and Attraction

Ward Total Trips Generated

% of the Total

Total Trips Attracted

% of the Total

Buguruni 6090 4.9% 4302 3.5% Chanika 289 0.2% 75 0.1% Gerezani 553 0.4% 876 0.7%

Ilala 3105 2.5% 3016 2.5% Jangwani 1959 1.6% 3628 2.9% Kariakoo 7727 6.3% 15167 12.3% Kinyerezi 106 0.1% 11 0.0% Kipawa 1152 0.9% 698 0.6% Kisutu 343 0.3% 1496 1.2%

Kitunda 377 0.3% 83 0.1% Kivukoni 1965 1.6% 10672 8.7% Kiwalani 1370 1.1% 1626 1.3%

Mchafukoge 494 0.4% 2039 1.7% Mchikichini 750 0.6% 753 0.6% Msongola 109 0.1% 174 0.1%

Pugu 227 0.2% 128 0.1% Segerea 2558 2.1% 1418 1.2% Tabata 3310 2.7% 2430 2.0% Ukonga 1938 1.6% 1097 0.9%

Upanga Magharibi 2067 1.7% 6072 4.9% Upanga Mashariki 440 0.4% 861 0.7%

Vingunguti 1744 1.4% 768 0.6% Total 38,674 31.4% 57,389 46.6%

Figure 45 Ilala Municipality Wards Origin/Destination Matrix Participation


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4.4.2. KINONDONI MUNICIPALITY

Kinondoni contributes with 45.2% of the trip generation total and 36.1% of the total trip

attraction.

Table 23 Kinondoni Municipality Trip Production and Attraction


% of the Total


% of the Total

Bunju 629 0.5% 258 0.2% Goba 145 0.1% 5 0.0%

Hanna Nassif 472 0.4% 309 0.3% Kawe 3775 3.1% 3055 2.5%

Kibamba 526 0.4% 317 0.3% Kigogo 2808 2.3% 666 0.5%

Kijitonyama 4422 3.6% 5839 4.7% Kimara 2322 1.9% 2486 2.0%

Kinondoni 3041 2.5% 3552 2.9% Kunduchi 2512 2.0% 1518 1.2% Mabibo 2756 2.2% 849 0.7%

Magomeni 3997 3.2% 3590 2.9% Makuburi 1350 1.1% 546 0.4%

Makumbusho 1545 1.3% 403 0.3% Makurumula 1898 1.5% 632 0.5%

Manzese 2679 2.2% 2125 1.7% Mbezi 1210 1.0% 1162 0.9%

Mburahati 1105 0.9% 670 0.5% Mbweni 50 0.0% 16 0.0%

Mikocheni 2046 1.7% 1641 1.3% Msasani 1898 1.5% 2914 2.4%

Mwananyamala 2867 2.3% 2273 1.8% Mzimuni 991 0.8% 47 0.0%

Ndugumbi 719 0.6% 4 0.0% Sinza 3334 2.7% 2752 2.2%

Tandale 1582 1.3% 578 0.5% Ubungo 4898 4.0% 6213 5.0%

Total 55,577 45.2% 44,421 36.1%


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Figure 46 Kinondoni Municipality Wards Origin/Destination Matrix Participation

4.4.3. TEMEKE MUNICIPALITY

Temeke contributes with 23.4% of the trip generation total and 17.3% of the total trip

attraction.

Table 24 Temeke Municipality Trip Production and Attraction


% of the Total


% of the

Total Azimio 843 0.7% 18 0.0%

Chamazi 110 0.1% 37 0.0% Chang'ombe 1238 1.0% 2238 1.8% Charambe 1733 1.4% 1333 1.1%

Keko 1280 1.0% 892 0.7% Kibada 54 0.0% 44 0.0%

Kigamboni 2472 2.0% 1198 1.0% Kimbiji 87 0.1% 5 0.0%

Kisarawe 79 0.1% 18 0.0% Kurasini 1908 1.6% 2029 1.6%

Makangarawe 610 0.5% 134 0.1% Mbagala 2493 2.0% 1907 1.5%

Mbagala Kuu 1958 1.6% 1354 1.1% Miburani 945 0.8% 504 0.4%

Mjimwema 262 0.2% 52 0.0% Mtoni 2549 2.1% 1718 1.4%

Pemba Mnazi 72 0.1% 33 0.0% Sandali 383 0.3% 204 0.2%

Somangila 67 0.1% 8 0.0% Tandika 5786 4.7% 4545 3.7% Temeke 2345 1.9% 2622 2.1%

Toangoma 192 0.2% 26 0.0%


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Ward Total Trips

Generated % of the

Total Total Trips Attracted

% of the Total

Vijibweni 138 0.1% 14 0.0% Yombo Vituka 1190 1.0% 305 0.2%

Total 28,797 23.4% 21,238 17.3%

Figure 47 Temeke Municipality Wards Origin/Destination Matrix Participation


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Figure 48 DSM Origin – Destination Matrix Distributed by Zones

4.5. VELOCITY BOARDING AND ALIGHTING

The VBA surveys data for the 28 surveyed routes was processed and analyzed to obtain

the operating parameters for the bus routes and it showed for the existing system to have

the following parameters as described below.


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Table 25 Selected Routes Operational Parameters

Useful from the VBASu is the possibility of generating a load profile per route surveyed,

showing occupation conditions and important production and attraction areas covered

through the route’s path. Giving an example of this feature enabled by this survey, the

passenger load profile for route Kimara – Kariakoo for the PM peak hour is displayed.


102

Figure 49 KIMKOO Load Profile PM Peak Hour

The full VBASu results can be accessed form the surveys databank VBASu results

section (Databank User Manual Chapter 7).

4.6. CLASSIFIED COUNTING’S

Classified Counting’s surveyed data was processed and analysis on the results

performed to obtain the hourly counts. The peak hour counts were then obtained to

validate the model against traffic information related to the city of DSM transport demand.

Information with more detailed (15 minutes period) can be obtained form the database.

For the analysis done the hour standard was used based on preference. Results for the

nine major means of transportation during the morning peak hour showed an overall

majority of cars to be dominating the roadway facility by usage within the existing system.

Table 26 Morning Peak Proportions for Transportation Modes

TRANSPORT TYPE COUNTS % AVERAGE

W1 W2 W1 W2 Small daladala 36,414 33,563 10% 12% 11% Large daladala 11,461 11,254 3% 4% 3.50%

Cars 126,923 61,349 35% 23% 29.00% Taxi 22,637 18,363 6% 7% 6.50%

Trucks 7,820 5,510 2% 2% 2.00% Buses 4,832 5,022 1% 2% 1.50%

Motorcycles 5,705 3,090 2% 1% 1.50%

Pedestrians 124,303 114,912 35% 43% 39.00%

Bicycles 20,354 16,463 6% 6% 6.00%


103

Figure 50 Morning Peak Modal Split

From the survey data, 39 % of DSM public transit road network is occupied by walking

inhabitants (non motorized transportation), 29% by private cars and 14.5% is occupied by

public daladalas (which are not necessarily occupied by passengers and sometimes

running on low occupancy levels). Only 6 % of the road network is occupied by bicycles,

during the peak hour.

Figure 51 Way 1 Full day Surveyed points all traffic profile

Proportion throughout the day between the different transportation means are in average

terms constant from the hourly analysis. Obvious increments on peak hours were seen

particularly on pedestrian and daladala flows.

Analysis on daladala proportions shows the preponderance of the small version

“Kipanya” over the larger ones “Coaster and DCM”. Also their share remains


104

constant when comparing overall hourly percentages and a reduction as usual during late

night hours. The following figure shows the flow pattern and discrimination between the

two daladala typology groups, small daladalas and big daladalas.

Figure 52 Way 1 Full day Surveyed Point Daladala traffic Profile

Figure 53 Way 2 Full day Surveyed points all traffic profile


105

Figure 54 Way 2 Full day Surveyed Point Daladala traffic Profile

4.6.1. POINT 10 CLASSIFIED COUNT RESULTS

As expected from previous field sightings, survey point 10 located at Jangwani region on

Morogoro Road, the share of public transportation traffic in the overall volume of

motorized transportation measured is slightly higher of the private modes. Daladalas

showed to have the highest occupancy on the road way.

Table 27 Morning Peak Proportions for Transportation Modes on Point 10

COUNTS % AVERAGE TRANSPORT TYPE W1 W2 W1 W2 Small

daladala 644 643 19% 25% 22%

Large daladala 89 103 3% 4% 3%

Cars 720 605 21% 24% 22% Taxi 510 243 15% 9% 12%

Trucks 59 56 2% 2% 2% Buses 44 37 1% 1% 1%

Motorcycles 55 70 2% 3% 2%

Pedestrians

1,005

716 30% 28% 29%

Bicycles 245 88 7% 3% 5%

Point 10 has showed to have the highest flow of commercial vehicles as well, with 577

(60.67%) of the total motorized traffic at the point daladalas and 455 (23.1%) commercial

vehicles, with an equivalent vehicles cars (EVC) of 1,982 vehicles on the way 1 direction

– towards town and 979 vehicles on the way 2 direction – away from form town.


106

Figure 55 Morning Peak Modal Split Point 10

Table 28 Point 10 Classified Counting’s Results

Figure 56 Point 10 Way 1 Full day All traffic Profile


107

Figure 57 Point 10 Way 2 Full day All traffic Profile


108

The full results information for the classified counting’s surveys can be accessed from the

surveys databank CCSu results section (Databank User Manual Chapter 7).

4.7. DIRECTIONAL FLOW RESULTS

The numbers/total hourly morning counts of vehicles making the turning movements at

the surveyed intersections were obtained after loading the DFSu surveys data into the

databank and it showed results as summarized in the following image description.

As part of the roadway design and particularly of that of traffic analysis and intersection

design, the measurement of volumes of traffic movements is the key element to evaluate

the level of service of a road. The fact that all major intersections’ performance will

eventually change the current operation of four phase traffic lights layout to a two phase

requires detail analysis and identification of existing conditions. Counts were then

focused on major intersections along Morogoro Road, Kawawa Road and Msimbazi

Street, since was the area of interest and the one being designed. Additional surveys

were performed on main CBD entrance streets such as Ohio Street, Maktaba/Azikiwe

Street, Zanaki Street, Uhuru Street and Nkrumah Street for further analysis on traffic

impact in CBD streets as a consequence of DART implementation along Morogoro Road,

Kawawa Road and Msimbazi Street.

As usual now, all the analysis was based on the identification of the critical period during

the day. Peak hour conditions offer the heaviest situation and the design is then directed

to obtain effective solutions to this scenario. The next figures show the results of these

counts during the morning and afternoon peak hour.


109


110


111


112

Figure 72 Point 57 – Kawawa Road and Bagamoyo Road Intersection


113

Figure 73 Point 58 – Kawawa Road and Dunga Street Intersection

Figure 74 Point 59 – Kawawa Road and Kinondoni Road Intersection

Figure 75 Point 60 – Kawawa Road and Mwinyijuma Street Intersection


114

Figure 76 Point 61 – Kawawa Road and Mlandizi Road Intersection

Figure 77 Point 62 – Msimbazi Street and Swahili Street Intersection

Figure 78 Point 63 – Msimbazi Street and Mafia Street Intersection


115

Figure 79 Point 64 – Msimbazi Street and UhuruStreet Intersection

Figure 80 Point 65 – Msimbazi Street and Lindi Street Intersection

Figure 81 Point 66 – Msimbazi Street and Nyerere Road Intersection


116

The full analytical graphics and data for the directional flow surveys can be accessed

from the databank DFSu results section (Databank User Manual Chapter 7).

4.8. STATION BOARDING AND ALIGHTING RESULTS

After conducting the boarding and alighting survey on the 13 stations along the Morogoro

road during the AM period, peak hour boarding and alighting volumes at each station

were computed to determine the most important stations.

The survey shows that the main boarding stations towards town are Ubungo, Bakheresa

and Mwembe Chai station, and the main boarding stations away from town are Fire,

Usalama and Bakheresa.

The main alighting stations towards town are Banana Market, Usalama and Fire. Away

from town are Manzese Darajani, Bakheresa and Ubungo station. Information of morning

peak hour is presented on the following tables.

Table 29 Morogoro Road Way 1 Boarding and Alighting Results


117

Table 30 Morogoro Road Way 2 Boarding and Alighting Results

The hourly Station boarding and alighting data for the other survey times of surveys can

be accessed form the surveys databank SBASu results section (Databank User Manual

Chapter 7).


118

4.9. CBD TRANSPORTATION ZONES UPDATE

Transportation zones give base to the modeling process since they are the

representation of travelers in a network crowded with lines (streets) and alternatives for

traveling (transportation modes). As explained on the Annex Volume 5 – DART

Operational Plan, transportation zones for DSM and their distribution and division were

supported on the existing regional division of wards and subwards. For CBD and central

dense regions subward division was primarily used. When analyzing the CBD area, it is

almost completely grouped by two big sunwards though the notorious land use

differences, road conditions and several other issues (Kivukoni, Kisutu, Mtendeni and

Mchafukoge Subwards).

Figure 82 CBD Original Division

For effectively analyzing the passenger demand on this crucial area, since stands as the

second trip attractor in the city, a more detailed division was required, thus making

necessary the breaking of the existing partition into more and dedicated zones.

Consequently, CBD was broken/divided from basically 3 subwards into 12 smaller

transportation zones.


119

Figure 83 New Transportation Zones CBD Division


120

5. RECOMMENDATIONS

Recent information (quantitative and qualitative), perfects the BRT designing plans,

hence the data must be as up to date as possible to perfect the decisions for the planning

process. Performing recent/frequent surveys can be a good practice in helping the

modeler to modify/adjust the model, by changing the allocation of resources, the time

needed for a task, or even inserting a new one for the purposes of perfecting it for

providing the best analytical results, in the future.


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APPENDIX

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