Department of Science and Technology Institutionen fr teknik och naturvetenskap Linkping University Linkpings universitet

gnipkrroN 47 106 nedewS ,gnipkrroN 47 106-ES

LiU-ITN-TEK-A-14/045--SE

A comparative study betweenEmme and Visum with respect to

public transport assignmentCisilia Hildebrand

Stina Hrtin

2014-10-10

LiU-ITN-TEK-A-14/045--SE

A comparative study betweenEmme and Visum with respect to

public transport assignmentExamensarbete utfrt i Transportsystem

vid Tekniska hgskolan vidLinkpings universitet

Cisilia HildebrandStina Hrtin

Handledare Ellen GrumertExaminator Anders Peterson

Norrkping 2014-10-10

Upphovsrtt

Detta dokument hlls tillgngligt p Internet eller dess framtida ersttare under en lngre tid frn publiceringsdatum under frutsttning att inga extra-ordinra omstndigheter uppstr.

Tillgng till dokumentet innebr tillstnd fr var och en att lsa, ladda ner,skriva ut enstaka kopior fr enskilt bruk och att anvnda det ofrndrat frickekommersiell forskning och fr undervisning. verfring av upphovsrttenvid en senare tidpunkt kan inte upphva detta tillstnd. All annan anvndning avdokumentet krver upphovsmannens medgivande. Fr att garantera ktheten,skerheten och tillgngligheten finns det lsningar av teknisk och administrativart.

Upphovsmannens ideella rtt innefattar rtt att bli nmnd som upphovsman iden omfattning som god sed krver vid anvndning av dokumentet p ovanbeskrivna stt samt skydd mot att dokumentet ndras eller presenteras i sdanform eller i sdant sammanhang som r krnkande fr upphovsmannens litterraeller konstnrliga anseende eller egenart.

Fr ytterligare information om Linkping University Electronic Press sefrlagets hemsida http://www.ep.liu.se/

Copyright

The publishers will keep this document online on the Internet - or its possiblereplacement - for a considerable time from the date of publication barringexceptional circumstances.

The online availability of the document implies a permanent permission foranyone to read, to download, to print out single copies for your own use and touse it unchanged for any non-commercial research and educational purpose.Subsequent transfers of copyright cannot revoke this permission. All other usesof the document are conditional on the consent of the copyright owner. Thepublisher has taken technical and administrative measures to assure authenticity,security and accessibility.

According to intellectual property law the author has the right to bementioned when his/her work is accessed as described above and to be protectedagainst infringement.

For additional information about the Linkping University Electronic Pressand its procedures for publication and for assurance of document integrity,please refer to its WWW home page: http://www.ep.liu.se/

Cisilia Hildebrand, Stina Hrtin

DEPARTMENT OF SCIENCE AND TECHNOLOGY

A comparative study between Emme and Visum

with respect to public transport assignment

Master Thesis carried out at Division of Communications- and Transport SystemsLinkpings University

November 2014

Cisilia Hildebrand

Stina Hrtin

Institute of Technology, Dept. of Science and Technology,

SE-581 83 Linkping, Sweden

Preface

The work presented in this thesis has been carried out in the Division of Communications-and Transport Systems at Linkpings University and at WSP Analysis & Strategy.First of all we want to thank our supervisor Ellen Grumert and examiner Anders Pe-terson at Linkpings University for their feedback during this project. We would alsolike to thank our colleagues at WSP for their support. A special thanks to our super-visor at WSP Analysis & Strategy, Christian Nilsson, that has guided us through thisthesis. Finally, we want to thank our families and friends for their support during theyears.

Cisilia Hildebrand and Stina Hrtin

Norrkping, November 2014

i

Abstract

Macroscopic traffic simulations are widely used in the world in order to provide as-sistance in the traffic infrastructure development as well as for the strategic trafficplanning. When studying a large traffic network macroscopic traffic simulation can beused to model current and future traffic situations. The two most common softwareused for traffic simulation in Sweden today are Emme and Visum, developed by INROrespective PTV.

The aim of the thesis is to perform a comparison between the software Emme and Visumwith respect to the assignment of public transport, in other words how passengerschoose their routes on the existing public transport lines. However, in order to makea complete software comparison the run-time, analysis capabilities, multi-modality,capacity to model various behavioural phenomena like crowding, fares etc. this willnot be done in this comparison. It is of interest to study the differences between thetwo software algorithms and why they might occur because the Swedish TransportAdministration uses Emme and the Traffic Administration in Stockholm uses Visumwhen planning public transport. The comparison will include the resulting volumes ontransit lines, travel times, flow through specific nodes, number of boarding, auxiliaryvolumes and number of transits. The goal of this work is to answer the followingobjective: What are the differences with modelling a public transport network in Emmeand in Visum, based on that the passengers only have information about the travel timesand the line frequency, and why does the differences occur?

In order to evaluate how the algorithms work in a larger network, Nacka municipality(in Stockholm) and the new metro route between Nacka Forum and Kungstrdgrdenhave been used. The motivation for choosing this area and case is due to that it isinteresting to see what differences could occur between the programs when there are amajor change in the traffic network.

The network of Nacka, and parts of Stockholm City, has been developed from anexisting road network of Sweden and then restricted by "cutting out" the area of interestand then removing all public transportation lines outside the selected area. The OD-matrix was also limited and in order no to loose the correct flow of travellers portalzones was used to collect and retain volumes.

To find out why the differences occur the headway-based algorithms in each softwarewere studied carefully. An example of a small and simple network (consisting of only a

iii

start and end node) has been used to demonstrate and show how the algorithms workand why volumes split differently on the existing transit lines in Emme and Visum.The limited network of Nacka shows how the different software may produce differentresults in a larger public transport network.

The results show that there are differences between the program algorithms but thesignificance varies depending on which output is being studied and the size of thenetwork. The Visum algorithm results in more total boardings, i.e. more passengershave an optimal strategy including a transit. The algorithms are very similar in bothsoftware programs, since they include more or less parts of the optimal strategy. Theparameters used are taken more or less into consideration in Emme and Visum. Forexample Visum will first of all focus on the shortest total travel time and then considerthe other lines with respect to the maximum waiting time. Emme however, first focuseson the shortest travel time and then considers the total travel time for other lines withhalf the waiting time instead of the maximum wait time. This results in that lesstransit lines will be attractive in Emme compared to Visum. The thesis concludes thatvarying the parameters for public transport in each software algorithm one can obtainsimilar results, which implies that it is most important to choose the best parametervalues and not to choose the "best" software when simulating a traffic network.

Keywords: assignment, Emme, macroscopic traffic simulation, public transport, Vi-sum

Contents

Preface i

Abstract iii

List of figures vii

List of tables ix

1 Introduction 11.1 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Paper outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.5 Research contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Travel forecasting 72.1 The four step travel forecasting model . . . . . . . . . . . . . . . . . . 8

2.1.1 Trip generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.2 Trip distribution . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.3 Mode choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.4 Route assignment . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Public transport assignment . . . . . . . . . . . . . . . . . . . . . . . . 11

3 Software products 133.1 Overview of macroscopic software products . . . . . . . . . . . . . . . . 133.2 Emme 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2.1 Public transport assignment . . . . . . . . . . . . . . . . . . . . 153.2.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3 Emme 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.4 Visum 13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.4.1 Public transport assignment . . . . . . . . . . . . . . . . . . . . 223.4.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.5 Assignment parameters and examples . . . . . . . . . . . . . . . . . . . 283.5.1 Assignment parameter settings in Emme and Visum . . . . . . . 283.5.2 Example with a small network, Emme . . . . . . . . . . . . . . 30

v

3.5.3 Example with a small network, Visum . . . . . . . . . . . . . . 333.5.4 Main differences between the algorithms . . . . . . . . . . . . . 353.5.5 Comparison between literature examples . . . . . . . . . . . . . 37

4 A case study 414.1 Model building in Emme . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.1.1 Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.2 Model building in Visum . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.2.1 Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.3 Model analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.3.1 Parameter analysis . . . . . . . . . . . . . . . . . . . . . . . . . 534.3.2 Line run time analysis . . . . . . . . . . . . . . . . . . . . . . . 534.3.3 Algorithm analysis with 100 demand . . . . . . . . . . . . . . . 544.3.4 Node analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.4 Emme 4, extended transit assignment . . . . . . . . . . . . . . . . . . . 56

5 Results 575.1 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.1.1 Emme 3 and Visum: base scenario . . . . . . . . . . . . . . . . 575.1.2 Emme 3 and Visum: future scenario . . . . . . . . . . . . . . . 60

5.2 Model analysis results . . . . . . . . . . . . . . . . . . . . . . . . . . . 635.2.1 Results from parameter analysis . . . . . . . . . . . . . . . . . . 635.2.2 Results from line run time analysis . . . . . . . . . . . . . . . . 695.2.3 Results from algorithm analysis with 100 demand . . . . . . . . 725.2.4 Results from node analysis . . . . . . . . . . . . . . . . . . . . . 73

5.3 Emme 4, extended transit assignment, results . . . . . . . . . . . . . . 76

6 Analysis 796.1 Comparison of the simulation results . . . . . . . . . . . . . . . . . . . 796.2 Software sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

6.2.1 Comparison of the parameter analysis . . . . . . . . . . . . . . . 826.2.2 Comparison of the line run time analysis . . . . . . . . . . . . . 896.2.3 Comparison of the algorithm analysis with 100 demand . . . . . 89

6.3 Comparison of the node analysis . . . . . . . . . . . . . . . . . . . . . . 906.4 Comparison of the algorithms for public transport assignment . . . . . 97

6.4.1 Extended transit assignment . . . . . . . . . . . . . . . . . . . . 97

7 Conclusions and future work 99

References 103

Appendix 105

List of Figures

1 Illustration of the four step travel model and what is included regardingthe decision in each step . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 The small network in Emme (not to scale) . . . . . . . . . . . . . . . . 303 Graphic result from transit assignment of the small network in Emme . 324 The small network in Visum (not to scale) . . . . . . . . . . . . . . . . 335 Graphic result from transit assignment of the small network in Visum . 356 Comparison between the reproduced example results in both Emme and

Visum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 The network limitation area of Nacka municipality, from Google maps . 418 The chosen alternative for Nacka metro . . . . . . . . . . . . . . . . . . 429 The network built in Emme . . . . . . . . . . . . . . . . . . . . . . . . 4610 Transit lines in the base scenario (each line is a separate color) . . . . . 4611 Transit lines in the future scenario (each line is a separate color) . . . . 4712 The network built in Visum . . . . . . . . . . . . . . . . . . . . . . . . 4913 Areas that the 100 demand will be assigned between . . . . . . . . . . 5414 The circled nodes that will be analysed . . . . . . . . . . . . . . . . . . 5515 Simulation results from the base scenario in Emme . . . . . . . . . . . 5916 Simulation results from the base scenario in Visum . . . . . . . . . . . 5917 Simulation results from the future scenario in Emme . . . . . . . . . . 6218 Simulation results from the future scenario in Visum . . . . . . . . . . 6219 The number of boardings . . . . . . . . . . . . . . . . . . . . . . . . . . 7320 Diagram over boarding difference between base and future scenario in

respective software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8121 Diagram over boarding difference between Emme and Visum in respec-

tive scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8222 Graphs comparing the result from the parameter analysis of boarding

time weight in each software . . . . . . . . . . . . . . . . . . . . . . . . 8323 Graphs comparing the result from the parameter analysis of wait time

factor in each software . . . . . . . . . . . . . . . . . . . . . . . . . . . 8424 Graphs comparing the result from the parameter analysis of wait time

weight in each software . . . . . . . . . . . . . . . . . . . . . . . . . . . 8525 Graphs comparing the result from the parameter analysis of auxiliary

time weight in each software . . . . . . . . . . . . . . . . . . . . . . . . 8726 Graphs comparing the result from the parameter analysis of n.o. transfer

penalty in each software . . . . . . . . . . . . . . . . . . . . . . . . . . 88

vii

27 Comparisons between the base and future scenario in Emme and Vi-sum at T-centralen with respect to the number of passengers boarding,alighting or passing through the station. . . . . . . . . . . . . . . . . . 90

28 Comparisons between the base and future scenario in Emme and Visumat Slussen with respect to the number of passengers boarding, alightingor passing through the station. . . . . . . . . . . . . . . . . . . . . . . . 91

29 Comparisons between the base and future scenario in Emme and Visumat Sofia with respect to the number of passengers boarding, alighting orpassing through the station. . . . . . . . . . . . . . . . . . . . . . . . . 92

30 Comparisons between the base and future scenario in Emme and Visumat Kungstrdgrden with respect to the number of passengers boarding,alighting or passing through the station. . . . . . . . . . . . . . . . . . 93

31 Comparisons between the base and future scenario in Emme and Visumat Nacka Forum (bus stop) with respect to the number of passengersboarding, alighting or passing through the station. . . . . . . . . . . . . 94

32 Node analysis made in the base scenario . . . . . . . . . . . . . . . . . 9533 Node analysis made in the future scenario . . . . . . . . . . . . . . . . 96

List of Tables

1 Line specific notation description for the algorithm section in Emme . . 172 Assignment variables that are generated from the simulation . . . . . . 223 Line specific notation description for the algorithm section in Visum . . 244 Parameter translation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Characteristics of the small network in Emme . . . . . . . . . . . . . . 306 Weighted times for each transit line . . . . . . . . . . . . . . . . . . . . 317 Algorithm first three steps for computing the optimal strategies in Emme 328 Algorithm last two steps for computing the optimal strategies in Emme 329 Characteristics of the small network in Visum . . . . . . . . . . . . . . 3310 Translation of assignment parameters from Emme to Visum . . . . . . 3311 In-vehicle, walk and origin wait times for the different routes . . . . . . 3412 Boarding, transfer and total wait times for the different routes . . . . . 3413 First step when computing the optimal strategies in Visum . . . . . . . 3414 Second step when computing the optimal strategies in Visum . . . . . . 3515 Attractiveness results from Emme . . . . . . . . . . . . . . . . . . . . . 3816 Attractiveness results from Visum . . . . . . . . . . . . . . . . . . . . . 3817 Relevant bus lines for the base scenario . . . . . . . . . . . . . . . . . . 4318 Relevant metro/light rail lines for the base scenario . . . . . . . . . . . 4319 Relevant bus lines for the future scenario (the new or changed transit

lines are stated in italics) . . . . . . . . . . . . . . . . . . . . . . . . . . 4420 Relevant metro/light rail lines for the future scenario (the new or changed

transit lines are stated in italics) . . . . . . . . . . . . . . . . . . . . . . 4421 Line run times from Emme and Visum (minutes) in the base scenario . 5122 Line run times from Emme and Visum (minutes) in the future scenario 5223 Output from the base scenario simulation in Emme and Visum . . . . . 5724 Total number of passengers boarding on each type of transit mode in

respective software, base scenario . . . . . . . . . . . . . . . . . . . . . 5825 Number of passengers boarding on each line in respective software, base

scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5826 Number of passengers boarding on each line in respective software, fu-

ture scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6027 Total number of passengers boarding on each type of transit mode in

respective software, future scenario . . . . . . . . . . . . . . . . . . . . 6028 Number of passengers boarding on each line in respective software, fu-

ture scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

ix

29 Results from the parameter analysis in Emme . . . . . . . . . . . . . . 63

30 Results from the parameter analysis in Visum . . . . . . . . . . . . . . 63

31 The difference between Visum and Emme with respect to the differencebetween the original results . . . . . . . . . . . . . . . . . . . . . . . . 64

32 Results in Emme with the original parameter settings . . . . . . . . . . 64

33 Parameter analysis results for the boarding time weight in Emme . . . 64

34 Parameter analysis results with wait time factor in Emme . . . . . . . 65

35 Parameter analysis results with wait time weight in Emme . . . . . . . 65

36 Parameter analysis results with auxiliary time weight in Emme . . . . . 66

37 Results in Visum with the original parameter settings . . . . . . . . . . 66

38 Parameter analysis results with boarding time penalty in Visum . . . . 66

39 Parameter analysis results with the formula for origin and transfer waittime in Visum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

40 Parameter analysis results with factor for origin and transfer wait timein Visum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

41 Parameter analysis results with factor for access, egress and walk timein Visum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

42 Parameter analysis results with factor for number of transfers (NTR) inVisum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

43 The new run times in Visum compared to the original run times in Emme 69

44 Line boardings with the new run times in Visum and the original runtimes in Emme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

45 Line boardings with the new and the original run times in Visum . . . 71

46 Mean in-vehicle time (minutes) for the five tests with 100 demand . . . 72

47 Number of boardings per line for the five tests with 100 demand . . . . 72

48 Node results from the base scenario in Emme . . . . . . . . . . . . . . 74

49 Node results from the future scenario in Emme . . . . . . . . . . . . . 74

50 Node results from the base scenario in Visum . . . . . . . . . . . . . . 75

51 Node results from the future scenario in Visum . . . . . . . . . . . . . 75

52 The result from using the standard transit assignment in Emme 4 andextended transit assignment (without any additional settings) . . . . . 76

53 Result from using the option flow distribution at origins . . . . . . . . 76

54 Result from using the option flow distribution at regular nodes withauxiliary transit choices . . . . . . . . . . . . . . . . . . . . . . . . . . 76

55 The result when using the additional setting to use flow distributionbetween transit lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

56 Comparison between Emme and Visum results from the base scenario . 79

57 Comparison between Emme and Visum results from the future scenario 79

58 Comparison of the results obtained from Emme with the Trafikverketparameter values and from Visum with the Trafikfrvaltning parametervalues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

59 Absolute boarding difference between base and future scenario in respec-tive software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

60 Boarding difference between Emme and Visum in respective scenario . 81

61 Absolute difference between the mean in-vehicle time from the analysiswith 100 demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

62 A concluding comparison between the algorithms . . . . . . . . . . . . 100

63 Difference in the number of boardings between base and future scenarioin Visum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

64 Difference in the number of boardings between base and future scenarioin Emme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

xi

1Introduction

When studying a large traffic network macroscopic traffic simulation (with aggregatedtraffic flow relations) can be used to model the current and future traffic situation.Macro simulation is often used as a part of travel forecasting at regional and nationaltraffic planning authorities and companies. An advantage with this type of simula-tion is that one can analyse and investigate a larger traffic network without investingin expensive infrastructure first. To obtain realistic results, the model must reflectthe reality accurate enough. This is done by calibrating the models, i.e. adjustingmodel parameters until the results resemble the observed or estimated data. Severalmacroscopic traffic simulation tools have been developed, with various advantages anddisadvantages. For example Emme (see manual [1]), Visum (see manual [2]), Aimsun(see website [3]), TransModeler (see website [4]) and VIPS (see report [5]).

In Sweden the most commonly used commercial macroscopic traffic simulation softwareproducts are Emme and Visum. The Swedish Transport Administration (Trafikverket)and several municipalities use Emme. Visum is the main macro simulation software atthe Traffic Administration in Stockholm (Trafikfrvaltningen) along with other trafficplanning companies and municipalities. Emme and Visum are the two main com-petitors of the traffic software market in Sweden and they are therefore of interest tocompare.

This project will provide knowledge of how the macroscopic traffic simulation softwareEmme and Visum differ regarding traffic assignment in terms of public transport. Inorder to compare these software algorithms, the existing traffic network of Nacka (aStockholm municipality) is studied. There are plans for an expansion of the existingmetro in Stockholm. This is an infrastructure investment by the Swedish government,Trafikverket, Trafikfrvaltningen, Stockholm County Council, Stockholm and Nackamunicipalities with on-going preliminary studies and is therefore of interest to studyfurther how it will affect the public transport system. This thesis will use the extensionof the metro as an example of a project often used within traffic planning. With thehelp of macroscopic traffic simulation one can investigate how the metro will affectother parts of the traffic network. The models in Emme and Visum need to be verifiedand then the infrastructure project, to build a metro to Nacka, added to the modellednetworks. The results for both the scenario with and without metro will be comparedbetween the software products in terms of assignment of public transport. Input dataneeded for both scenarios will be collected, in collaboration with WSP Analysis &Strategy and Trafikverket, from their earlier traffic prediction studies in Sweden.

1

1 INTRODUCTION

1.1 Aim

The aim of this master thesis is to perform a comparison between the software productsEmme and Visum with respect to the assignment of public transport. Headway basedassignment in these programs will be used, which means that the travellers will onlyhave information about the travel times and the line frequency.

The traffic network that will be the study area is Nacka municipality and the newstretch of the metro between Nacka and Kungstrdgrden. The comparison shall con-sist of both the traffic network before the metro extension (base scenario) and the trafficnetwork after opening the new metro (future scenario). Both the base and future sce-nario will have the same traffic volume, which are based on a future travel forecastfrom Trafikverket. The goal is to provide a greater understanding of how these twomacroscopic traffic simulation software products performs and what their differencesare regarding the assignment of public transport.

The aim can be described by the following objective:

What are the differences with modelling a public transport network in Emme and inVisum, when using headway based assignment, and why do the differences occur?

To provide an answer to this question the two simulation models in both base and futurescenario are required to be as similar as possible, which means that the networks inEmme and Visum could be in need of adjustment with respect to pathways, metro andbus etc. In order to evaluate the software products sensitivity regarding the publictransport assignment parameters this will also be analysed in the base scenarios.

1.2 Limitations

Data collection will not be done in this thesis, the relevant data is already availablefrom previous projects in the Stockholm area. There is a ready-made model in Emmefrom traffic prognoses and this will be imported to Visum in order for the models to beas similar as possible. In the two future scenarios the metro will be added to the basescenarios and the same input data will be used to be able to compare all scenarios.

If the entire traffic network of Stockholm were to be studied, the project would becometoo extensive. Therefore the area will be limited to Nacka municipality and the areaalong the route of the proposed metro. This means that the OD-matrices that areavailable needs to be adapted to this area. In Chapter 4 there is a more detailed de-scription of the traffic network and available data. The results of this example networkmay or may not extend to other networks, therefore other examples of smaller networkshelps to explain the actual differences along with a description of the algorithms.

The report will only include studies of public transport, i.e. bus, light rail and metro,instead of using demand matrices for public transport, car and truck which would makethe project too extensive. The specific assignment procedure that will be analysed inthis thesis is called headway-based, which is suited for public transport areas withhigh frequency transit lines. The headway can be explained as the time between twovehicles of the same transit line serving a node. This type of assignment procedurerequires only a few types of input data, i.e. line frequencies and travel times. Since

2

1.3 Method

the analysis regards the future traffic situation there are only frequencies and traveltimes available and this assignment method is therefore suitable for this procedure.The headway assignments for Emme and Visum are based on the optimal strategies,where the passengers choose the first transit line that arrives from an optimal set oflines.

Since calibration is not the main focus in this thesis, only a comparison of the origi-nal models and the base/future scenario model will be performed so that the modelsproduce realistic results. Due to this, the future scenario results will not show how themetro actually affects travelling in Stockholm and Nacka. They will at best show anelasticity measurement of the movements from bus to metro etc. However, an impor-tant analysis will be the differences in public transport assignment between the softwaremodels with and without the new metro, which can be done without calibration of themodels.

There is no known scientific basis for these parameters collected from Trafikverket(Emme) or Trafikfrvaltningen (Visum). Due to this one cannot draw any conclusionsregarding what parameters that are best at representing the reality, since the modelsused in this thesis are based on future prognoses and cannot be validated. Furtherstudies are then needed concerning calibrations or evaluations of the assignment pa-rameters, mentioned in section 2.2.

In order to make a complete software comparison the run-time, analysis capabilities,multi-modality, capacity to model various behavioural phenomena like crowding, faresetc. this will however not be done in this comparison.

1.3 Method

The main objective is to compare the macroscopic traffic simulation software products,Emme (version 3) and Visum (version 13), with respect to the public transport as-signment and examining the reasons for the result differences. By adjusting the modelparameters such as weights for waiting and boarding time, and by adding the newmetro line a more thorough comparison can be made. The future scenario models willbe used for further comparison between the software products and only to some extentused for evaluation of the distribution of public transport passengers.

An evaluation of the extended transit assignment in Emme 4 will also be performedin order to determine if there are any significant differences between using standard orextended transit assignment. It is also interesting to see if the difference between theextended assignment and the assignment in Visum.

In order to perform a comparison between the two software algorithms, a traffic networkwith the same conditions is created. To ensure that both the road network and publictransport routes are consistent in both Emme and Visum, a network is developed inEmme and then imported to Visum. See Chapter 4 for a more detailed descriptionof the adjustment of the road network, travel matrix and transit lines. In this thesisthe most interesting outputs are in-vehicle time (how long time the passenger spendsin transit vehicles), origin wait time (how long time the passenger waits at the origintransit stop), transfer wait time (how long time the passenger waits at transfer stops),

3

1 INTRODUCTION

total transit volume, total number of transfers (transfer volume), transit volume ondifferent lines, the number of passengers that walks the whole way from origin todestination, and average number of transfers per passenger. These outputs will beused in the comparison between the software algorithms.

To compare the software algorithms, theory regarding macroscopic traffic simulationand the theoretical models in each program will be studied. The manuals that describeand explain the underlying mathematical methods in both software algorithms havebeen a key part of the comparison. As a complement to the manuals, scientific articleswith relevant content have also been used to gain a broader and deeper understandingof the assignments.

In order to ensure that this thesis is of good quality regarding the technical content,the report will be revised by the developer of Emme, Michael Florian and Hans-JrgenDon from Visums Traffic Customer service. This is done to make sure that nothingimportant will be overlooked or misinterpreted.

1.4 Paper outline

The report begins with a literature review regarding the travel forecasting, presentedin Section 2. An overview of travel forecasting and how it can be used for predictingchanges in a traffic network is given. Focus will be on the different simulation methodsthat can be used as a part of the prediction. It contains a general description of thedifferent simulation approaches and a more detailed description of the advantages anddisadvantages of macroscopic simulation.

The four step travel model will be described in Section 2.1, which is a commonly usedmethod for a prediction. The method includes both the estimation and calculationof trips and usage of simulation software which give the travellers itinerary. The foursteps will be described separately but the main focus will be on step 3 (mode choice)and 4 (route assignment) because they are essential in macroscopic traffic simulationsoftware such as Emme or Visum.

Chapter 3 contains an overview of the two most frequently used macroscopic simula-tion software products in Sweden. Focus will be on Emme 3 (version 3.3.4), 4 andVisum 13 regarding the public transport assignment with corresponding parametersand algorithms. There is an explanation of the mathematical foundation regarding theassignment for both software programs. It also contains an example of how the optimalstrategy is obtained for a small transit network. Some differences and similarities willalso be described regarding the transit assignments.

A description of the study area is presented in Chapter 4, and contains the transitline network which is the foundation of the case study (both base scenario and futurescenario). It includes an explanation of how the network, with associated demandmatrix, was developed and verified.

The results of the simulation runs are showed in Section 5. Tables and diagrams willrepresent the output measurements of importance.

Chapter 6 contains comparison and analyses regarding the sensitivity analyses and

4

1.5 Research contributions

results of the simulations from the previously mentioned chapter. A comparison of thetransit assignment algorithms can also be found in this section.

In Chapter 7 the final conclusions are stated and directions for future research, aremade within the subject area.

1.5 Research contributions

The result of this thesis might contribute to a deeper understanding of how the twosimulation software algorithms calculates the optimal route for each traveller withrespect to travel time. Simulations are often a part of a bigger investigation concerninglarge changes in the transport system. It is of importance that the simulations areanalysed and performed in a correct way. The investigation decision, which is partlybased on the simulation results, can cost a lot of money and affect a lot of people usingthe transport system. In many companies and authorities one of the two simulationsoftware is chosen as a standard program. However, why they have chosen that specificsoftware instead of the other is often rather unclear. The conclusion of this thesis willhopefully help to understand how the algorithms work. It is important to make gooddecisions from the analytical results since almost everyone in the community will beaffected by the changes concerning the transport system which might be done basedon the simulations.

When calibrating (adjusting model parameters in order to obtain results which matchesmeasured values) a transit network model, the link specific parameters are often changed.By performing an analysis regarding the assignment parameters for the entire network,this thesis might contribute to using these parameters for calibration instead of keepingthem fixed. This might produce more accurate and realistic simulation results.

5

2Travel forecasting

Travel forecasting can be used for predicting changes in a traffic network. A commonmethod for performing travel forecasting is the four step model where a macroscopicsimulation software tool is a part of the procedure. A travel or traffic forecast is aprediction of how the traffic will change in the future and are always based on externalconditions and factors. To create a model with good accuracy the model primarily needto include socio-economic data and a transportation network. This chapter aims toprovide a short description regarding travel forecasting including the four step modeland an overview of how the public transport assignment works. For more informationabout travel forecasting and the four step model see Ortuzar and Willumsen [6], Hydnet.al. [7], WSP [8], National Cooperative Highway Research Program [9], Californiadepartment of transportation [10], and Lind [11].

The traffic forecasting can partly be carried out using a computer based software with amodel of the traffic network. Regarding the transportation network and specific publictransport network (transit network) the coding can be complex. There can be a bigrange of available modes such as local bus, express bus, light rail, commuter rail andbus rapid line. The lines service is often different in peak and off-peak hours duringthe day. The total flow is studied in a macroscopic simulation and the individualbehaviour of the vehicles is not taken in consideration. The road network is of a largerscale and may include traffic network for an entire city or country and the simulation ismade section by section. Emme and Visum are macroscopic simulation software usedwhen carrying out a travel forecast. The model that is created in the traffic simulationsoftware is a simplified version and representation of the real world network that is ofinterest.

The forecasting method can be used for more than prediction of the future, it is alsoused to investigate how the travel pattern will change when modifying or changinganything in the traffic infrastructure. The model may be useful when analysing theeffect of editing the road design, altering the public transport supply or implementingtolls. The result of prediction models can be a part of the decision making processwhen deciding about changes regarding the traffic system. By forecasting it is possibleto compare the effects of alternative actions so the decision maker easier can determinewhich action or actions will affect the transport system in the desired direction. Themost common reason and aim for a traffic prediction is to investigate changes in the flow(traffic volume), after modifying the traffic network. This shows how the modificationhas affected the traffic system in terms of more, less or shifted traffic volume. It can

7

2 TRAVEL FORECASTING

also be the number of trips and vehicle kilometres for different transport modes ondifferent roads and transit lines. It may as well provide an indication of the expectedtraffic congestion in the area.

The choice of forecasting procedure is a trade-off between the wanted accuracy ofthe prediction and available resources regarding time, money, effort and available re-sources. It is not always the case that the procedure chosen gives the most accurateresult because that requires a lot of work, for example extensive data collection andprecise calibration. What kind of forecast procedure used also depends on the under-lying reason for the analysis and the forecast duration period. There are three types ofsimulation approaches: microscopic, mesoscopic and macroscopic simulation. In a mi-croscopic simulation model the individual vehicles and their behaviour are studied. Theroad network is relatively small and can for example consist of a roundabout. Whendesigning control strategies for different functions (e.g. traffic lights) and analysingactual investment regarding traffic information, the model needs to be complementedby more detailed models. This type of model requires more input data with additionalcoding and is therefore significantly more resource intensive. Traffic analysts oftenlimits the network to a smaller geographic area, which is more suitable for microsim-ulation. Mesoscopic simulation is between micro- and macro level. The individualvehicles are simulated but the activities and interactions are described in macroscopicrelationships. This approach is often used when evaluating traveller information sys-tems. In a macroscopic simulation model the total flow is studied and the individualbehaviour of the vehicles is not taken in consideration. The road network is of a largerscale and may include traffic network for an entire city or country and the simulation ismade section by section. Emme and Visum are macroscopic simulation software usedwhen carrying out a travel forecast. The model that is created in the traffic simulationsoftware is a simplified version and representation of the real world network that is ofinterest.

In the next section the four step travel model is described shortly, where the softwareprograms Emme and Visum are used in step 3 and 4 of the travel model.

2.1 The four step travel forecasting model

The four step model is a commonly used approach for traffic modelling and consists offour distinct steps which are well described in the literature, for example in Ortuzar andWillumsen [6], see Figure 1 for an illustration of how the four step model works.

1. Traffic Generation

2. Trip distribution

3. Mode choice

4. Route assignment

8

2.1 The four step travel forecasting model

Figure 1: Illustration of the four step travel model and what is included regarding thedecision in each step

2.1.1 Trip generation

This step is described in [9] and the objective is to estimate the number of trips of eachpurpose type that begin or ends in each zone. The estimation is based on the amountof activity in the zone. The number of trips generated in this step are the flow usedin the model. Usually the trips are vehicle and person trips (auto or transit) whichoften includes both walking and bicycle modes. The results and the outputs from thetrip generation model are trip productions and trip attractions for each zone and trippurpose.

2.1.2 Trip distribution

This step calculates the percentage of the total traffic that will travel between eachpair of zones. The result can be presented in a matrix where each row and columnrepresents a zone. Each value in a cell in the matrix represents the number of tripsbetween the zones and this is called a travel matrix or OD matrix, where Tij is thedemand from zone i to zone j. Often the travel demand varies over time and differentmatrices may be used to study different time periods.

2.1.3 Mode choice

The purpose of this step is to split the trips between the zones by different travel modes.The definition of modes depend on the areas supply of transportation and what typeof transportation analysis that is required. The modes can commonly be divided intothree groups: auto-mobile, transit and non-motorized. The choice of mode can depend

9

2 TRAVEL FORECASTING

on the range of transport modes, travel time with the particular mode and the cost.There are different approaches for the mode choice but according to Hgerwall Stein,[12], the most common is the logit model. There are a number of logit models but oneof the more basic versions is the so called linear model, see the formula below:

Pj =e

aixi(j)

k e

aixi(k)(1)

where Pj is the proportion of the known total value, distributed on alternative j, xirepresent the characteristic (e.g. time, cost), ai is the weight for the respective xi andk is the available transport mode.

In order to obtain the distribution of the number of trips for each travel mode T kij ,following calculation is made with the result from the trip distribution and logitmodel:

T kij = TijPj (2)

2.1.4 Route assignment

The route assignment calculates how the forecasted travellers will be distributed amongdifferent links in the transport network (included non-motorized links) or the transitlines. How the route assignment works depend on the software used and what is beinganalysed. There are different types of assignments and the two main ones are: the auto-mobile assignment that handles routing of vehicles and the public transport (transit)assignment that deals with routing of linked passenger trips (which include walks,boarding and alighting). Depending on the software there can be more alternatives,variants or combinations of these two assignments.

The flow unit in an auto-mobile assignment is the number of vehicles and in a transitassignment the number of passengers. Another difference between the two assignmentsregards that the transit assignment have line routes that consists of a set of links,called line segments. When determining the perceived travel time of the passengersan impedance function is computed. In the route assignment this function is usedin order to divide the demand on each route. The impedance function reflects theunwillingness to travel and increases with longer total travel time. The impedancefunction in transit assignment, compared to the auto-assignment, also contain levelof service variables that are not included in the auto-mobile assignment such as waittime, boarding time, and auxiliary time (walk time). The trip between two nodes canbe served by more than one transit line and the lines can have different modes (e.g.city bus, express bus, metro).

When the travellers have decided on using public transport, the demand is assignedto different transit lines. There are different types of transit assignments dependingon the environment and available time table of the public transport. The assignmentprocedures available vary depending on the software and an example of the most com-mon are: transport system-, headway- and timetable-based. When the purpose is to

10

2.2 Public transport assignment

evaluate the entire system instead of analysing individual transit lines the transportsystem-based procedure is used. This requires no transit line network and is used tocreate a public transport network where the passengers chose the shortest routes. Atimetable-based procedure should be used for transport systems that have lines withlong headways, e.g. long-distance trains or transit lines in a rural area. To be able toperform a timetable-based assignment it requires complete timetable information, triparrivals and departure times. Headway-based assignment is based on optimal strategytheory which requires frequencies and travel times for the relevant public transportlines. Since this type of procedure does not demand exact timetables it is only appro-priate for long-term transport planning when the schedules are undetermined.

2.2 Public transport assignment

When assigning the demand to a public transport network there are several methodswith different purposes, which are described in the manuals [1] and [2]. First of all thereis a standard assignment called headway-based, which mainly takes the frequency ofthe transit lines into consideration. This is best suited for a larger cities with frequentlydeparting transit lines. However it is not suited for rural areas where the lines mightdepart less frequently. For those cases one might use the timetable-based assignmentinstead. This variant requires a complete timetable for all transit lines available. Thereis also a headway/time-based assignment, which uses both frequency and the traveltime in order to decide which will be the optimal route. Another variant of the publictransport assignment is the transport system-based, which creates a completely newpublic transport network based on the existing infrastructure.

The weights used in the algorithms are based on some valuation of the economic costfor the community. Wait, walk and transfer time is weighted more than travelling ina vehicle because people values that time higher. The time is calculated as a cost,so called generalized cost, and the travellers wants to minimize that cost. The timecan be categorized and when it comes to public transport system the time is valueddifferently. The time elapsed while transferring between two lines are interpreted aslonger than the actual time is.

The assignment weights can be adjusted in order to obtain a model of the publictransport network that resembles the real life network. The parameters in this thesishave been collected from one of the Swedish Transport Administration Emme models,which consists of the recommended parameter settings. The different parameters andtheir values can be seen later on in Section 3.5.2. Other literature describes calibrationmethods and how specific networks have been calibrated with respect to the differ-ent assignment parameters. The network reports have used two kinds of calibrationmethods; using data from transit lines, using data from surveys regarding travellerbehaviour. The first method have been used in Parveen et.al. [13] and Fung [14], whilethe second methods have been more frequently used by Horowitz [15], Wardman [16],Abrantes and Wardman [17], Kurauchi et.al. [18] and Rydergren [19]. For exampleRydergren describes in [19] how a Stockholm network have been calibrated againstbehaviour surveys with different assignment algorithms. The conclusions drawn fromthese mentioned reports are that the assignment parameters needs to calibrated afterdifferent software products, assignment algorithms, assignment settings, and network

11

2 TRAVEL FORECASTING

area. Therefore the correct way to handle these parameters is to adjust them accordingto different situations. No previous parameter translations between Emme and Visumexists, at least according to the authors knowledge, in the headway-based assignment.Therefore an assumption will be made from studying the manuals and examining howthe parameters work in each software. The parameters that will be translated areweights, factors and/or penalties for how the passengers perceive boarding, walking,travelling with a public transport vehicle, and waiting time compared to the time theycould spend in an auto-mobile instead. This translation is presented in Section 3.1,Table 4.

Logit distribution is a probability distribution (see equation 1) where the flow aresplit up according to the assigned percentage. The flow will be adjusted according toequation 2, which considers the distribution on different modes, connectors betweenorgin nodes and the network etc. By using a logit distribution one will force the demandto chose different connector links between origin nodes and the network, boardinga transit line versus walking to another station in order to obtain a shorter traveltime, or transferring to another transit line instead of staying on-board. According toFlorian and Constantin, [20], the logit distribution will even out the transit travellerson different paths in several situations where they would all choose the same travelstrategy. This leads to a more realistic result where passengers choose different routesfrom origin to destination.

12

3Software products

This chapter contains information about different macroscopic simulation tools and es-pecially focuses on the two most commonly used, i.e. Emme and Visum. More detaileddescriptions of these software products are therefore presented with respect to publictransport assignment and the algorithms available. The Section 3.2 and 3.4 describeshow the public transport assignment works in the respective software. These sectionsalso contain descriptions of the transit assignment algorithms. Visum have severalalgorithms, however, only the algorithm that corresponds to the Emme 3 standardalgorithm will be thoroughly defined. Other algorithms are also available in Emme 4and they will be presented in Section 3.3. Then, in Section 3.5.4, the mathematicaldissimilarities of the software algorithms are reviewed. The software manuals, Emme 3[1], Emme 4 [21] and Visum 13 [2], have been used in this chapter and are the sourcesif nothing else is stated.

In the article by Florian and Spiess, [22], there are an explanation of how the optimalstrategies work and an example with a small public transport network. Some previouscomparisons between Emme, Visum and similar software have been studied and used asa foundation to this thesis. The conclusions of these comparisons have been of interestfor further investigations. Also, identification of weaknesses in the comparisons hasbeen important. Some of the comparisons studied are Johanssons report [5], Larsensreport [23] and Hgerwall Steins master thesis [12], where the first two papers examinesboth Emme and Visum (based on VIPS algorithms) with respect to public transport.However, no thorough comparison of the algorithms has been made. [12] focuses on theauto-mobile assignment and has therefore been used for software facts and comparisonmethod.

3.1 Overview of macroscopic software products

The following software products contain more or less macroscopic traffic simulationfeatures; Emme, Visum, Aimsun, TransModeler, and VIPS. Aimsun is, according toTransport Simulation System:s website [3], a hybrid between a micro-, macro-, andmesoscopic traffic simulation software. However, the main focus is on microscopicsimulations and therefore it is not often used for macroscopic traffic networks such asSweden or the Stockholm region. TransModeler simulation software is also a hybridbetween the three types of detail levels; macro, micro and meso. The TransModeler

13

3 SOFTWARE PRODUCTS

website [4] states that the software contains simulation models such as toll facilities, on-street parking, signal control, dynamic traffic assignment and a combination of microand macro (the areas of most interest are modelled on micro level and the rest atmacro level). There are also public transport assignments that are based on headwaysor timetables. This software is not commonly used for public transport assignmentin Sweden. There are however some Swedish traffic projects that uses TransModelerfor dynamic auto-mobile assignment. VIPS (Volvo Interactive Planning System forpublic transport), described in Johansson [5], was developed by Volvo TransportationSystem in Gothenburg and have previously been frequently used at Trafikfrvaltningen.VIPS is a macroscopic transport simulation software that was incorporated with Visum.Version 13 of Visum, contains some parts of the VIPS algorithm and there are relativelyfew traffic analysts that still uses VIPS. In Sweden most municipalities and countiesuses traffic planning tools such as software with macroscopic traffic simulation for futuretraffic predictions etc. Trafikverket, for example, have incorporated Emme in theirtraffic prognosis software that they use for all parts of Sweden and Trafikfrvaltningenuses Visum instead. Since these two essential infrastructure operators in Sweden usesEmme and Visum they are of interest for further investigation. They have previouslybeen compared in Johanssons report [5], Larsens [23], and Hgerwall Steins masterthesis [12] that will be described below.

A comparison was made in 1984 between Emme and VIPS, which was incorporated intoa previous version of Visum, described in [5] at Stockholm county council (TrafikontoretStockholms lns landsting) by Johansson. The software VIPS was used to analysechanges in the transit network, however the new launched Emme software had alsorecently been installed at the office so it was of interest to evaluate both programs.The aim was to compare the result of the chosen itineraries by the software programsagainst a made survey. In the survey 100 persons per node-pair were asked abouttheir itinerary. The same transit line network over central parts of Stockholm was usedtogether with an OD-matrix for time period of an average hour between 07 : 0009 : 00in VIPS and Emme. According to the results VIPS generated more volumes (+12%)on the buses compared to the survey and Emme lower volumes (7%). Passengers splitup more on different itineraries between an origin and destination in VIPS compared toEmme. The average number of transit per passenger is 0.63 in VIPS and 0.59 in Emme.In Emme almost every one chose the same itinerary. The average absolute differencebetween the survey and the models regarding the number of boardings on each bus lineis 30% for VIPS and 15% for Emme. Johansson, [5], also made a comment on that thepenalty of transfers of five minutes is calibrated for VIPS against the survey results.The author mentions a desire to continue the comparison with calibrating the transferpenalty against Emme.

In 2011 a comparison between Emme and Visum was made regarding the transit as-signment by Larsen, [23]. He mentions some differences but does not really give anexplanation of how he comes to certain conclusions. There are some examples butthe calculations are missing and only the final answer is stated. However, the idea ofhaving a simple example to point out how the algorithms works is a good idea and willbe used in this master thesis as well.

The traffic (auto-mobile) assignment with network equilibrium was compared, betweenEmme and Visum, and evaluated by Hgerwall Stein in 2007, [12]. Even though adifferent assignment was compared the same method as in this report was used. A

14

3.2 Emme 3

limited part of a network was developed in Emme and later imported into Visum.The same OD-matrix was used in both software programs. His conclusion was thatthe result is similar regarding the flows on the links and routes which implies thatboth Emme and Visum are based on the same algorithm (network equilibrium). Thedifferences were mainly caused by the rounding of the numbers in the OD-matrix.

3.2 Emme 3

Emme (Equilibre Multimodal, Multimodal Equilibrium), described in the Emme man-ual [1], developed at the Centre for Research on Transportation (CRT) at the Universityof Montral in the seventies. In the eighties the first commercial Emme version wasdeveloped at the CRT, called Emme 2. Professor Michael Florian is one of the foundersof INRO and the software Emme, and he had a key role in developing the modules usedregarding the transit assignment. The Emme 4 manual [21] states that improvementshave been made and new versions of the software Emme have been released. Emme3 includes a graphic interface for network editing, more tools for simulation, analysisetc. In Emme 4 there is a congestion assignment tool that models crowding, discom-fort on vehicles, capacity limits and increasing waiting time. There are continuouslyongoing development regarding the interface, analysis, implementation of virtually andzone-level travel demand model etc. Emme is used in over 85 countries, including Aus-tralia, Canada, USA, South Africa, Central and South America, across Asia and mostEuropean countries.

The macroscopic traffic simulation software Emme is used for modelling urban, regionaland national traffic systems. Emme is a traffic analysis tool that is used by trans-portation planners and traffic analysts around the world. This section will describe thesoftware and some of its features with respect to the public transport assignment.

3.2.1 Public transport assignment

The transit assignment is based on the theory of optimal strategies approach by Florianand Spiess, [22]. The public transport assignments in Emme consists of headway-based,headway/time-based and timetable-based transit assignments, however the transportsystem-based assignment is not included in the currently marketed versions.

The transit network consists of centroids (zones), regular nodes, links and a set oftransit lines. A transit line contains of a set of nodes and a set of links. The se-quence of nodes represents the itinerary and where the travellers may board or alight.Each link can have more than one transit line, which consists of several transit linesegments.

The transit assignment algorithm aims to minimize the total travel time containing;wait time, auxiliary time, boarding time and in-vehicle time. The time componentsare weighted to compare these times with the in- vehicle time. The total travel timeis converted into a general cost (TTT ), in other words the traveller wants to minimizethe total cost. The time is defined for each line segment and ads up to the total traveltime for the entire trip. The wait time factor scales the time a traveller has to wait

15

3 SOFTWARE PRODUCTS

at a node for an attractive line. The factor is also used to define the waiting timestogether with the waiting time at a specific node. The value can differ between 0.01to 1 and can either be node specific or the same for the entire network. The wait timeweight, that describes how much the wait time is valued compared to the in-vehicletime, can be set between 0999.999, as well as the parameter for boarding time. Thevalue can be the same throughout the whole network or be node/line specific. Theboarding time weight is also set between 0999.999 and should represent, in relationto the in vehicle time, how much boarding or a transfer is worth. The auxiliary timeweight and spread factor must also have the value 0 to 999.99.

After each transit assignment one can obtain an assignment report and matrices with;transit times, in-vehicle times, auxiliary transit times, total waiting times, first waitingtimes, boarding times, and average number of boardings. Graphical results are alsoavailable in Emme 3, with options for comparisons between two scenarios.

A line i is attractive if the travel time of that line is lower than another attractive linestotal travel time, including wait time. This means that it is more profitable to boardline i if it arrives directly than it is to wait for a faster attractive line. The waitingtime at a node, twt, depends of the combined frequency (i) for the attractive lines (inthe optimal set of lines i I), see equation (3) obtained from Nilssons educationalmaterial, [24]:

twt =1

iI i

(3)

16

3.2 Emme 3

3.2.2 Algorithm

Some of the notations mentioned in this section are described in Table 1 below.

Table 1: Line specific notation description for the algorithm section in Emme

Notation Explanation of notation

twt Wait timewwt Wait time weight and factortaux Auxiliary timewaux Auxiliary time weight

tboarding Boarding timewboarding Boarding time weightttravel Travel timeTTT Total expected travel time, equation (4)

Also called impedance function and generalized costa Link indexA All links in the networkA All links which includes the optimal linesi Line indexI All lines in the networkI All optimal lines, i.e. lines selected

according to the optimal strategies algorithmn Node indexN All nodes in the network Line frequencyh Line headwaypi Combined line probability (share of the total demand), equation (5)

TTT

TTT without wait time (twtwwt), equation (12)TTT Total expected travel time of the optimal lines, equation (7)

The passengers want to minimize the travel time for the entire trip. The formula forthe total expected travel time (TTT) is shown below in equation (4).

TTT = wauxtaux +wwttwt +wboardingtboarding + ttravel (4)

The educational report by Matti Pursula et. al., [25], states that the assignment isperformed in two parts, first computing the optimal strategy to reach the destinationfrom each origin and second is to assign the demand according to the strategy.

The different options for how the passengers can reach the destination are saved in aset of strategies. A strategy can be explained as rules that allow the traveller to makefeasible decisions and reach the destination node. An example of a strategy, accordingto the Emme manual [1]: At node 1, take the line that arrives first of the attractivelines 1 and 2. If line 1 was taken, alight at node 2. If line 2 was taken alight at node4. At node 2, take the line that arrives first of the attractive lines 3 and 4. If line 3was taken, alight at node 4. If line 4 was taken, alight at node 3. At node 3, take theattractive line 5 and alight at node 4.

17

3 SOFTWARE PRODUCTS

The more information the traveller has the more complex the strategies become. Thetraveller knows the distribution of inter-arrival times for the transit lines for a specificnode and the travel time between nodes. The traveller receives the information whenreaching the node and the distribution of passengers is also known. Together it ispossible to calculate the combined accepted waiting time for arrival of the first vehiclein the set of transit lines passing the node and the probability of each line to arrivefirst. The chosen routes will depend on what transit lines arrives first at the nodes.According to Nilsson, [24], the travellers wait time at a node depends on the combinedfrequency of the attractive lines that serves the node, see equation (3). This part isreversely calculated, starting in a destination node and continuing backwards to allaffected origin nodes. A transportation network G consists of a set of nodes and a setof links, G = (N,A). A trip is defined by a sequence of nodes, n N , via links a A.A link a is assigned a link travel time ca and a distribution of the waiting time. Theresult is the optimal strategy Anr (a sequence of links) with expected total travel timeTTT nr from each node n N to destination r.

The first part of the algorithm initializes the expected travel time to reach r, TTTnr,to infinity for all nodes except for the destination node passengers TTT which is setto zero. The frequency variable, i, for all i I

, contains the combined frequencies ofthe attractive lines and is initialized to zero. The set S is used to identify links thathave not yet been examined, and it is initialized with all the links in A. The set A isinitialized to an empty set and is used to identify the optimal strategy.

The second part of the algorithm starts with checking if the set S contains any non-examined links, if it is empty then the algorithms first part will be stopped. If S isnot empty the link a closest to the destination r is selected. The time TTT

nr + ca isconsidered to be the time from node n to the destination r without including waitingtime at node n. If this time is smaller compared to the previous time at n, TTTnr thelink a is included in the optimal strategy and both i and TTTnr are updated to thenew combined total travel time of the attractive transit lines. It is important to knowhow TTTnr changes. The first time it will be iTTTnr = 0 (which is not defined),in order to make the algorithm more compact the convention 0 = is assumed,where is the waiting time factor.

To obtain the probability that a line i will be boarded is, according to [24] by Nilsson,defined as the ratio between the line frequency and the combined frequency of theattractive lines:

i =i

jI j(5)

The line headway is used in Emme when the optimal strategy is computed for passen-gers. According to Matti Pursula et. al [25] the headway can be the actual headwayor the perceived headway of a specific transit line or segment (user-determined). Theheadway in Emme is used to define the waiting times but also used for dividing thepassengers on attractive transit lines.

In the second part of the algorithm, the demand from node i to the destination r, gir,is assigned according to the optimal strategy A. The proportion of the demand of the

18

3.3 Emme 4

node i at the links a A corresponds to its frequency, see equation (5). The volumescan be updated simultaneously because the links are evaluated in reverse topologicalorder (decreasing TTT

i + ca) and therefore it is possible to examine every link onlyones.

The attractiveness test can be described by inequality (6) below and states that line i(second choice, the line with next shortest travel time compared to the first choice) isattractive if:

TTTfirst choices > TTT

i,second choice (6)

This means that the travel time for line i is lower than the total expected journeytime for the first choice. Therefore it will be better to board line i if it would arriveat the stop now than to wait for the first choice line. In order to calculate the finalexpected total journey time for all the combined attractive lines the following equationis formulated.

TTT i =

jIjTTT

j + twtwwt (7)

3.3 Emme 4

If Trafikverket will choose to upgrade their Emme version to Emme 4 there will be somenew functions available and more settings that can be used when calibrating models.The most relevant assignment procedure for larger cities is, according to the Emme 4manual [26], the extended transit assignment. Therefore this will be more thoroughlydescribed than the other assignments in Emme 4 and all facts are based on the promptmanual, [26], and the scientific report by Cepeda, Cominetti and Florian, [27], whichdescribes some of the new features in Emme 4.

Extended transit assignmentThis assignment is based on the standard transit assignment and the theory of optimalstrategy but in this extended version it is possible to model a connector choice. In otherwords the travellers can be divided among more connectors instead of only choosingthe shortest connector. Also the choice of route is more sensitive to travel times (inaddition to the headway), so lines with lower frequencies and shorter travel times stillcan be an attractive option.

In the extended transit assignment there are still the parameters used in the standardtransit assignment and some extra optional parameters such as boarding, in-vehicle andauxiliary transit cost. The boarding cost is a penalty associated with every boardingthat is done (both initial and transfer). The in-vehicle cost will be multiplied withthe in-vehicle time weight and can be constant, segment, link, node or transit linespecified. The cost will be added to the total travel time. The auxiliary transit costcan be constant, node or link specified and is multiplied with the weight and will beadded to the total auxiliary time in the TTT-equation.

19

3 SOFTWARE PRODUCTS

There is an optimal choice at the origin where a logit distribution can be used to splitthe flows on different connectors. If the logit distribution is not used all travellers willleave their origin node through the connector that have least impact on the travel time.The logit distribution is specified at choice points and can be defined for all origins orthe ones with a special attribute. At the choice points the distribution may be appliedon all connectors or just the efficient ones, which means the connectors that brings thetraveller closest to their destination node. When using the logit distribution for nodeswith specified attributes it makes it possible to use different choice sets at differentorigins (1 indicates to use logit on all connectors and 1 to apply only to the efficientones).

The logit function in the program contains two parameters, scale and truncation. Thescale is used in the computation of the likelihoods which the proportion are based on.The scale parameter has to be 0 or greater. If it is 0 the proportions for all the con-nectors in the choice set is the same. Larger values will give the best connector higherproportions. The truncation parameter is used to drop connectors that have propor-tions that are considered being too small. The connectors with smaller proportionsthan the given truncation parameter are not included until the remaining connectorshave proportions larger than the parameter value.

The proportions computed for the connectors can be changed to fixed proportions usinga user-defined link attribute. The proportions must be between 0 and 1 and the sumof for all the connectors from an origin must be 1. This can only be done on a subsetof origins and for the rest of the origin the link attribute must be 1.

In the extended transit assignment there is also possible to have a logit distribution atthe regular nodes with auxiliary transit choices. When using the logit assignment thetravellers at a node considering:

Wait at the node for a vehicle of an attractive line

Leave the node by the best auxiliary transit link or any efficient auxiliary transitlink

All travellers in an assignment without logit wait for a vehicle or leave the node by anauxiliary transit link. When using the logit assignment it is also possible to split thetravellers between stay on board and alighting. The travellers that alight must leaveby an auxiliary transit. So there will only be a split if:

The line on which the travellers are travelling on is also attractive at the node

It is possible to leave the node by auxiliary transits

The proportions of the alighting or the once who stays on board are computed basedon the impedance to the destination. The same logit function parameters are used asin the function for original nodes.

In the standard transit assignment the flow distribution is based on the frequency butin the extended assignment there is a choice to use a distribution based on frequencyand transit time to the destination. This means that fast lines with lower frequencyare more attractive, which results in smoother flow changes. This option can be chosenin the whole network or for certain nodes.

20

3.4 Visum 13

It is also possible to prohibit connector to connector paths, which means that the trav-ellers can not travel via only connectors to reach their destination. This suits networkswith a large amount of zones or a dense population.

Congested transit assignmentThis is an equilibrium assignment which includes an aboard congestion model based onvolume dependent cost functions. The link cost depends on the transit volumes (num-ber of travellers using the public transport network) that will represent the vehicleslowing down due to the number of passengers and the discomfort for the passengersthat will increase when the vehicle gets crowded. The congestion aboard a transitvehicle is modelled by adding functions to the transit segments which will transformthe crowding effect to delay at the segment. This leads to a non-linear model that issolved by the Wardrops user optimal principle, a transit equilibrium model.

Capacitated transit assignmentThis assignment uses a method to obtain transit flows that corresponds to the fact thattransit segments become congested. This is an equilibrium transit assignment that con-sider in-vehicle congestion functions (same as in the congested transit assignment) andthe increased waiting times at stops that depends on the transit lines capacities. Inevery iteration of the equilibrium process an extended transit assignment is performedand the goal is to divide the flow so that the total travel time is the same for everypassenger.

Stochastic transit assignmentAn average of a number of strategy-based assignments is computed in the stochastictransit assignments. The travel time of the segments, the perceived headways and/orthe perception factors are varied by different distribution functions (uniform, normal orgumbel). The volumes for the transit segments are obtained by computing the averageof the separate transit assignments which each are based on a different set of randomfactors.

Deterministic transit assignmentThis is a timetable-based assignment which includes information of the departure andarrival into the optimal path. The travellers know what time they want to leavethe origin or arrive at the destination. The path with the lowest cost will be used.This assignment is good to use when the headways are different throughout the studyperiod.

3.4 Visum 13

Visum is a software product that has the same field of application as Emme and isoften used by traffic analysts in Sweden and around the world. The software devel-opers of Visum are PTV Group in Karlsruhe, Germany. The company currently has600 employees that works on improving the different software packages. The Visummanual [2] states that the latest edition of Visum is version 13 which includes features

21

3 SOFTWARE PRODUCTS

such as trip distribution of the four step model, line cost calculations, fare calculationsand timetable-based assignment for public transport, and a traffic safety module thatcontains historical data of accidents etc. PTV also develops microscopic and meso-scopic traffic simulation software products. Today the company is located in America,Latin America, Asia Pacific, Austria, Benelux (Belgium, Netherlands and Luxemburg),China, France, Italy, the Middle East, and UK. PTV Group was founded by Dr.-Ing.Hans Hubschneider at the Karlsruher University in Germany. However, all informationconcerning the assignment procedures are presented in the manual, [2], and thereforethis will be the main source regarding Visum.

3.4.1 Public transport assignment

When performing a public transport assignment there are three different approaches;transport system-, headway- and timetable-based. As previously mentioned the headway-based assignment, that will be studied in this thesis, depends on the frequency of eachtransit line, i.e. how often the line departures.

There are many output matrices available in the headway-based assignment procedurein Visum. They are calculated in result matrices and the following outputs can beobtained; Journey time, in-vehicle time, origin wait time, transfer wait time, walk time,access time, egress time, perceived journey time, and number of transfers etc. Apartfrom result matrices there is also a list of public transport assignment assignmentstatistics containing information about the mean and total values of the previouslymentioned time components. Another output analysis tool that can be used is to viewdifferent lists or graphic link and connector bars. In the lists and bars there are anumber of output choices including transit volumes on specific links, modes or transitlines.

Table 2: Assignment variables that are generated from the simulation

Time components/Penalties Comments

In-vehicle time Time spent in a vehiclePuT-Aux ride time Time spent in an auxiliary mode

Access time Time spent on the connector from an originEgress time Time spent on the connector to a destination

Transfer walk time Time spent on walking in the networkbesides from access and egress time

Origin wait time Result from the headway of that specific lineTransfer wait time Result from the headway of that specific lineNumber of transfers An extra time penalty for making a transferBoarding penalty PuT A time penalty added for all or some boardings (transfers included)

Boarding penalty PuT-Aux A time penalty added for using all or some auxiliary modesMean delay (penalty) A time penalty added for all or some passengers

Perceived journey time All passengers wants to minimize this time, which consistsof all variables mentioned above together with the weights

and factors stated previously in this chapterImpedance The objective for PuT-assignment, minimize weighted PJT

The perceived journey time (s) is calculated in the Visum headway-based assignmentin order to minimize the expected travel time for all demand. The function s consists ofseveral time variables, weights and penalties that affects the generalized journey timein minutes. The different time components and factors are displayed in Table 2. In theassignment settings the factors are multiplied with the corresponding time componentsand all penalties are set to a user specified value. Some of the time components have

22

3.4 Visum 13

both factors and attributes, which are both multiplied with the time variable. Regard-ing the Origin wait time (OWT) there is a formula that includes several attributes,which can be multiplied or added to the OWT.

Impedance is a measure of how much the perceived journey time (s) is weighted, whichresults in a value (in minutes) of how unwilling the travellers are to travel with aspecific line or mode. In Visum the impedance is calculated by multiplying a userspecified factor with s (the total expected travel time), see equation (8). The totalexpected travel time is calculated with the combined parameters and time componentsthat corresponds to the Emme parameters.

s = wauxtaux +wwttwt +wboardingtboarding + ttravel +wtransferNTR (8)

These equations are used in the assignment procedure and all factors, penalties andattributes can be determined by the user. All time components, stated in Table 2,are calculated automatically and cannot be changed manually. However, it is possibleto control the passenger arrival rate so they do not arrive randomly at the stop area.By adjusting the origin wait time factor one can obtain a transport system where thepassengers have more information about the timetable, which can result in a morerealistic assignment in a system with longer headways.

23

3 SOFTWARE PRODUCTS

3.4.2 Algorithm

The notations mentioned in this section are summarized in Table 3 below.

Table 3: Line specific notation description for the algorithm section in Visum

Notation Explanation of notation

twt Wait timewwt Wait time weight and factortaux Auxiliary timewaux Auxiliary time weight

tboarding Boarding timewboarding Boarding time weightttravel Travel time

wtransfer Transfer weightNTR Number of transferss Total expected travel time, equation (8)i Line indexI All lines in the networkI All optimal lines, i.e. lines selected

according to the headway-based algorithm Line frequencyh Line headwaypi Combined line probability

(share of the total demand), equation (5)u Constraining factor, the combined wait and travel time

for all optimal lines, equation (9)

s

s (PJT) without wait time (twtwwt), equation (11)

c

Total expected travel time of the optimal lines, equation (13)

For the headway-based assignment procedure there are three steps performed. In orderto calculate which route or routes will be the most attractive for the travellers thereare five types of passenger information systems that the user can apply to the model,stated under choice models in the list below.

Calculating the headway

From a user-determined time profile attribute, which is used in this study(due to that the headways already have been calculated in the model ob-tained from Trafikverket)

From the mean headway stated in the timetable

From the mean wait time stated in the timetable, this is the default setting

Route search and route choice

Paths are evaluated with respect to their impedance (generalized cost)

Choice models (based on the logit model):

24

3.4 Visum 13

Passengers have no information and the transit lines have exponen-tially distributed headways (similar to the algorithm in Emme, optimalstrategies)

Passengers have no information and constant headways

Passengers have information on the elapsed wait time

Passengers have information on the next coming departure times of thelines from the stop they are currently waiting at

Passengers have complete information

Route loading

The set of lines that are assigned with some share of the demand are called the optimalset. All lines i have a remaining journey time, denoted si, that consists of the remainingin-vehicle time, access time, origin wait time and various penalties and/or weights. Thewait time is calculated in different ways depending on the type of passenger information.

No information and exponentially distributed headways (similar to the al-gorithm in Emme, optimal strategies)In the first two cases, with no information, a constraining equation is used to determineif the routes have potential to be in the optimal set or not. If the remaining travel timeis less or equal to the constraining factor (uk1, the combined wait time and traveltime of the lines from 1 to k1) then the route could be in the optimal set, otherwisethe demand share of that route is zero. Note that the set of remaining travel timesare listed in descending order and that k stands for the ranking index in this listedset.

uk =1+k

l=1lslkl=1l

(9)

For the route to be in the optimal set the remaining journey time of route with index kneeds to be less or equal to the constraining factor of the previous route in the sortedset, see equation (10).

sk uk1 (10)

If the route to be calculated is listed first there are no other routes to compare with andtherefore this route is directly set to be in the optimal set. However, the other optimalroutes can still turn out to be non-optimal in a later stage of the assignment procedure.The routes are sorted according to the remaining journey time, in descending order,and are used in the following assignment computation stage.

The next step is to filter out the unattractive routes based on a comparison betweenthe examined ranking index k and the previous routes with index 1 to k 1. Thismakes sure that all attractive lines have an expected remaining journey time that is

25

3 SOFTWARE PRODUCTS

lower than the combined remaining journey time for the other attractive lines. Theexpected remaining journey time for index k is referred to as s

k and is stated inequation (11).

s

k = wauxtaux +wboardingtboarding + ttravel +wtransferNTR (11)

The share of each attractive line is based on the headways of all optimal routes andthe equation describes the probability that the passengers will board this specific lineroute first. The line probability is therefore dependent of how frequently the transitline departures and can be seen in equation (12).

i =i

jI j(12)

The combined routes for ranking index 1 to k 1 will have an expected remainingjourney time that consists of the combined wait time (equation (3)) with the waitfactor wwt and the combined travel time. The equation for combined wait time andremaining journey time, c

k, are calculated according to equation (13).

c

k =1

k1l=1 l

wwt +k1

l=1

ls

l (13)

Since the set of optimal routes are listed in descending order the first route will be thebest with respect to the total remaining journey time and is therefore always a partof the final optimal set. To decide whether or not the other routes actually belongs tothe optimal set (I) the constraint in equation (14) needs to be fulfilled.

s

k c

k (14)

All the lines in the optimal set have a share of the assigned demand i, see equation (12),where i is one of the optimal lines.

This type of choice model suits situations where the headways are irregular and thereis no passenger information that can lower the uncertainty.

No information and constant headwaysThis information type is basically the same as the previously mentioned type, a pas-senger boards the line which arrives first an