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Department of Science and Technology Institutionen för teknik och naturvetenskap Linköping University Linköpings universitet

gnipökrroN 47 106 nedewS ,gnipökrroN 47 106-ES

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

A comparative study betweenEmme and Visum with respect to

public transport assignmentCisilia Hildebrand

Stina Hörtin

2014-10-10

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

A comparative study betweenEmme and Visum with respect to

public transport assignmentExamensarbete utfört i Transportsystem

vid Tekniska högskolan vidLinköpings universitet

Cisilia HildebrandStina Hörtin

Handledare Ellen GrumertExaminator Anders Peterson

Norrköping 2014-10-10

Upphovsrätt

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© Cisilia Hildebrand, Stina Hörtin

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 SystemsLinköpings University

November 2014

Cisilia Hildebrand

Stina Hörtin

Institute of Technology, Dept. of Science and Technology,

SE-581 83 Linköping, Sweden

Preface

The work presented in this thesis has been carried out in the Division of Communications-and Transport Systems at Linköpings University and at WSP Analysis & Strategy.First of all we want to thank our supervisor Ellen Grumert and examiner Anders Pe-terson at Linköpings 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 Hörtin

Norrköping, 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 Kungsträdgårdenhave 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

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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

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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

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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 Kungsträdgården 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

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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 Trafikförvaltning 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

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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 (Trafikförvaltningen) 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, Trafikförvaltningen, 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 Kungsträdgården. 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 Trafikförvaltningen (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-JürgenDon from Visum’s 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], Hydénet.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


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 Hägerwall 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, xi

represent 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


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 Johansson’s report [5], Larsen’sreport [23] and Hägerwall Stein’s 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 Trafikförvaltningen.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 Trafikförvaltningenuses 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 Johansson’s report [5], Larsen’s [23], and Hägerwall Stein’s 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 läns 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 : 00−09 : 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 Hägerwall 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 Montréal 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 0−999.999, 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 0 − 999.999 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 (λi) for the attractive lines (inthe optimal set of lines i ∈ I∗), see equation (3) obtained from Nilsson’s educationalmaterial, [24]:

twt =1

∑i∈I∗ λi

(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 weight

ttravel 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 networkλ Line frequencyh Line headwayπ 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, λi, 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 λi 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 λiTTTnr = ”0·∞” (which is not defined),in order to make the algorithm more compact the convention ”0 · ∞” = τ is assumed,where τ 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:

πi =λi∑

j∈I∗ λj(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

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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 =∑

j∈I∗

πjTTT′

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.

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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.

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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 Wardrop’s 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

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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 line

Number 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.

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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 weight

ttravel Travel timewtransfer Transfer weight

NTR 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 algorithmλ Line frequencyh Line headwayπ 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):

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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 (uk−1, the combined wait time and traveltime of the lines from 1 to k −1) 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+

∑kl=1 λlsl

∑kl=1 λl

(9)

For the route to be in the optimal set the remaining journey time of route with index k

needs to be less or equal to the constraining factor of the previous route in the sortedset, see equation (10).

sk ≤ uk−1 (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

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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).

πi =λi∑

j∈I∗ λj(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

∑k−1l=1 λl

wwt +k−1∑

l=1

πls′

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 πi, 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 and is one of the optimal strategies. However,the procedure is slightly different due to the fact that all headways are constant. Thisinformation variant originates from VIPS algorithm RDT (random departure time)and was then developed further in Visum. First of all there can be several minimumremaining journey times that should be included in the optimal set. When calculat-ing arg min

k

sk for all lines k the result leads to values of k, which have the smallest

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3.4 Visum 13

remaining journey time sk. The demand share is also calculated differently comparedto the previous information type, although it is still based on the probability that linei departs first, for all i in the optimal set of lines.

The choice model with no information and constant headways are suitable in thosecases where the headways are fairly regular and the passengers have no informationabout the transit lines actual arrival times.

Information on the elapsed wait timeThis choice model assumes that all passengers know how long they have been waitingat the stop area. With that information they can improve their estimation of theremaining journey time. By ignoring the lines that have longer travel times thanthe line that will arrive next, according to how long the passenger already have beenwaiting, one can lower the total remaining journey time compared to the two previouschoice models. The assumption made in this model type does not require investmentsin the infrastructure since all passengers can be said to have access to a watch, or atleast their perception of the time elapsed. However, this method is more calculationintense due to that the optimal set now will vary depending on the elapsed wait time.Given that the expected waiting time is larger than the elapsed waiting time one canexpress the remaining journey time as s mentioned in the first information type withthe amendment of the wait time term. Previously this was calculated as wwttwt andwith consideration to the elapsed time (te) this term is adjusted to wwt(twt − te).

In order to calculate whether a route is worth taking, even though the travel timeis longer than another line of the optimal set, tj needs to be computed. When thepassenger has waited tj time units or more, the remaining travel time for line j islonger than the expected remaining wait time including the remaining travel time forline j −1. Therefore line j is excluded from the optimal set of lines at the exact time tj .

Information on departure timesThe choice model with known departure times all passengers receive information aboutline arrivals at the transit stop. The optimal lines are chosen by minimizing the re-maining travel times with respect to the exact departure times. To decide whether aline is in the optimal set it needs to satisfy the inequality sk < minl sl +hl. That is, thelowest possible line number (since the list of lines is sorted in descending order) musthave a higher remaining travel time than the evaluated line k.

Complete informationThe passengers have information regarding the departure times at all stops beforedeciding which route to choose. This choice model suits transport systems whereall passengers have full access to departure times and always choose the route thatminimizes the total travel time.

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3.5 Assignment parameters and examples

This section will describe the parameter setting needed for the public transport assign-ments in Emme and Visum. The assignment algorithms will also be illustrated in asmall example performed in each software.

3.5.1 Assignment parameter settings in Emme and Visum

When performing any type of assignment there are user-determined values for adjustingthe model results. These are referred to as assignment parameters and can differbetween software products. The parameters mentioned below are used in the publictransport assignment in Emme:

• Boarding time (min)

• Wait time factor

• Weight factors:

– wait (origin and transfer wait time)

– auxiliary (walk time at the connectors)

– boarding

• Spread factor

With Visum there are several settings that can be varied and these are stated in thefollowing list:

• Boarding penalty (min), PuT (public transport)

• Boarding penalty (min), PuT-Aux (public transport auxiliary modes)

• Mean delay (min)

• Formula/attribute for origin/transit wait time

• Weight factors:

– origin and transit wait (OWT and TWT)

– access, egress and walk (ACT, EGT and WT)

– transfer (NTR, in minutes)

– in-vehicle (IVT)

– PuT-Aux ride time

– Fares

With the help of both software manuals the assignment parameters could easily bedetermined according to the translation in Table 4. The following can be found in theEmme [1] and Visum [2] manual and have been interpreted by the authors:

28


• boarding time and boarding penalty PuT both implies an addition to the per-ceived travel time

• wait time factor in Emme is described to be represented in Visum by an attributefor the origin and transfer wait time

• wait time weight in Emme corresponds to the factors for origin and transfer waittime in Visum

• aux time weight in Emme corresponds to the factors for access, egress and walktime in Visum

• boarding time weight in Emme do not exist explicitly in Visum, it can howeverbe added to the boarding penalty PuT if needed

• spread factor in Emme do not exist explicitly in Visum, it can however be mul-tiplied to the origin and transfer wait time if needed

• factor for NTR, number of transfers, do not exist in Emme

• fares for public transport lines in Visum (and cost penalty in Emme 4), do notexist in Emme 3

Table 4: Parameter translation

Parameter Emme parameter Visum factor or attribute

wboarding Boarding time Boarding penalty PuTwwt Wait time factor Formula for OWT, Attribute for time profiles (transit lines)

Transit stop area attribute, TWTWait time weight OWT and TWT factor

waux Aux time weight ACT, EGT and WT factorwboarding Boarding time weight Factor for boarding penalty PuT

(gives the same effect as adjusting only boarding penalty)Spread factor 1 -

Factor for NTR - wtransfer

Boarding time (Emme) and boarding penalty PuT (Visum) implies the same effecton the network, for each boarding (transfers included) the passengers are punishedwith a time penalty. This is done so that the impedance (resistance) increases forboarding transit lines and the passengers try to minimize their number of transfers.This parameter will be referred to as tboarding and is complemented with a weight ofhow the boarding time is perceived, wboarding.

When a passenger waits for a transit line there are both a wait time factor and await factor weight in the Emme assignment. The wait time factor is used in orderto obtain arrival distributions and the wait factor weight is used to compare howmuch impedance that should be applied to the wait time compared to the in-vehicletime (ttravel). In Visum the corresponding time components are origin and transferwait time, each with a separate wait time factor and wait factor weight. These timecomponents together form the notation twt with the weight factor wwt. According tothe model obtained from Trafikverket and Emme manual the wait time factor is setto 0.5, which corresponds to a uniform distribution of the passenger arrival times anda regularly spaced service. Lower values can be used when for example the headwaysare long and passengers know the timetables.

To obtain a spread of travellers on different transit lines Emme uses a parameter calledspread factor. Spread factor can in some situations be used in addition to the weights

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by increasing or decreasing the attractive trip options for the travellers. The factoris multiplied with the product of the waiting time and waiting time weight, and isincluded in wwt. There is a corresponding formula in Visum that achieves a variance inthe waiting times if desired, which also leads to a various amount of attractive transitlines. A lower spread factor means less attractive transit lines. According to the modelfrom Trafikverket the spread factor is set to the default value 1, which is the defaultvalue.

The time that passengers spend on connector links (from origin or destination to thenetwork) and between transfers is called auxiliary time in Emme. In Visum it is dividedinto access (from origin), egress (from destination) and transfer walk time and togetherthey correspond to the auxiliary time in Emme. This variable will be mentioned astaux and its associated weight factor is waux. The weight factor is a measurement ofhow much the passengers wants to avoid walking compared to travelling with a transitvehicle (in-vehicle time).

The headway (h) and frequency (λ) variables are both used in Emme and Visum, wherethe frequency of a line j is inversely proportional to the headway, λj = 1

hj.

3.5.2 Example with a small network, Emme

To exemplify how the algorithm for optimal strategies work a small network will beused and the results calculated both by hand and in Emme. In Table 5 the used valuesare stated and the network can be seen in Figure 2.

Table 5: Characteristics of the small network in Emme

Line number/Link Line length (km) Speed (km/h) Line/Walk time (min) Headway

1 16.66 50 20 102 5 50 6 53 4.17 50 5 5

Zone 4 to node 1 - 5 1.2 -Zone 4 to node 2 - 5 12 -Node 3 to zone 5 - 5 3 -

Figure 2: The small network in Emme (not to scale)

The red nodes are zones from which the trips are generated. In this example zonenumber 4 is the origin and zone number 5 is the destination for the 100 trips assigned.There are three different public transport lines, line 1 goes from node 1 to node 3directly, line 2 goes from node 1 to node 2 and line 3 goes from node 2 to node 3. The

30


headways are set to 10, 5 and 5 which means that line 1 departs with a 10 minuteinterval and line 2+3 departs every 5th minute.

The parameters used in Emme originates from Trafikverket’s assignment settings forpublic transport. These values are well established and the most commonly used valuesare presented in the list below. No scientific basis or any type of documentation forchoosing these specific values have been found. Since the thesis does not focus oncalibration of assignment parameters the following values will be used without furtherconsideration.

• Boarding time: 5.00

• Wait time factor: 0.50

• Weight factors:

– wait = 1.50

– auxiliary = 2.00

– boarding = 1.00

• Spread factor: 1.00

This leads to TTT = 1 · ttravel + 2 · taux + 1.5 · 0.5 · twt + 1 · tboarding. In order to exem-plify the algorithm described in the previous section the optimal strategies have beencalculated by hand. The computations are reversed, i.e. it starts at the destinationnode and moves backwards to the origin node. The network characteristics are statedin Table 6.

Table 6: Weighted times for each transit line

Node Line In-vehicle time Walk time Wait time (equation(3)) Boarding time

3 - - 6 - -2 3 5 6 3.75 51 1 Line 1: 20 Line 1: 6 Line 1: 7.5 Line 1: 5

2 Line 2: 11 Line 2: 6 Line 2: 3.75 Line 2: 5Zone 4 - - To node 1: 2.4 At node 1: 2.5 -

To node 2: 24 At node 2: 3.75

There are no lines departing at node 3 and therefore it does not require any computa-tions, apart from the travel time (walk time) with the walk time weight.

Node 2 have only one line, i.e. line 3, with the travel time TTT′

i = 16 (see step 1 inTable 7) and expected travel time 19.75 minutes (step 2 in Table 7). Since only this linedeparts from node 2 that line is considered to be attractive (step 3 in Table 7).

At node 1 there are two lines available, line 1 and line 2, where the second line requiresa transfer. The expected travel time for the transfer line, 19.75 minutes, will be addedto the travel time for line 2. This is done in order to attain the total travel time fromnode 1 if line 2 arrives now, i.e. without wait time at node 1, 30.75 minutes (step 1).The first line is a direct line and therefore its total travel time only consists of thein-vehicle and boarding time, 31 minutes (step 1). The line with the shortest traveltime will be the first choice if both lines arrive at the same time. This leads to that thefirst choice is line 2 and after calculations according to the total travel time function(with combined wait time) the expected travel time is determined as 34.5 minutes, see

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equation (7) and step 2 in Table 7. If line 1 arrives directly it will take 31 minutesto arrive at destination, which is still attractive compared to 34.5 minutes in expectedtravel time if the passenger waits for line 2 instead of boarding line 1. This can beobserved from the attractiveness test in equation (6) and step 3 in Table 7 where the

line i in this case is the first choice (shortest TTT′

i among the lines departing fromthat node).

Table 7 and 8 shows the complete results when following the reversed assignmentalgorithm approach of the optimal strategies in Emme (i.e. begins with destinationand moves backwards to the origins).

Table 7: Algorithm first three steps for computing the optimal strategies in Emme

Node Step 1: Travel time (T T T′

i) Step 2: Expected travel time Step 3: Attractiveness test

without wait time (equation (4), first choice) (equation (6))

3 6 - -2 16 19.75 Only line 3 is attractive1 Line 1: 31 (Line 1: 38.5) 34.5>31,

Line 2: 30.75 (first choice) Line 2: 34.5 both lines are attractiveZone 4 Node 1: 2.4 (first choice) Node 1: 4.9 4.9<24, only the link from

Node 2: 24 (Node 2: 26.75) zone 4 to node 1 is attractive

Table 8: Algorithm last two steps for computing the optimal strategies in Emme

Node Step 4: Probabilities for attractive lines Step 5: Expected travel time(equation (5)) T T T

∗

i(equation (7))

3 - -2 π3=1 19.751 π1=0.33 Line 1 and 2: 33.33

π2=0.67Zone 4 π4=1 Line 1 and 2 via node 1: 35.73

This leads to the conclusion that both line 1 and 2 are attractive lines (step 3), whichalso is the result from the assignment made in Emme (Figure 3). When calculating theline shares for the attractive lines the line frequencies are used as previously mentionedin the algorithm section (equation (5)) and step 4 in Table 8. The probability thatline 1 is chosen is 33% and 67% that the passengers choose line 2 instead. Step 5 inTable 8 is done in order to determine the total expected travel time from zone 4 tozone 5 with a combination of the attractive lines, which leads to 35.73 minutes. Thiswas also generated in the Emme assignment, in Figure 3.

Figure 3: Graphic result from transit assignment of the small network in Emme

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3.5.3 Example with a small network, Visum

The assignment procedure in Visum with no passenger information and where thetransit lines have exponentially distributed headways, similar to the optimal strategies,will be exemplified in this section. Since the headways are predetermined the headwaycalculation is not described in this example. The used values are stated in Table 9 andthe network can be seen in Figure 4.

Table 9: Characteristics of the small network in Visum

Line number/Link Line length (km) Speed (km/h) Line/Walk time (min) Headway

1 16.66 50 20 102 5 50 6 53 4.17 50 5 5

Zone 1 to node 1 - 5 1.2 -Zone 1 to node 2 - 5 1.2 -

Figure 4: The small network in Visum (not to scale)

The blue nodes are zones from which the trips are generated. In this example the firstzone is origin and the second zone is destination for the 100 trips assigned. There arethree different transit lines, line 1 goes from node 1 to node 3 directly, line 2 goes fromnode 1 to node 2 and line 3 goes from node 2 to node 3. The headways are set to 10, 5and 5 which means that line 1 departs with a 10 minute interval and line 2+3 departsevery 5th minute. The line times are assigned by adding run times in the time profileedit dialogue for each line.

The parameters used in Emme have been somewhat translated through experimentsand theory from the software manuals, see Table 10. Equation (15) shows the formulafor s with these translated parameter values.

s = 1 · ttravel +2 · taux +1.5 ·0.5 · twt +1 · tboarding +0 ·NTR (15)

Table 10: Translation of assignment parameters from Emme to Visum

Emme Visum Value

Boarding time (min) Boarding penalty PuT (min) 5Wait time factor Formula for OWT, time profile and stop area attribute 0.5

Attribute for TWT, stop area attributeWait time weight OWT and TWT factor 1.5Aux time weight ACT, EGT and WT factor 2

Boarding time weight Factor for boarding penalty PuT 1(gives the same effect as adjusting only boarding penalty)

Spread factor - 1- Factor for NTR (min) 0

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3 SOFTWARE PRODUCTS

To exemplify the algorithm described in the previous section the first information levelhas been calculated by hand. The calculation results were equal to the assignmentdistribution of the demand in Visum. The used network data with weights can be seenin Table 11 and 12.

Table 11: In-vehicle, walk and origin wait times for the different routes

Route Line In-vehicle time Weighted Un-weightedwalk time origin wait time

1 1 20 2.4 102 2+3 11 2.4 53 3 5 24 5

Table 12: Boarding, transfer and total wait times for the different routes

Route Line Un-weighted Total weighted tboardingwboarding

transfer wait time wait time +NT Rwtransfer

1 1 0 7.5 52 2+3 5 7.5 103 3 0 3.75 5

Step 1 is performed in order to calculate the remaining journey time from equation (15),where the routes are ranked from the lowest remaining journey time to the highest.From this ranking uk, equation (9), is calculated and if the route with ranking index k

fulfils the constraint sk ≤ uk−1 one can consider the route to be a part of the optimalset, see Table 13 for the calculation results of the first step.

Table 13: First step when computing the optimal strategies in Visum

Route Lines

(equation (15))Ranking Index k

uk(equation (9))

In the optimal set of lines?(equation (10))

1 1 34.90 2 35.57 34.90<35.90 yes2 2+3 30.90 1 35.90 yes3 3 37.75 3 36.44 37.75>35.57, no

This results in that route 1 and 2 are considered to be a part of the optimal set of lines,although more tests are required to determine whether the routes truly are attractivecompared to each other. This will be done in step 2 below.

There will always be at least one attractive route, and that is the route with rankingindex 1 (with the lowest sk). The other routes (in this case only route 2) needs to becompared to those routes that have less sk time, i.e. all routes in the optimal set thathave ranking index from 1 to k − 1. When comparing ranking index k to the otherroutes one will need:

• Line probability (combined frequency if there are transfers involved, equation (5))πl, i ∈ I∗ : i < k −1

• Combined remaining wait time (first part of equation (13)) 1∑k−1

l=1λl

wwt

• Combined remaining travel time (second part of equation (13))∑k−1

l=1 πls′

l

When the above mentioned equations are calculated for each route the combined traveland wait times for all routes in the optimal set (ranking index from 1 to k − 1) is

34


obtained, equation (13). To decide if the route is attractive the attractiveness test

mentioned in equation (14) needs to be fulfilled, i.e. s′

k ≤ c′

k. See Table 14 for thecalculation results.

Table 14: Second step when computing the optimal strategies in Visum

Ranking Route Line The expected Comb. Line Comb. Comb. wait Attractiveness Shareindex k remaining journey remaining probability remaining time and test

time for index k, wait time journey remainingwithout wait time time journey time

1 2 1 23.4 - - - - Optimal 0.672 1 2+3 27.4 7.5 π1 = 1 23.4 30.9 27.4 < 30.9 ⇒ 0.33

Optimal

In this specific example the optimal routes are the first and second, with ranking index2 and 1. The graphic result can be seen in Figure 5. Compared to the results fromcorresponding Emme example this leads to the exact same optimal route distributionand line shares.

Figure 5: Graphic result from transit assignment of the small network in Visum

3.5.4 Main differences between the algorithms

The difference between the software assignments is how the first and second attractive-ness test is performed. The criteria in Emme (step 1, equation (6)) is based on thatthe line with the shortest expected arrive time at destination will be the first choiceif it would arrive at the examined node directly (i.e. without wait time). In Visumthe total expected time including wait time is considered when limiting the number oflines that can be chosen from the optimal set (from equation (10)).

The constraining factor in Visum, uk−1, determines if a line is attractive in the firstattractiveness test. This factor is equal to twt plus the total expected journey time, s,for all lines that are in the attractive set. This factor is the combined wait and traveltime for all the optimal lines, i.e. the lines with shorter expected journey time thanline k. When using equation (10), the total expected journey time of route with indexk must be less or equal to the combined expected journey time mentioned above asuk−1, which considers all lines with ranking number 1 to k − 1. The factor states thelongest time one should have to wait for one of the attractive lines and in order for linek to be attractive its travel time must be lower than the longest time of all combinedoptimal lines. The same type of criteria is stated in Emme, however the total expectedjourney time does not include the wait time, TTT

′

, and therefore states the travel timeif the line arrives now. Also, this travel time is calculated for the "second choice" (the

35

3 SOFTWARE PRODUCTS

line with next shortest travel time at that stop). The travel time for the second choicemust be strictly less than the total expected remaining journey time of the first choice,i.e. the line with shortest travel time at that node. Both first attractiveness tests inEmme and Visum aims to separate the lines with too long remaining travel time.

In Visum there are another attractiveness test performed in order to double check if aline have too long travel time. Since the first test was focused on detecting lines withtoo long journey time (low line frequency) this test needs to examine the travel time ifa line arrives directly at a node. In contrast to Emme, which first of all tests whetheror not the lines have low remaining travel time and secondly tests if the other lineshave shorter travel times compared to the total journey time of the first choice line.This means that it would pay off to board another line rather than to wait for the linewith shortest travel time (takes the line frequency into account).

To summarize the differences between the two algorithms first and second attractivenesstests these examples are stated for each software:

• Emme, example of the first and second attractiveness test:

– There are two lines, 1 and 2, departing from node A

– The first line has an expected remaining travel time of 20 minutes if it wouldarrive at node A now and an expected remaining journey time of 27 minutes(i.e. including wait time)

– The second line has an expected remaining travel time of 25 minutes if itwould arrive at node A now and an expected remaining journey time of 26minutes (i.e. including wait time)

– When determining which line will be the first choice the algorithm in Emmecompares the travel time of line 1 and 2

– Line 1 will be the first choice since 20 < 25 (first attractiveness test inEmme)

– To examine if line 2 also is attractive (if it would arrive first) the totalexpected remaining journey time of line 1 will be compared to the expectedremaining travel time of line 2

– Line 2 will be the second choice since 25 < 27 (second attractiveness test inEmme)

• Visum, example of the first and second attractiveness test:

– There are two lines, 1 and 2, departing from node A

– The first line has an expected remaining travel time of 20 minutes if it wouldarrive at node A now and an expected remaining journey time of 27 minutes(i.e. including wait time)

– The second line has an expected remaining travel time of 25 minutes if itwould arrive at node A now and an expected remaining journey time of 26minutes (i.e. including wait time)

– When determining which lines that will be attractive all lines are ranked indescending order with respect to the expected remaining journey time

36


– Line 2 will be the first choice since 26 < 27 (first attractiveness test inVisum)

– To examine if line 1 also is attractive (if it would arrive first) the totalexpected remaining travel time of line 1 will be compared to the combinedexpected remaining journey time of the already attractive lines (in this caseonly line 2)

– Line 1 will be the second choice since 20 < 26 (second attractiveness test inVisum)

Regarding that Emme includes the wait time in the flow distribution and in the secondattractiveness test but not in the first attractiveness test, the headway influence onattractive lines are smaller than in Visum. In Emme 4, one can use a combined headwayand travel-time distribution so that more people will choose the line departing less oftenand have a short travel time (the travellers adapt more to the timetable). Where inVisum the wait time is included in the flow distribution and both attractiveness tests.This leads to that the headway affects the choice of attractive lines more than inEmme and therefore lines with longer headways are excluded. That problem can beavoided if one uses other types of information settings available in Visum. The issuewith taking the wait time into account several times can lead to that more peopleare choosing to change transit line in Visum as more routes are attractive which cancontain more transfers. An approach that can be used to prevent this is to apply atransfer penalty.

3.5.5 Comparison between literature examples

This section will describe two examples stated in the reports by Nilsson [24] and Larsen[23]. The example in [24] consists of a network in Emme and therefore a similar networkwas built in Visum in order to evaluate the algorithm differences further. In Larsen’sreport [23] there are two networks built in Emme and Visum, however the results didnot coincide with the calculations made by the authors of this thesis. Therefore areproduction of the networks was made.

According to the example in Nilsson’s report [24] 50 % will choose line red and 50% line green from the first node. From the last node before destination 8 % travelswith line blue and 42 % with line black. When calculating the results with the Visumalgorithm (and running the simulation) 50 % chose red and green. However, the blueline was in this case determined as unattractive. Instead the demand was spilt equallyon the black and grey line, i.e. 25 % of the total demand on respective line. Thereasons why the blue, black and grey line differs between the software assignments arethe attractiveness tests and that Emme calculates the expected remaining journey timecompared to Visum that calculates the remaining journey time for all routes accordingto the information available. Regarding the attractiveness tests they differ due tothat Emme will choose the line with the shortest travel time if the line arrives now("first choice") and Visum will choose the line with the shortest total time (includingwait time etc.). That is why the blue line, with shortest travel time and longestheadway, will be chosen in Emme and not in Visum. Since the results from followingthe stated assignment algorithm in Visum led to the same demand distribution when

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3 SOFTWARE PRODUCTS

simulating the network one can assume that the algorithm is correctly interpreted fromthe manual. The results from this reproduced example can be seen in Figure 6 (the linkcolors represents each transit line), Table 15 and Table 16. Note that the percentagein Figure 6 is the share of travellers from the previous node, not the share of the totaldemand.

Table 15: Attractiveness results from Emme

Node Attractiveness test result

A First choice: Green lineAlso attractive if they would arrive before the first choice: Red line

B First choice: Blue lineAlso attractive if they would arrive before the first choice: Green line

C First choice: Blue lineAlso attractive if they would arrive before the first choice: Black line

Table 16: Attractiveness results from Visum

Node Attractiveness test result

A First and second test:Green line, Red line

B First and second test:Green line

C First and second test:Black line, Grey line

Figure 6: Comparison between the reproduced example results in both Emme andVisum

When reproducing the test network in Chapter 5 from Larsen’s report [23] both thesimulation and calculation results were equal between Emme and Visum. However, theresults did not coincide with the Visum results in [23]. The reason why the results arenot the same depends on that the settings for headway-based assignment in Visum aredifferent. The simulation run by Larsen[23] used the RDT-based assignment ("randomdeparture time" from VIPS, or "no information and constant headways" in Visum),which uses constant headways instead of exponentially distributed headways, whilethe reproduced test network used exponentially distributed headways. The differencesstated by Larsen ([23]) are that the algorithms differ when deciding the attractive

38


routes, as this thesis also states in the previous section 3.5.4, and how the travellersare distributed on the attractive lines. The distribution however, does not coincidewith the reproduced results since the algorithm settings were different.

39

4A case study

A traffic network representing the Stockholm area is available from earlier studiesalong with transit lines and demand matrices. In order to obtain as similar models aspossible, the network will be built in Emme and then imported to Visum.

The large networks were obtained from Trafikverket and contain the transport systemof Sweden in a future scenario with and without the metro to Nacka, and represents thetraffic situation at 07 : 00 − 09 : 00 in the morning. In this thesis the traffic predictionwithout metro is called the base scenario and the traffic prediction that contains themetro route from Kungsträdgården to Nacka Forum is called the future scenario.

The existing network of Sweden that was obtained from Trafikverket will have to belimited in order for the software differences to be more traceable. When only studyinga smaller part of Sweden it will be easier to identify the differences and similaritiesbetween the transit assignment in Emme and Visum. To receive a more accuratebreakdown of public transport lines, the area includes the entire municipality of Nacka,large parts of Södermalm and the area towards Stockholm Central Station. The linesgoing into and out of the chosen area must be cut in order to function in the new area.A series of assembly nodes will need to be positioned in order to capture the flows withdestination outside the area. The chosen network area can be seen in Figure 7.

Figure 7: The network limitation area of Nacka municipality, from Google maps

41

4 A CASE STUDY

The assignment parameters for public transport are determined for the Emme modeland have been interpreted to the Visum model by studying the manuals and performingexperiments, see Chapter 3. In the Sections 4.1 and 4.2 there are more detailed descrip-tions of the used parameters and how the models where built and verified. Accordingto Florian and Spiess [22] urban environment and short headways in a public transportnetwork would be best suited for the headway-based assignment procedure when per-forming long term traffic predictions. Stockholm is an urban environment with a publictransport system which have short headways and therefore this assignment procedureis the most appropriate. Headway-based assignment is also used by Trafikverket andTrafikförvaltningen when modelling Stockholm’s public transport system.

In Figure 8 the chosen metro alternative, according to the pre-study [28], can be seenand consists of a connection to the existing blue line at Kungsträdgården. The reasonfor choosing this alternative is due to that Stockholm City and the municipalitiesinvolved decided that this was the most socio-economically efficient alternative. Thefollowing stations will be a part of the metro extension to Nacka:

• Kungsträdgården (connection with the existing blue line)

• Sofia

• Hammarby Kanal

• Sickla

• Saltsjö-Järla

• Nacka Forum (end station)

Figure 8: The chosen alternative for Nacka metro

42

All bus lines that will be of importance in the base scenario are listed in Table 17 withline numbers, start and destination station. The important lines are those that passthrough a large part of the study area or those considered to have an essential roll inthe public transport supply (lines with a lot of boardings). When limiting the networkthe existing public transport lines needs to be cut off and therefore several new originand destination nodes will be created.

Table 17: Relevant bus lines for the base scenario

Line number Line origin Line destination

43 Ruddammen Karlaplan (skansen)53 Roslagstull Henriksdalsberget55 Hjorthagen Sofia59 Karolinska sjukhuset Norra Hammarbyhamnen66 Reimersholme Sofia74 Mariatorget Sickla köpkvarter76 Ropsten Norra Hammarbyhamnen

401 Slussen Älta402 Slussen Kvarnholmen403 Slussen Hästhagen409 Slussen Ektorps C410 Slussen Saltängen411 Slussen Skuru413 Slussen Björknäs C414 Slussen Orminge C417 Slussen Hasseludden420 Slussen Gustavsberg C422 Slussen Gustavsberg, Lugnet

425X, 428X Slussen Gustavsberg, Björkvik430, 430X Slussen Eknäs brygga

431, 433, 434, 435, 436, 437,438, 440, 446, 462, 474

Värmdö Slussen

434 Slussen Sollenkroka435, 437 Slussen Hemmesta vägskäl

443 Slussen Jarlaberg444 Slussen Västra Orminge448 Slussen Gustavsvik449 Slussen Ektorp471 Slussen Västra Orminge474 Slussen Hemmesta vägskäl821 Tyresö C Nacka sjukhus840 Handenterminalen Nacka strand

The same type of table can be seen, with all relevant trail traffic lines (metro and lightrail) for the base scenario, in Table 18.

Table 18: Relevant metro/light rail lines for the base scenario


L22 Sickla udde Solna centrumL25, L26 Slussen Saltsjöbaden, Solsidan

T10, T11 (blue) Kungsträdgården Akalla, HjulstaT13, T14, T15 (red) Ropsten, Mörby centrum Alby, Fruängen, Norsborg

T17, T18, T19 (green) Alvik, Hässelby strand, Åkeshov Farsta, Hagsätra, Skarpnäck

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4 A CASE STUDY

Table 19 contains the transit lines for the future scenario, since some lines will bechanged or removed due to the new metro line.

Table 19: Relevant bus lines for the future scenario (the new or changed transit linesare stated in italics)


43 Ruddammen Tanto53 Roslagstull Henriksdalsberget55 Hjorthagen Sofia59 Karolinska sjukhuset Norra Hammarbyhamnen66 Reimersholme Sofia74 Mariatorget Sickla köpkvarter76 Ropsten Norra Hammarbyhamnen

401 Slussen Älta402 Slussen Kvarnholmen403 Slussen Hästhagen409 Nacka forum Ektorps C410 Nacka forum Saltängen411 Nacka forum Skuru413 Nacka forum Björknäs C420 Sickla bro Gustavsberg C422 Sickla bro Gustavsberg, Lugnet

425X, 428X Slussen Gustavsberg, Björkvik430, 430X Slussen Eknäs brygga

431, 433, 434, 435, 436, 437,438, 440, 446, 462, 474

Värmdö Slussen

434 Slussen Sollenkroka435, 437 Slussen Hemmesta vägskäl

442 Nacka forum Orminge C443 Nacka forum Jarlaberg444 Nacka forum Västra Orminge445 Nacka forum Orminge Ö448 Nacka forum Gustavsvik449 Nacka forum Ektorp471 Nacka forum Västra Orminge474 Nacka forum Hemmesta vägskäl821 Tyresö C Nacka sjukhus840 Handenterminalen Nacka strand

Table 20 shows the trail traffic lines for the future scenario, where the new metro lineare stated in italics.

Table 20: Relevant metro/light rail lines for the future scenario (the new or changedtransit lines are stated in italics)


L22 Sickla udde Solna centrumL25, L26 Slussen Saltsjöbaden, Solsidan

T10, T11 (blue) Hagsätra, Nacka forum Akalla/Hjulsta, BarkarbyT13, T14, T15 (red) Ropsten, Mörby centrum Alby, Fruängen, Norsborg

T17, T18, T19 (green) Alvik, Hässelby strand, Åkeshov Farsta, Hagsätra, Skarpnäck

44

4.1 Model building in Emme

The parameter settings in Emme are, as previously mentioned, obtained from Trafikver-ket and can be seen in the list below.

• Boarding time: 5.00

• Wait time factor: 0.50

• Weight factors:

– wait = 1.50

– auxiliary = 2.00

– boarding = 1.00

• Spread factor: 1.00

In order to limit the network a graphic modification tool from Emme 2 was used,and the limitation method follows the manual description of additional assignmentwith traversal matrix from the manual [1]. The graphic modification tool can definean area in which the chosen links or nodes are set to contain a user specified value.The links in the desired area were marked and then all links, nodes and zones wereexported to a new database. This database was dimensioned to contain 161 zones,600 regular nodes, 3 000 links, 100 transit lines and vehicles, 2 000 transit segmentsand 10 full matrices. The network now consists of the links, nodes and zones from themarked area. By combining transit lines that have the same route path the frequencyof that combined line can be increased, according to the headway-based assignmentalgorithm. All lines that did not affect the study area were eliminated and the numberof transit lines reduced to about 30 lines (both directions). These final transit lineswere adjusted with respect to the new area, by adding a line segment attribute thatstated if a segment was in the area or outside the area.

A problem that arises when cutting a network is to adjust the OD matrix so there still isthe same amount of travellers going to the corresponding origin and destination zone.This was solved by using an additional assignment for the auto-mobile mode calledtraversal matrix. The traversal matrix ensures that no transit volume that travels into, out from or inside the desired area will change from the previous matrix. All linksgoing in or out to a zone within the area were marked with that zone number (portalzones), in a link attribute. Links going in to the zone was assigned the positive zone idand links going out from the zone was assigned the negative zone id. This procedureleads to that all transit volume passing these marked links will be assigned a neworigin or destination of the portal zone number from that link attribute. The resultafter an assignment of this type is a new matrix containing all demand travelling insideor in to the limited network area. This traversal matrix was then imported to the newdatabase and a complete smaller network was obtained by adding the zones used inthe traversal matrix. The resulting network in Emme can be seen in Figure 9 and thetransit lines for the base and future scenario in Figure 10 and 11, where each transitline is represented by a separate color.

45

4 A CASE STUDY

Figure 9: The network built in Emme

The base scenario includes more transit lines that passes Nacka Forum and further onto Slussen. In the future scenario the transit lines have been altered and more buseshave Nacka Forum as the end station instead of Slussen. Also the new blue metro lineis added in the future scenario and can be spotted passing the water from Sofia toKungsträdgården.

Figure 10: Transit lines in the base scenario (each line is a separate color)

46

4.1 Model building in Emme

Figure 11: Transit lines in the future scenario (each line is a separate color)

4.1.1 Verification

With the limited network finalized it is of importance to check the model for errorsand to make sure that the results are realistic in comparison to the original models.A number of verifications were performed such as comparing the large scale networkwith the small using Emme 3 graphic result for transit and auxiliary transit volumes.If the difference in transit and auxiliary transit flow is low and the connectors insidethe network have no difference then the network is considered to function according tothe original network. In order to verify the traversal matrix a comparison between theorigin and destination sum matrices were done. The traversal matrix was correct sinceall zone demands inside the network were the same and all portal zone demands werelarger than or equal to the original demand. Another network check was to comparethe origin and destination sum matrices with the assigned auxiliary transit volumes.The model was corrected by adding connectors from some portal zones to one of theaccessible nodes inside the network.

47

4.2 Model building in Visum

The network in Emme was completed and then the network, transit line, vehicle, andmode files were exported and then imported to Visum through the import selection"Emme/2 - import parameters". In order for the network to function properly a fewadjustments needed to be made and a PuT-assignment to be created in the proceduresequence. First of all the walk links between origin/destination nodes and the rest ofthe network were not stated as connectors when importing to Visum. The solutionto this problem was to add a separate node for each connector and thus create linksbetween the connectors and the rest of the network (an option when importing thenetwork from Emme). Another problem that occurred was that the walk times, traveltimes with PuT and vehicle speed were incorrectly set. To adjust those issues one canuse the multi-edit tool for links and lines or manually change each line run time. It isalso important that the walk times are correctly set by editing the links and connectors.To be able to run the assignment a demand matrix needs to be added, in this case thetraversal matrix created in Emme was used. In the option for OD demand data thedemand segments was set to be collected from the traversal matrix. Another settingthat is required for the assignment is demand models, which consists of the standard4-step procedure mentioned in Chapter 2, Section 2.1.

From translating the assignment parameters in Emme to Visum the following valueswere obtained. Since there are no previous translations of these parameters an as-sumption was made from studying the manuals and how the parameters work in eachsoftware, see Chapter 3.

• Boarding penalty PuT: 5.00

• Boarding penalty PuT-Aux: 0.00

• Mean delay: 0.00

• Formula for origin and transit wait time: 0.50

• Weight factors:

– origin and transit wait = 1.50

– access, egress and walk = 2.00

– transfer = 0.00

– in-vehicle = 1.00

– PuT-Aux ride time = 1.00

The imported network in Visum can be seen in Figure 12.


Figure 12: The network built in Visum

When the network was completed a procedure sequence was created and set to theheadway-based assignment. The traversal matrix containing the trips made in thelimited network was used as input together with additional settings such as, simula-tion time interval, headway calculation and logit model options for distribution on theconnectors. Before running the simulation all assignment parameters were insertedaccording to the list above (not mentioned parameters were kept as the default value).There are also choice model information available here, which was described in Chap-ter 3, Section 3.4.1. The settings for all impedance parameters that are used to adjustthe assignment procedure are also changed in the assignment procedure dialogue.

4.2.1 Verification

When analysing the software algorithms the conditions in both Visum and Emme haveto be the same regarding network coding, frequency, in-vehicle and walk times etc. Inthis thesis the line run times and all other model coding are based on the future trafficprognoses in Emme obtained from Trafikverket. Therefore a verification of the Visummodel is done and several tests were made such as analysing the assignment statisticresults and matrices for in-vehicle time, origin wait time and walk time. The transitlines were studied as well, especially the total volume and travel time on each line.When comparing travel times with Emme (which in this case is considered to be thereality) some of the lines did not match. In certain situations there were stop pointsmissing in the time calculation. This was corrected by adding the missing stop pointsin the time profile for each line and updating the run time so that the correct traveltime was copied from the Emme data. Graphic parameters for link and connectorbars were also used in order to evaluate the model. The transit and walk volume wasassigned to the link bars and compared to the Emme model. An important setting for

49

4 A CASE STUDY

the boarding penalty PuT is the data format, which have the default format of seconds.In this case the data format "precise duration" was used instead of "integer" in order toobtain the penalty in minutes. One must also change this value for each time profile(i.e. each line) so that all lines obtains the correct penalty. In all cases where there aretime profile or stop area attributes, which the users can add themselves, it is impliedthat those values should be added in the respective menu list.

A small difference between the total walk, transit and transfer volume in the models wasobserved and further analyses of the assigned demand were required. When comparingthe input matrix and the assigned demand matrix the difference only consisted of theintrazonal demand, i.e. all demand which was going in and out through the same node.This was the case for Emme as well and therefore the differences ought to depend onthe route assignment algorithms or parameter settings, i.e. not a problem with theinput matrix.

The line run times for both Emme and Visum are verified and can be seen in Table 21(base scenario) and Table 22 (future scenario). The line direction is stated with "r" or"t" depending on whether it goes to or from the city center, and if there are severalline routes the letters "a" and "b" are used. From these tables it can be concluded thatVisum have a slightly lower line run time in both scenarios. This can either dependon rounding of the line run times or that Visum have less dwell time. In the modelanalysis the line times will be adjusted so that they are equal in order to identify if thedifferences depends on the line run times.

50


Table 21: Line run times from Emme and Visum (minutes) in the base scenario

Line EMME Visum Difference

43r 27.14 27.13 0.006743t 29.84 29.83 0.006753r 32.31 32.32 0.006753t 35.73 35.72 0.013355r 31.1 31.13 0.033355t 30.2 30.18 0.0167

59ra 26.05 26.07 0.016759rb 6 5.98 0.016759ta 22.35 22.37 0.016759tb 6 5.98 0.016766r 22 21.98 0.016766t 22.5 22.48 0.016774r 30.09 30.13 0.043374t 31.59 31.63 0.043376r 16.2 16.23 0.0333

401r 13.66 13.68 0.0233401t 13.13 13.17 0.0367402r 8.9 8.88 0.0167402t 8.86 8.82 0.0433403r 16.26 16.28 0.0233403t 15.72 15.75 0.0300409r 50.91 50.97 0.0567410r 30.4 30.25 0.1500410t 19.18 19.20 0.0200411t 30.28 30.30 0.0200413r 17.46 17.48 0.0233414r 17.46 17.48 0.0233417r 18.26 18.28 0.0233417t 21.18 21.20 0.0200420t 19.66 19.67 0.0067422r 16.46 16.48 0.0233425t 11.69 11.62 0.0733430r 11.8 11.72 0.0833430t 12.07 12.05 0.0200431t 11.16 11.10 0.0600434r 11.8 11.72 0.0833435r 11.2 12.12 0.9167443r 19.09 19.07 0.0233443t 18.59 18.37 0.2233444r 11.4 11.55 0.1500448t 14.72 14.77 0.0467449t 21.8 21.75 0.0500471r 18.13 18.13 0.0033471t 20.23 20.20 0.0300474r 13.3 13.22 0.0833821r 17.96 17.98 0.0233821t 16.98 16.98 0.0033840r 17.92 17.87 0.0533840t 17.93 17.87 0.063322r 4.03 4.03 0.003322t 6.03 6.03 0.003325r 13.98 13.95 0.030025t 13.98 13.95 0.030026r 13.91 13.85 0.060026t 13.91 13.85 0.0600

Blue r 2 1.98 0.0167Blue t 1 0.98 0.0167Red r 7 6.98 0.0167Red t 9 8.98 0.0167

Green r 9 8.98 0.0167Green t 11 10.98 0.0167

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4 A CASE STUDY

Table 22: Line run times from Emme and Visum (minutes) in the future scenario

Line EMME Visum Difference

43r 27.14 27.13 0.006743t 29.84 29.83 0.006753r 32.31 32.32 0.006753t 35.73 35.72 0.013355r 31.10 31.13 0.033355t 30.20 30.18 0.0167

59ra 26.05 26.07 0.016759rb 6.00 5.98 0.016759ta 22.35 22.37 0.016759tb 6.00 5.98 0.016766r 22.00 21.98 0.016766t 22.50 22.48 0.016774r 30.09 30.13 0.043374t 31.59 31.63 0.043376r 16.20 16.23 0.0333

401r 13.66 13.68 0.0233401t 13.13 13.17 0.0367402r 8.90 8.88 0.0167402t 8.86 8.82 0.0433403r 16.26 16.28 0.0233403t 15.72 15.75 0.0300409r 28.00 27.98 0.0167410r 12.92 12.87 0.0533410t 6.50 6.48 0.0167

411ra 26.60 26.58 0.0167411rb 18.10 18.08 0.0167413r 7.50 7.48 0.0167413t 8.00 7.98 0.0167420t 12.82 12.80 0.0200422r 10.00 9.98 0.0167425t 11.69 11.62 0.0733430r 11.80 11.72 0.0833430t 12.07 12.05 0.0200430t 13.42 13.35 0.0700431t 11.16 11.10 0.0600434r 11.80 11.72 0.0833435r 12.20 12.12 0.0833442t 5.19 5.15 0.0400443r 6.00 5.97 0.0333443t 5.23 5.22 0.0133444r 2.93 2.92 0.0133445t 4.69 4.65 0.0400448t 5.74 5.73 0.0067449t 13.19 13.15 0.0400471r 8.00 7.98 0.0167471t 9.24 9.23 0.0067474r 13.30 13.22 0.0833474t 11.43 11.38 0.0467821r 17.96 17.98 0.0233821t 16.98 16.98 0.0033840r 17.92 17.87 0.0533840t 17.93 17.87 0.063322r 6.04 6.05 0.010022t 8.04 8.05 0.010025r 13.98 13.95 0.030025t 13.98 13.95 0.030026r 13.91 13.85 0.060026t 13.91 13.85 0.0600

Blue r 3.99 3.97 0.0233Blue t 2.99 2.97 0.0233

New metro line, Blue r 12.50 12.50 0.0000New metro line, Blue t 11.50 11.50 0.0000

Red r 4.45 4.47 0.0167Red t 4.45 4.47 0.0167

Green r 9.00 8.98 0.0167Green t 11.00 10.98 0.0167

52

4.3 Model analysis

4.3 Model analysis

This section describes all experiments that have been performed in order to evaluaterespective software. First of all a parameter analysis was conducted to examine if thesoftware algorithms responds similar when changing the same parameter. Then a linetime analysis was done to obtain two networks with the exact same run times andtherefore be able to identify if the algorithms still differ. To examine the algorithmsfurther experiments with assigning only 100 trips between two zones was made. Finallyan analysis of how important public transport junctions such as Slussen and T-centralenwill be affected in the base versus future scenario in respective software.

4.3.1 Parameter analysis

It is of interest to find out how sensitive the results are with respect to the assignmentparameters. When analysing public transport systems there are standard values usedto reflect the impedance for the respective time component (e.g. wait time). When amodel is used to predict the future with estimated values it is hard to calibrate theseparameter values because there are no data to compare to. Also, when using limitednetworks and OD-matrices, as in this thesis, calibration and validation can only beperformed in very general terms. Due to these facts a sensitivity analysis has beenperformed. The analysis has been done with the base scenario in both Emme andVisum. First the results from the respective software will be presented and then acomparison is stated between the two.

According to a model from Trafikförvaltningen, they use different standard values forthe transit assignment parameters in Visum compared to the translated parameterslisted in Table 4. Therefore it is of interest to evaluate the differences, both betweenthe Visum scenarios and between the Emme scenarios. The values used at Trafikför-valtningen can be seen in the list below.

• Mean delay: 0.00

• Formula for origin and transit wait time: 1

• Weight factors:

– origin and transit wait = 2.00

– access, egress and walk = 2.00

– transfer = 5.00

– in-vehicle = 1.00

– PuT-Aux ride time = 1.00

4.3.2 Line run time analysis

As previously mentioned the line run times are slightly different in Emme and Visum.Therefore an evaluation will be made of the run times, the results are presented inSection 5.2.2.

53

4 A CASE STUDY

4.3.3 Algorithm analysis with 100 demand

In order to identify if the algorithms function in the same way this type of demandanalysis could be of importance to the thesis. When assigning only 100 trips betweentwo zones the passenger choice of optimal strategy will be easier to follow and evaluate.There have been five different tests performed between the orange circled areas in Fig-ure 13; Nacka Forum to Slussen (13a), Värmdö to Slussen (13b), North of T-centralento Gullmarsplan (13c), Saltsjöbaden to Gullmarsplan (13d), and Kvarnholmen to thenorth of T-centralen (13e).

(a) Nacka Forum to Slussen (b) Värmdö to Slussen

(c) North of T-centralen to Gullmarsplan (d) Saltsjöbaden to Gullmarsplan

(e) Kvarnholmen to the north of T-centralen

Figure 13: Areas that the 100 demand will be assigned between

54

4.3 Model analysis

4.3.4 Node analysis

It is of interest for the thesis to see the volume at a specific node and how it may changebetween the base- and future scenario. The chosen nodes are the ones that representsthe public transport stops at T-centralen, Kungsträdgården, Slussen, Sofia and Nackaforum, see Figure 14. The volume might differ between Emme and Visum and if sothese differences are important to be aware of. The question to be answered is if achange in the transit line volumes will affect the total volume at a station. Volumesat the stations can be significantly smaller than in reality which is due to the cut oftransit lines, i.e. some will walk the whole way due to shorter distances between originand destination. This could affect Kungsträdgården station especially since the northgoing metro only reaches T-centralen with the current network limitation (in realitythis line continues towards Akalla and Hjulsta). This leads to that a lot of passengersmost likely will choose to walk the short distance instead of using a transit line. TheOD-matrix is limited and the passengers have new destination/origin nodes where thedistance between the new nodes might be less than in the unlimited network. Thereforethe results will not be realistic, however there are settings that can force the passengersto board under certain circumstances (for example when the walk time is longer thana specified number of minutes).

Figure 14: The circled nodes that will be analysed

The volumes of each transit line will be collected from the simulation output and anal-ysed in Section 6.2. This gives an indication of how route choices are made in respectivesoftware. Other important output will be the total number of public transport trav-ellers, the total number of those who choose to walk from origin to destination, andthe number of transfers. These results will contribute to a more comprehensive viewof how travellers choose among the transit line options.

55

4 A CASE STUDY

4.4 Emme 4, extended transit assignment

A series of tests have been done with the base scenario network in the newest versionof Emme (Emme 4.0, test version), which contains more functional settings to thestandard transit assignment, see Chapter 3.3. In Emme 4.0 (in this thesis mentionedas Emme 4) it was not possible to get the number of travellers walking between the startand finish node directly from the assignment output. Therefore the results comparedare the total number of boardings and passenger kilometres.

All the new features will not be tested and the ones that is of interest in this study arethe flow distribution between the transit lines, the flow distribution at regular nodeswith auxiliary transit choices and flow distribution at origins. Also the standard transitassignment will be compared to the extended transit assignment in Emme 4 withoutusing any additional functions.

The assignment parameters used is the same as in Emme 3, see Chapter 4.1, but thereare some more parameters to the additional functions. The parameter scale will bevarying between 0 and 1 in the different tests.

56

5Results

All simulation results from the sensitivity analysis and the base/future scenario forrespective software algorithm are presented in this chapter. The important outputfor comparing the results will be total transit volume, transit line volumes, passengerkilometres, in-vehicle times, total walking volume, and the average number of transfersfor the passengers.

5.1 Simulation results

This section will present the results from simulations of the base and future scenario inboth Emme and Visum. It also contains the results from all model analysis mentionedin Section 4.3, i.e. parameter, line run time, 100 demand, and node analysis.

5.1.1 Emme 3 and Visum: base scenario

The result from simulation of the base scenario model is summarized in Table 23. Asshowed the total number of boardings is larger in Visum compared to Emme. This isdue to that the travellers choose routes containing more transfers in Visum and lesspeople walk the whole way to their destination node. This means that in Visum everyperson does an average of 0.4 transits per trip compared to in Emme where the averageis 0.36 transfers per trip.

Table 23: Output from the base scenario simulation in Emme and Visum

Base Transit N.o. N.o. Transfers Passenger Auxiliary Assignedscenario volume boardings transfers per passenger kilometres transit volume demand

Emme 75 516 102 707 27 191 0.36 301 126 21 197 96 713Visum 75 957 106 482 30 525 0.40 307 481 20 756 96 713

The volume (number of boarding) on each transit line for the base scenario is showedin Table 24 and 25. The absolute difference between Emme and Visum can also be seenin the table along with the relative difference, which states the absolute difference inrelation to the average value of both software results. The number of boardings differbetween almost every transit line, in some cases the volumes are lower and some higher

57

5 RESULTS

but as stated before the total number of boardings are larger in Visum. The resultfrom Emme is graphically shown in Figure 15, where the purple symbolizes the transitvolumes and red represents the volumes out from the origin nodes to the network(connector links). The result from Visum can be seen in Figure 16, and the volume isrepresented by the blue bars and the walking volume from origin or to destination ismarked in green.

Table 24: Total number of passengers boarding on each type of transit mode in respec-tive software, base scenario

Boardings Emme Boardings Visum Absolute difference Relative difference

All bus lines 39 781 42 814 3 033 0.02All light rail lines 2 600 2 488 112 0.01

All metro lines 60 325 61 180 855 0.005

Table 25: Number of passengers boarding on each line in respective software, basescenario

Line Boardings Emme Boardings Visum Absolute difference Relative difference

43r 1 102 1 187 85 0.0243t 4 797 4 924 127 0.00553r 2 882 3 090 208 0.01553t 4 079 3 686 393 0.02555r 1 715 1 268 447 0.07555t 1 061 1 721 660 0.12

59ra 165 197 32 0.04559rb 27 93 66 0.27559ta 22 29 7 0.0759tb 0 0 0 0.0066r 80 156 76 0.1666t 119 172 53 0.0974r 2 410 2 622 212 0.0274t 5 587 5 774 187 0.0176r 1 356 1 424 68 0.01

401r 457 1 208 751 0.225401t 510 808 298 0.115402r 481 506 25 0.015402t 548 496 52 0.025403r 170 451 281 0.225403t 80 127 47 0.115409r 678 683 5 0.00410r 0 0 0 0.00410t 736 822 86 0.03411t 55 62 7 0.03413r 804 643 161 0.055414r 643 513 130 0.055417r 629 490 139 0.06417t 266 274 8 0.005420t 354 334 20 0.015422r 805 635 170 0.06425t 688 662 26 0.01430r 11 99 88 0.40430t 51 43 8 0.045431t 4 164 4 214 50 0.005434r 11 99 88 0.40435r 10 111 101 0.42443r 78 88 10 0.03443t 159 598 439 0.29444r 140 263 123 0.155448t 18 63 45 0.28449t 138 140 2 0.005471r 567 321 246 0.14471t 735 567 168 0.065474r 48 541 493 0.42821r 42 108 66 0.22821t 54 56 2 0.01840r 99 78 21 0.06840t 150 370 220 0.2122r 321 314 7 0.00522t 172 83 89 0.17525r 335 164 171 0.1725t 943 819 124 0.03526r 0 287 287 0.5026t 829 821 8 0.00

Blue r 171 0 171 0.50Blue t 12 12 0 0.00Red r 8 756 9 033 277 0.01Red t 14 988 15 234 246 0.005

Green r 13 660 13 887 227 0.005Green t 22 738 23 014 276 0.005

All lines 102 706 106 482 3 776 0.01

58


Figure 15: Simulation results from the base scenario in Emme

Figure 16: Simulation results from the base scenario in Visum

59

5 RESULTS

5.1.2 Emme 3 and Visum: future scenario

The summarized result from the future scenario simulation can be seen in Table 26.The total transit volume is the same and the number of boardings is still lower inEmme but the difference is a little smaller compared to the base scenario. There are 1624 more boardings in Visum. The total number of transfers are lower in Emme andevery traveller does in average 0.35 transits per trip and in Visum 0.37 transits pertrip.

Table 26: Number of passengers boarding on each line in respective software, futurescenario

Future Transit N.o. N.o. Transfers Passenger Auxiliary Assignedscenario volume boardings transfers per passenger kilometres transit volume demand

Emme 75 995 102 645 26 650 0.35 304 606 20 718 96 713Visum 76 222 104 269 28 047 0.37 309 985 20 491 96 713

In Table 27 and 28 the results from the future scenario are presented. Note that thetransit lines are not all the same in both scenarios because some of the transit lineswere changed due to the building of the new metro line. The table states the numberof boardings on each transit line in both Emme and Visum, the absolute differenceand the relative difference. Figure 17 illustrates the results in Emme and Figure 18demonstrates the final result of the simulation in Visum.

Table 27: Total number of passengers boarding on each type of transit mode in respec-tive software, future scenario

Boardings Emme Boardings Visum Absolute difference Relative difference

All bus lines 27 420 31 047 3 627 0.03All light rail lines 5 437 4 376 1 061 0.055

All metro lines 69 792 68 848 944 0.005

60


Table 28: Number of passengers boarding on each line in respective software, futurescenario

Line Boardings Emme Boardings Visum Absolute difference Relative difference

43r 1 096 1 183 87 0.0243t 4 567 4 683 116 0.00553r 2 000 2 264 264 0.0353t 2 584 2 183 401 0.0455r 1 035 945 90 0.02555t 995 1 517 522 0.105

59ra 106 152 46 0.0959rb 27 94 67 0.27559ta 11 19 8 0.13559tb 0 0 0 0.0066r 66 163 97 0.2166t 71 95 24 0.0774r 921 1 416 495 0.10574t 2 472 2 985 513 0.04576r 934 1 203 269 0.065

401r 263 367 104 0.085401t 391 781 390 0.165402r 501 526 25 0.01402t 595 658 63 0.025403r 97 137 40 0.085403t 61 124 63 0.17409r 66 45 21 0.095410r 0 0 0 0.00410t 90 62 28 0.09

411ra 24 14 10 0.13411rb 14 14 0 0.00413r 665 249 416 0.23413t 91 104 13 0.035420t 142 212 70 0.10422r 431 218 213 0.165425t 643 825 182 0.06430r 41 195 154 0.325

430ta 51 0 51 0.50430tb 1 72 71 0.485431t 3 491 3 795 304 0.02434r 41 195 154 0.325435r 45 218 173 0.33442t 207 74 133 0.235443r 0 0 0 0.00443t 0 218 218 0.50444r 180 8 172 0.455445t 109 99 10 0.025448t 75 31 44 0.21449t 108 104 4 0.01471r 612 225 387 0.23471t 299 338 39 0.03474r 224 1 005 781 0.32474t 514 563 49 0.025821r 175 195 20 0.025821t 64 112 48 0.135840r 60 219 159 0.285840t 164 143 21 0.03522r 1 154 992 162 0.0422t 2 260 1 189 1 071 0.15525r 331 184 147 0.14525t 707 704 3 0.0026r 0 324 324 0.5026t 985 983 2 0.00

Blue r 346 302 44 0.035Blue t 484 487 3 0.00

New metro line, Blue r 3 322 2 009 1 313 0.125New metro line, Blue t 4 894 3 747 1 147 0.065

Red r 8 982 9 295 313 0.01Red t 17 193 17 244 51 0.00

Green r 14 096 14 510 414 0.005Green t 20 475 21 254 779 0.01

All lines 102 649 104 271 1 622 0.005

61

5 RESULTS

Figure 17: Simulation results from the future scenario in Emme

Figure 18: Simulation results from the future scenario in Visum

62

5.2 Model analysis results


Results from the different experiments that were mentioned in Section 4.3 are presentedin this chapter. The analysed parameters original value have been used from project toproject and does not have any documented scientific basis. Therefore it is of interestto examine what will happen to the results if changing one parameter at a time. Thiswill tell how sensitive the software results are to small or large adjustments of eachassignment parameter.

5.2.1 Results from parameter analysis

This section shows the evaluation of how the different software algorithms behave whenadjusting one transit assignment parameter at a time, while keeping the OD-matrixfixed. At first there will be a table stating the results from experimenting in Emme,i.e. Table 29. Then the same results for Visum are shown in Table 30.

Table 29: Results from the parameter analysis in Emme

Parameters Test value Total transit volume Number of transfers Transfers/person

Boarding time 0 81 013 72 584 0.90Orig. value 5 4.5 76 399 27 678 0.36

5.5 75 111 25 509 0.3410 70 461 16 559 0.24

Wait time factor 0.01 83 032 61 274 0.74Orig. value 0.5 0.4 76 982 26 660 0.35

0.6 74 408 24 384 0.331 71 441 20 721 0.29

Wait time weight 1 77 787 40 384 0.52Orig. value 1.5 1.4 76 338 26 989 0.35

1.6 75 225 26 304 0.352 73 862 23 978 0.32

Auxiliary time weight 1 62 186 11 712 0.19Orig. value 2 1.9 75 070 25 160 0.34

2.1 76 688 27 047 0.353 80 604 51 676 0.64

Original parameters 75 516 27 191 0.36

Table 30: Results from the parameter analysis in Visum

Parameters Test value Total transit volume Number of transfers Transfers/person

Boarding time 0 80 798 77 082 0.95Orig. value 5 4.5 76 785 31 817 0.41

5.5 75 407 28 727 0.3810 70 169 18 018 0.26

Wait time factor 0.01 82 542 59 159 0.72Orig. value 0.5 0.4 77 109 31 569 0.41

0.6 74 813 29 572 0.401 71 574 24 469 0.34

Wait time weight 1 78 007 44 441 0.57Orig. value 1.5 1.4 76 705 30 726 0.40

1.6 75 516 30 426 0.402 73 627 27 961 0.38

Auxiliary time weight 1 63 165 13 402 0.21Orig. value 2 1.9 75 393 28 851 0.38

2.1 77 047 32 166 0.423 80 281 56 429 0.70

Factor for NTR 1 75 812 27 904 0.37Orig. value 0 5 75 760 18 079 0.24

10 75 713 13 105 0.1715 75 711 5 157 0.07

Original parameters 75 957 30 525 0.40

The results from analysing the difference between Emme and Visum with respect tothe difference between the original results of transit volumes and number of transfers ineach software. The positive values represents that Visum have that much more transitvolume or number of transfers than Emme, see Table 31.

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5 RESULTS

Table 31: The difference between Visum and Emme with respect to the differencebetween the original results

Parameters Test value Difference in transit volume Difference in transfers

Boarding time 0 -657 1 164Orig. value 5 4.5 -55 805

5.5 -145 -11610 -733 -1 875

Wait time factor 0.01 39 129 -3 334Orig. value 0.5 0.4 -932 -5 448

0.6 -314 1 5761 -36 1 855

Wait time weight 1 -309 415Orig. value 1.5 1.4 -221 723

1.6 -74 4032 -150 787

Auxiliary time weight 1 -677 649Orig. value 2 1.9 538 -1 644

2.1 -118 3573 -82 1 786

Original parameters 441 3 334

Parameter analysis results in Emme

The resulting output using the original parameter values (see list in Table 4) can beseen in Table 32. There are about 75 500 persons that use the public transport networkwhich of approximately 27 200 transfers during the route. The number of persons whochooses to walk the whole way between origin and destination is almost 21 200.

Table 32: Results in Emme with the original parameter settings

Transit Total boarding N.o. Transfers Passenger Auxiliary Assignedvolume boarding transfers per passenger kilometres transit volume demand

75 516 102 707 27 191 0.36 301 126 21 197 96 713

When changing the boarding time, from 5 to 0, 4.5 and 5.5, it leads to the resultsseen in Table 33. When there is less time penalty for boarding the transit volume willincrease along with the number of transfers during a route. When the penalty is zerominutes, most demand choose to ride with the transit lines. If the penalty is set to 4.5instead, the result leads to that less people travel with transit lines and there are fewertransfers made. If there is a higher boarding penalty than originally, 5.5 minutes, thetotal transit volume and number of transfers will decrease.

Table 33: Parameter analysis results for the boarding time weight in Emme

Boarding Transit Total boarding N.o. Transfers Passenger Auxiliarytime volume boarding transfers per passenger kilometres transit volume

0 81 013 153 597 72 584 0.90 336 704 15 7004.5 76 399 104 077 27 678 0.36 302 368 20 3145.5 75 111 100 620 25 509 0.34 300 208 21 60210 70 461 87 020 16 559 0.24 289 309 26 252

The result from changing the wait time factor is showed below in Table 34. The waittime factor can be set between 0 and 1, and gives the distribution of travellers arrivalto the transit stops. If the factor is 0.5 there is a uniform distribution of travellers andlower values can be used if there are long headways or passengers know the timetable.This means that the travellers wait half the waiting time in average, which in theassignment is represented by the passengers arriving at the station after half the waiting

64


time. A higher value of the wait factor gives less total transit volume and more demandwill decide to walk to the destination instead, which reflects a transport system withhigh timetable uncertainties.

Table 34: Parameter analysis results with wait time factor in Emme

Wait Transit Total boarding N.o. Transfers Passenger Auxiliarytime factor volume boarding transfers per passenger kilometres transit volume

0.01 83 032 144 306 61 274 0.74 333 386 13 6810.4 76 983 103 642 26 660 0.35 302 882 19 7310.6 74 408 98 792 24 384 0.33 298 064 22 3051 71 441 92 162 20 721 0.29 286 695 25 272

The waiting time weight indicates how the travellers perceive the time to wait for atransit vehicle, see result in Table 35. The original value is 1.5 and when the waittime is less important, e.g. 1 or 1.4, the transit volume will increase. If it is weightedwith 1 approximately 2 300 more persons will decide to use public transport insteadof walking the whole way. The number of transfers will increase significantly, about 13200 more. This depends on that more routes with transfers will be attractive due tothat the wait time for transferring is weighted less. When the weight is 1.4 the numberof transfers will decrease with about 200, however the total volume that chooses publictransport increases with more than 800. This depends on that passengers have moreincentive to take a route with only one boarding than to board several times. If theweight is set to 1.6, more people (290) will choose to walk instead and the number oftransfers decreases with almost 900. When the weight is increased to 2, the numberof transfers decrease with about 3 200 and approximately 1 700 persons less choose touse public transport.

Table 35: Parameter analysis results with wait time weight in Emme

Wait Transit Total boarding N.o. Transfers Passenger Auxiliarytime weight volume boarding transfers per passenger kilometres transit volume

1 77 787 118 171 40 384 0.52 320 163 18 9261.4 76 338 103 327 26 989 0.35 302 201 20 3751.6 75 225 101 530 26 304 0.35 300 204 21 4882 73 862 97 840 23 978 0.32 296 668 22 851

The auxiliary time weight will affect how many that chooses to walk instead of usingthe public transport network. It will also affect the number of transfers due to thatthe transfer walk time (all walking inside the network) is included in the auxiliarytime. The result from this parameter analysis are available in Table 36. The originalvalue for the weight is 2 and when the time is unweighted (compared to in-vehicletime, i.e. weight equals 1) significantly more demand will walk instead of riding witha public transit vehicle. It differs with more than 13 000 and the number of transferswill increase with almost 15 500. If the weight is 1.9, almost the original value, about445 more persons will choose to walk to the destination and the number of transfersdecreases with 2 030. When the weight is increased to 2.1 the demand that uses publictransport decreases with approximately 1 170 and the number of transfers drops withabout 145.

65

5 RESULTS

Table 36: Parameter analysis results with auxiliary time weight in Emme

Auxiliary Transit Total boarding N.o. Transfers Passenger Auxiliarytime weight volume boarding transfers per passenger kilometres transit volume

1 62 186 73 898 11 712 0.19 276 275 34 5271.9 75 070 100 231 25 160 0.34 299 431 21 6432.1 76 688 103 735 27 047 0.35 303 594 20 0253 80 604 132 280 51 676 0.36 339 694 16 109

Parameter analysis results in Visum

A parameter translation has been made between Emme and Visum so the sensitivityanalyses are done with the same conditions. The result with these original values canbe seen in Table 37.

Table 37: Results in Visum with the original parameter settings

Transit Total boarding N.o. Transfers Passenger Auxiliary Assignedvolume boarding transfers per passenger kilometres transit volume demand

75 957 106 482 30 525 0.40 307 481 307 481 96 713

The boarding time in Emme correspond to the boarding time penalty PuT in Visumand the assignment parameter is changed from values between 0-5.5 see Table 38 forthe results. With a penalty of 0 an increment of the total transit volume of over 4800 people and more will choose a route that includes transfers, almost 47 000 morethan the original parameter result. If the boarding penalty changes to 4.5 there is anincrement, however not as large as without penalty. Approximately 800 more will usepublic transport instead of walking and the number of transfers will almost be 1 300more. If the boarding time instead was set to 5.5 minutes, the total number that usethe public transport will decrease with 550 passengers and the number of transfers willbe 1 800 less.

Table 38: Parameter analysis results with boarding time penalty in Visum

Boarding Transit Total boarding N.o. Transfers Passenger Auxiliarytime penalty volume boarding transfers per passenger kilometres transit volume

0 80 798 157 879 77 082 0.95 347 529 15 9164.5 76 785 108 602 31 817 0.41 309 213 19 9285.5 75 407 104 134 28 727 0.38 305 833 21 3065.5 70 169 88 187 18 018 0.26 292 705 26 545

In Visum the wait time factor is split into two parts, formula for OWT (origin waittime) and for TWT (transfer wait time), and they are both set to 0.5 in the originaltranslation. Both parameters have the same values in each test so it should reflect thesame changes as in Emme, see result in Table 39 below. Low values leads to that morepassengers travel with the transit lines, for example the penalty 0.01 gives around 6 600more passengers and nearly 29 000 more transfers between two vehicles. If the valueis changed to 0.4, the result will still be that more chooses to use public transport,there will be approximately 1 100 more. The number of transfers will also be around1 100 more than the original result. If the values instead are 0.6, the total number ofpassengers travelling with public transport will be about 1 100 less and the number oftransfers will decrease with nearly 950 compared to the original settings.

66


Table 39: Parameter analysis results with the formula for origin and transfer wait timein Visum

Formula for Transit Total boarding N.o. Transfers Passenger AuxiliaryOWT,TWT volume boarding transfers per passenger kilometres transit volume

0.01 82 542 141 701 59 159 0.72 328 595 14 1720.4 77 109 108 679 31 569 0.41 309 337 19 6040.6 74 813 104 385 29 572 0.40 306 079 21 9011 71 574 96 043 24 469 0.34 293 950 25 140

The wait time weight in Emme corresponds to the factors for both OWT and TWT,which originally is set to 1.5. The result after shifting these values can be seen inTable 40. If the new factor is set to one, which means the time is unweighted, comparedto in-vehicle time, about 2 000 persons will switch from walking to using the publictransport system. There will be about 14 000 more transfers and when the factor is 1.4there are 200 more transfers compared to the original result. The total transit volumewhen the value is 1.4 has increased with more than 740 compared to the decrease ofaround 440 when the factor was 1.6. Higher values of these factors will lead to largedifferences, especially regarding the number of transfers.

Table 40: Parameter analysis results with factor for origin and transfer wait time inVisum

Factor for Transit Total boarding N.o. Transfers Passenger AuxiliaryOWT,TWT volume boarding transfers per passenger kilometres transit volume

1 78 007 122 448 44 441 0.57 322 926 18 7061.4 76 706 107 432 30 726 0.40 307 878 20 0081.6 75 516 105 942 30 426 0.40 306 809 21 1972 73 627 101 588 27 961 0.38 303 245 23 087

The auxiliary time is divided into three parts in Visum; Access time (ACT), Egresstime (EGT) and Walk time (WT). The original value in Emme is 2 and thereforeeach of part of the auxiliary time in Emme is also 2. In the analysis all three partswere switched to the same value in each simulation, in Table 41 the results are stated.Unweighted times, factors equal to 1, gives decreased total transit volumes, about 12800, and the number of transfers will also be less (approximately 17 100). If the factorsare set to 1.9, the difference will not be as high compared to the original result, about560 persons chooses to walk instead and the transfers differs with 1 670. If the factoris set to a higher value, 2.1, 1 090 will switch from walking to taking public transportinstead. People will also choose routes with more transfers (1 642 more) when thefactor is 2.1.

Table 41: Parameter analysis results with factor for access, egress and walk time inVisum

Factor for Transit Total boarding N.o. Transfers Passenger AuxiliaryACT,EGT,WT volume boarding transfers per passenger kilometres transit volume

1 63 165 76 566 13 402 0.21 253 996 33 5481.9 75 393 104 244 28 851 0.38 304 474 21 3202.1 77 047 109 214 32 167 0.42 311 267 19 6663 80 282 136 710 56 429 0.7 348 235 16 432

There is a factor in Visum that does not exist in Emme, i.e. a penalty for transfer-ring. The penalty is set in minutes and will be multiplied with the number of transfers

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5 RESULTS

made on a trip. With 1 minute in penalty it does not lead to any significant differenceregarding the total transit volume (only 144 more), although it affects the number oftransfers among the routes, about 2 600 less transfers. A penalty of 15 minutes givesa large difference when it comes to the total volume travelling with public transport,which decreases with 245 persons. A 5 minute penalty gives a decrease of approxi-mately 12 500 less transfers and 15 minutes leads to more than 25 300 less transfers.Therefore this factor should be thoroughly chosen and evaluated before using it intraffic simulation studies. The results from the mentioned experiments can be seen inTable 42.

Table 42: Parameter analysis results with factor for number of transfers (NTR) inVisum

Factor Transit Total boarding N.o. Transfers Passenger Auxiliaryfor NTR volume boarding transfers per passenger kilometres transit volume

1 75 812 103 716 27 904 0.37 305 229 20 9015 75 760 93 839 18 079 0.24 298 322 20 953

10 75 713 88 818 13 105 0.17 295 589 21 00015 75 711 80 868 5 157 0.07 286 652 21 002

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5.2.2 Results from line run time analysis

By analysing the line itineraries and changing the times in Visum so that they almostare the same, see Table 43 for the calibrated line run times, it can be determined thatthe differences does not depend on the difference in line run times. This is due to thefact that the difference between the total number of boardings in respective softwarestill are significantly high.

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

Line Emme line run time New Visum line run time Absolute Difference

43r 27.14 27.13 0.0143t 29.84 29.83 0.0153r 32.31 32.32 0.0153t 35.73 35.72 0.0155r 31.1 31.1 0.0055t 30.2 30.18 0.02

59ra 26.05 26.07 0.0259rb 6 5.98 0.0259ta 22.35 22.37 0.0259tb 6 5.98 0.0266r 22 21.98 0.0266t 22.5 22.48 0.0274r 30.09 30.1 0.0174t 31.59 31.6 0.0176r 16.2 16.2 0.00

401r 13.66 13.67 0.01401t 13.13 13.13 0.00402r 8.9 8.88 0.02402t 8.86 8.85 0.01403r 16.26 16.27 0.01403t 15.72 15.72 0.00409r 50.91 50.92 0.01410r 30.4 30.4 0.00410t 19.18 19.18 0.00411t 30.28 30.28 0.00413r 17.46 17.47 0.01414r 17.46 17.47 0.01417r 18.26 18.27 0.01417t 21.18 21.18 0.00420t 19.66 19.67 0.01422r 16.46 16.47 0.01425t 11.69 11.68 0.01430r 11.8 11.8 0.00430t 12.07 12.07 0.00431t 11.16 11.15 0.01434r 11.8 11.8 0.00435r 11.2 11.2 0.00443r 19.09 19.08 0.01443t 18.59 18.58 0.01444r 11.4 11.4 0.00448t 14.72 14.72 0.00449t 21.8 21.8 0.00471r 18.13 18.13 0.00471t 20.23 20.23 0.00474r 13.3 13.33 0.03821r 17.96 17.97 0.01821t 16.98 16.98 0.00840r 17.92 17.92 0.00840t 17.93 17.92 0.0122r 4.03 4.03 0.0022t 6.03 6.03 0.0025r 13.98 13.98 0.0025t 13.98 13.98 0.0026r 13.91 13.9 0.0126t 13.91 13.9 0.01

Blue r 2 1.98 0.02Blue t 1 0.98 0.02Red r 7 6.98 0.02Red t 9 8.98 0.02

Green r 9 8.98 0.02Green t 11 10.98 0.02

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5 RESULTS

Table 44 shows the number of boardings on each line and the absolute difference be-tween the new run times in Visum and the base scenario in Emme. The total absolutedifference is 3753 boardings more or less.

Table 44: Line boardings with the new run times in Visum and the original run timesin Emme

Line Visum boardings, new run times EMME boardings, original run times Absolute difference

43r 1 187 1 102 8543t 4 925 4 797 12853r 3 087 2 882 20553t 3 689 4 079 39055r 1 267 1 715 44855t 1 721 1 061 660

59ra 197 165 3259rb 93 27 6659ta 29 22 759tb 0 0 066r 156 80 7666t 172 119 5374r 2 622 2 410 21274t 5 777 5 587 19076r 1 425 1 356 69

401r 1 208 457 751401t 824 510 314402r 507 481 26402t 498 548 50403r 451 170 281403t 127 80 47409r 685 678 7410r 0 0 0410t 834 736 98411t 62 55 7413r 646 804 158414r 516 643 127417r 489 629 140417t 278 266 12420t 334 354 20422r 639 805 166425t 658 688 30430r 101 11 90430t 40 51 11431t 4 186 4 164 22434r 101 11 90435r 116 10 106443r 97 78 19443t 589 159 430444r 268 140 128448t 68 18 50449t 140 138 2471r 275 567 292471t 554 735 181474r 550 48 502821r 108 42 66821t 56 54 2840r 78 99 21840t 364 150 21422r 314 321 722t 83 172 8925r 164 335 17125t 819 943 12426r 287 0 28726t 821 829 8

Blue r 0 171 171Blue t 12 12 0Red r 9 033 8 756 277Red t 15 234 14 988 246

Green r 13 887 13 660 227Green t 23 014 22 738 276

All lines 106 459 102 706 3 753

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In Table 45 the boarding difference between the new run times and the original runtimes in Visum can be seen. When comparing the original run time results and the newrun time results in Visum the number of boardings only differ with 22. This comparisonstates that the calibration of line run times did not lead to any significant improvement.Therefore the run times could not be the reason why the main differences between thenumber of boardings occur.

Table 45: Line boardings with the new and the original run times in Visum

Line Visum boardings, new run times Visum boardings, original run times Absolute difference

43r 1 187 1 187 043t 4 925 4 924 153r 3 087 3 090 253t 3 689 3 686 355r 1 267 1 268 155t 1 721 1 721 1

59ra 197 197 059rb 93 93 059ta 29 29 059tb 0 0 066r 156 156 066t 172 172 074r 2 622 2 622 074t 5 777 5 774 376r 1 425 1 424 0

401r 1 208 1 208 1401t 824 808 16402r 507 506 2402t 498 496 2403r 451 451 0403t 127 127 0409r 685 683 2410r 0 0 0410t 834 822 12411t 62 62 1413r 646 643 3414r 516 513 3417r 489 490 0417t 278 274 4420t 334 334 0422r 639 635 3425t 658 662 4430r 101 99 1430t 40 43 2431t 4 186 4 214 28434r 101 99 2435r 116 111 5443r 97 88 10443t 589 598 10444r 268 263 5448t 68 63 5449t 140 140 0471r 275 321 46471t 554 567 13474r 550 541 8821r 108 108 0821t 56 56 0840r 78 78 0840t 364 370 622r 314 314 022t 83 83 025r 164 164 025t 819 819 026r 287 287 026t 821 821 0

Blue r 0 0 0Blue t 12 12 0Red r 9 033 9 033 0Red t 15 234 15 234 0

Green r 13 887 13 887 0Green t 23 014 23 014 0

All lines 106 459 106 482 22

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5 RESULTS

5.2.3 Results from algorithm analysis with 100 demand

When assigning 100 trips between five different zone pairs the resulting mean in-vehicle time and number of boardings for respective software can be seen in Ta-ble 46 and 47.

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

From area To area Emme Visum

T-centralen Gullmarsplan 9 8.98Saltsjöbaden Gullmarsplan 12.48 12.48

Värmdö Slussen 11.22 11.17Kvarnholmen T-centralen North 28.2 28.15Nacka Forum Slussen 12.6 12.58

From these mean in-vehicle times one can see that Visum have lower, or equal, valuesthan Emme. This might depend on how the different programs calculates and roundsnumbers in time format.

Table 47: Number of boardings per line for the five tests with 100 demand

From area To area Line Boardings Emme Boardings Visum

T-centralen Gullmarsplan Green r 100 100Saltsjöbaden Gullmarsplan 22r 100 100

25t 50 5026t 50 50

Värmdö Slussen 425t 14 14431t 86 86

Kvarnholmen T-centralen North 53t 62 62402r 38 38

Green t 38 38Nacka Forum Slussen 409r 9 9

410t 32 32411t 2 2417t 9 9420t 12 12471t 35 35

The number of boardings are exactly the same for all experiments and lines, whichimplies that the algorithms work in the same way in this specific case.

However, one of the lines shows a difference between the algorithms when assigningonly 100 demand from Gullmarsplan to the most eastern part of Nacka municipality inthe future scenario. The results are presented in a diagram, Figure 19, by the numberof boardings on each used line.

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Figure 19: The number of boardings

According to these results one can see that the Visum passengers choose itinerarieswith more transfers involved than the passengers in Emme. Visum also have threemore lines that passengers choose to travel with. The total number of boardingswill not be the same in both software since Visum have more boardings due to theassignment algorithm and parameter settings.

5.2.4 Results from node analysis

The node analysis was made in both Emme and Visum and the results are presentedseparately to be compared later in Section 6.3.

Node analysis results in Emme

All results from analysing the mentioned nodes in Emme can be seen in Table 48 and49 in respective scenario.

The boarding volumes at T-Centralen will increase (+382) and this depends on thatthe number of transfer boardings are higher in the base scenario compared to the futurescenario. The number of passengers alighting at T-centralen will instead decrease (-1329) in the future scenario. The flow of passengers who passes through the station ona transit vehicle is basically unchanged.

Kungsträdgården will have more passengers boarding after the opening of the newmetro line and also more total alighting passengers. The largest increase is the numberof through passages in the future scenario, 5 447 more compared to 0 in the basescenario.

No travellers are passing through Slussen in any of the scenarios. This is due to the cutof the transit lines and there are no possibilities to just pass Slussen in this network,

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5 RESULTS

passengers can only board or alight a transit line at this node. The total number ofpassengers who alight a transit line (including transfer) at the station will decreasewith 733 in the future scenario. 34 passengers find other itineraries to reach theirfinal destination and 26 passengers will not start their route at Slussen. The numberthat boards a vehicle at Slussen decrease with 1 611 passengers compared to the basescenario.

The Sofia station does not have any volumes in the base scenario since there are notransit lines available until the future scenario. Then the new metro will pass Sofiawhich will of course increase the volumes and that affects boarding, alighting andpassing travellers.

Nacka Forum will have a larger number of total boarding passengers and alightingpassengers in the future scenario (+1 072 and +402) compared to the base scenario.The sum of travellers who passes the station onboard of a vehicle will decrease withabout 2 000 passengers. This is due to the fact that the transit lines are designeddifferently in the two scenarios. Most transit lines that passes Nacka Forum in thebase scenario have this station as an end station in the future scenario.

Table 48: Node results from the base scenario in Emme

Boarding passengers Passengers through Alighting passengersStation Initial Transfers Total Final Transfer Total

T-centralen 4 296 3 081 7 377 21 235 12 954 2 901 15 856Kungsträdgården 12 0 12 - 171 0 171

Slussen 158 2 093 2 251 - 521 4 552 5073Sofia - - - - - - -

Nacka Forum 253 368 621 2 492 84 368 452

Table 49: Node results from the future scenario in Emme


T-centralen 4 076 3 683 7 759 21 382 11 294 3 233 14 527Kungsträdgården 102 70 172 5 447 1 303 28 1 331

Slussen 132 507 640 - 487 3 853 4 340Sofia 1 036 0 1 036 5 289 638 0 638

Nacka Forum 103 1 440 1 543 295 105 750 854

Node analysis results in Visum

All results from analysing the mentioned nodes in Visum can be seen in Table 50 and51 in respective scenario.

At T-centralen the total number of boarding passengers will decrease with 2 522 pas-sengers from the base to the future scenario. This depends mostly on the number ofpassengers that transfers at T-centralen are less after the new metro line is built. Thenumber decrease with 2 285 passengers compared to before. More passengers will justpass the node without stopping; approximately 3 300 more travellers will pass the nodein a vehicle. Almost 4 050 people less will alight at the node and it mostly depends onthat less (-3 001) people will alight for a transfer.

Kungsträdgården will have more total boarding and total alighting passengers afterthe new metro line is open. However, the largest difference between the scenarios isthe number of passengers that only passes the station (+4 778).

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Slussen will have less people boarding (-764) and alighting (-747) at the stop. Thedecrease is caused by that less people will transfer at the node.

Sofia will of course have larger amount of travellers in every category because in thefuture scenario there will actually be a transit line passing the node. 3 703 passengerswill pass the node without stopping and totally 1 139 people will board and 699 peoplewill alight.

Nacka Forum will loose boarding passengers and that can depend on the transit linesare adjusted when the new metro line is open. 181 people less will start their journeyat Nacka Forum but 54 more will do a transfer boarding. Fewer people will pass thestop (-1 350) after the new metro is open and the number of alighting will decreasewith 395 passenger. This also depends on that fewer passengers will transfer at thestation.

Table 50: Node results from the base scenario in Visum


T-centralen 4 354 5 955 10 309 18 414 13 721 5 631 19 352Kungsträdgården 12 0 12 - 0 0 0

Slussen 124 1 906 2 030 - 523 5 169 5 691Sofia - - - - - - -

Nacka Forum 213 225 439 1 742 86 224 310

Table 51: Node results from the future scenario in Visum


T-centralen 4 117 3 670 7 787 21 726 12 673 2 630 15 303Kungsträdgården 39 39 79 4 778 425 0 425

Slussen 99 1 167 1 266 - 494 4 451 4 944Sofia 1 139 0 1 139 3 703 699 0 699

Nacka Forum 32 279 312 392 120 585 705

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5 RESULTS

5.3 Emme 4, extended transit assignment, results

The result from the optimal strategy in Emme 4 and extended optimal strategy inEmme 4 is stated in Table 52 below.

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

Emme version N.o. boardings Passenger kilometres

Emme 4, standard transit assignment 102 707 301 126Emme 4, extended transit assignment 102 696 300 751

The result from using the flow distribution at origins choice can be seen in Table 53.The logit function scale is varied between 0 and 1, where 0 represents an equal dis-tribution between the connector links and 1 represents all travellers using the bestconnector.

Table 53: Result from using the option flow distribution at origins

Scale, distribution at origins N.o. boardings Passenger kilometres

1 103 903 301 8600.75 104 767 302 8960.5 107 079 305 503

0.25 109 442 308 3130 112 828 310 599

The result from using the flow distribution at regular nodes with auxiliary transitchoices can be seen in Table 54. The scale is the same for the distribution at originsfor all test (0.5) while the scale for the regular nodes are varied. The distribution inthis case is between the amount of travellers that waits for another attractive publictransport line or uses an efficient auxiliary route instead of staying on board the currentvehicle. The logit function scale is varied between 0 and 1, where 0 represents an equaldistribution between the connector links and 1 represents that all travellers use thebest option.

Table 54: Result from using the option flow distribution at regular nodes with auxiliarytransit choices

Logit setting Scale, regular nodes N.o. boardings Passenger kilometres

Logit distribution between auxiliary and wait 1 107 499 306 265Logit distribution between auxiliary and wait 0.75 107 870 306 827

Optimal strategy (all passengers chooses the same line/aux. path) 0.5 107 079 305 503Logit distribution between auxiliary and wait 0.5 108 228 307 968Logit distribution between auxiliary and wait 0.25 109 149 309 909Logit distribution between auxiliary and wait 0 95 495 276 552

The result from using the flow distribution between transit lines can be seen in Table 55.Both the scale for the distribution at origins and regular nodes is constant 0.5. Thesettings regarding logit distribution between auxiliary and waiting will still be used,i.e. the optimal strategy is not used in this case in order to obtain some variation inthe passengers choice of route.

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5.3 Emme 4, extended transit assignment, results

Table 55: The result when using the additional setting to use flow distribution betweentransit lines

Flow distribution between transit lines N.o. boardings Passenger kilometres

Frequency only 108 228 307 968Frequency and transit time to destination 106 529 306 627

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6Analysis

This chapter concludes the comparisons of the software algorithms from the simulationresults, software sensitivity, and node analysis.

6.1 Comparison of the simulation results

When comparing the transit line volumes there are some differences in general buthow big they are depend on the specific transit line. It is of interest to see how theboarding volumes will split up in the base and future scenario. The volumes on eachtransit line in Emme and Visum for the base scenario can be seen in Table 25 and thevolumes in the future scenario in Table 28. The transit lines with most differences havebeen summarized in Table 59. The number is the absolute difference between the twoscenarios for each software. All relevant simulation results from the base scenario inrespective software are mentioned in Table 56. Regarding the future scenario Table 57states the same output measurements and also contains a comparison between the baseand future scenario in each software.

Table 56: Comparison between Emme and Visum results from the base scenario

N.o. public N.o. passengerstransport N.o. N.o. N.o. transfers Passenger that walks Total n.o.

Base scenario passengers boardings transfers per passenger kilometres the entire way travellers

Emme 75 516 102 707 27 191 0.36 301 126 21 197 96 713Visum 75 957 106 482 30 525 0.40 307 481 20 756 96 713

Difference 441 3 775 3 334 0.04 6 355 -441 0Emme relative

Visum 0.6 % 3.7 % 12.3 % 11.1 % 2.1 % - 2.1 % 0

Table 57: Comparison between Emme and Visum results from the future scenario


Future scenario passengers boardings transfers per passenger kilometres the entire way travellers

Emme 75 995 102 645 26 650 0.35 304 606 20 718 96 713Visum 76 222 104 269 28 047 0.37 309 985 20 491 96 713

Difference 227 1 624 1 397 0.02 5 379 -227 0Visum relative

Emme 0.3 % 1.6 % 5.2 % 5.7 % 1.8 % - 1.1 % 0Future- relative base

scenario in Emme 0.6 % 0.1 % 1.9 % 2.8 % 1.2 % - 2.3 % 0Future- relative base

scenario in Visum 0.3 % 2.1 % 8.1 % 7.5 % 0.8 % - 1.3 % 0

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6 ANALYSIS

From the Visum model parameters by Trafikförvaltningen mentioned in Section 4.3.1the simulation results stated in Table 58 was obtained with the base scenario. TheEmme results when using the Trafikverket parameter values are also stated in thistable as well as comparisons made between the two models. The results are even moredifferent with these parameter settings, which implies that the parameter values affectsthe results. Therefore the public transport parameters should be calibrated first ratherthan other network attributes.

Table 58: Comparison of the results obtained from Emme with the Trafikverket pa-rameter values and from Visum with the Trafikförvaltning parameter values


Base scenario passengers boardings transfers per passenger kilometres the entire way travellers

Emme,Trafikverket 75 516 102 707 27 191 0.36 301 126 21 197 96 713

Visum,Trafikförvaltningen 74 306 96 785 22 479 0.30 291 708 22 407 96 713

Difference 1 210 5 921 4 712 0.06 9 418 -1 210 0Emme relative

Visum 2% 6% 17% 16% 3% -6% 0

There will be variations regarding the transit lines since some of them have beenedited between the scenarios. These lines will not be of interest when comparing thescenarios within the same software. This is due to that the boarding volumes can changedrastically from the base to the future scenario because of the editing. Therefore theyare not included in Table 59. However, when comparing the two software algorithmsthese edited transit lines are of high importance.

Table 59: Absolute boarding difference between base and future scenario in respectivesoftware

Line Visum Emme

53t 1 503 1 49574r 1 206 1 48974t 2 789 3 11522t 1 106 2 08825t 262 22625r 2 010 2 205

Green t 1 760 2 263

The transit line 53t loose passenger in the future scenario in comparison to before thenew metro line is open. The difference between the scenarios in Visum is a little higherthan in Emme but the difference is almost negligible. Travellers take the new metroline from Sofia instead of taking the bus and could be a contributing factor why thereare less passengers travelling with line 53t.

The transit line 74r (Mariatorget to Sickla köpkvarter) also have fewer passengers inthe future scenario in both Visum and Emme. The differences are more significance inEmme where it changes from 2 410 passengers to 921 and in Visum from 2 622 to 1416 passengers. This is also the case for line 74t which is the opposite direction (Sicklaköpkvarter to Mariatorget), 2 789 passengers less in Visum and 3 115 in Emme. In thefuture scenario more people take the metro line to Slussen or T-centralen and transit tothe new blue line to get to their destination/origin in Nacka municipality/Södermalminstead of taking the bus line 74t.

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6.1 Comparison of the simulation results

The light rail line 22t, from Solna centrum to Sickla udde, will have more travellersin the future scenario and in Emme the total increase is 1 106 compared to Visum,2 088. In this scenario a lot of the buses stop at Nacka Forum and only have routeswithin Nacka municipality. So in order to travel from the other areas in Stockholm toNacka municipality the choices are not as many as in the base scenario and this canbe a reason for the increased number of passengers on line 22t.

The red metro line, north direction, have an increase in the future scenario with 2 010passengers and in south direction a small increase of 262 people in Visum. Emme alsohave more passengers in the future on the red line in the north direction, 2 205, and226 more in the south direction compared to the base scenario. The increase in northdirection could be an effect of that more people choose the red line and later changeto blue line in order to reach Nacka Forum faster.

The green metro line in the north direction loses passengers when the new metro line isopen in both Emme and Visum. The decrease is larger in Emme where the differenceis 2 263 people and in Visum 1 760 persons. The loss of passengers could be an effectof that the travellers might have their origin node closer to the new metro line andchose to use this instead when travelling north. In Figure 20 the boarding differencesfrom Table 59 can be seen in a diagram.

Figure 20: Diagram over boarding difference between base and future scenario in re-spective software

A comparison was also made between the two software algorithms regarding the samescenario. In Table 28 the boarding volumes for every transit line is showed. Six lineswith the most significant differences have been summarized in Table 60.

Table 60: Boarding difference between Emme and Visum in respective scenario

Line Base scenario Future scenario

55t 660 522401r 751 10422t 89 1 071

New metro line, Blue r 1 313New metro line, Blue t 1 147

Green t 276 779

The bus line 55t (Hjorthagen to Sofia) have more passengers in Visum (+660) in thebase scenario. Also in the future scenario Visum have more travellers on the bus line,522 more passengers. It is generally the same regarding line 401r where there are 751more passengers choosing that line in Visum compared to Emme in the base scenario.In the future scenario the difference is just 104 more travellers in Visum.

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6 ANALYSIS

Before opening the metro the light rail 22t, between Solna centrum and Sickla udde,have 172 passengers in Emme and 83 in Visum so the difference is in total 89 travellers.After opening the metro the total difference is 1 071.

Regarding the new blue metro line there are no passengers in the base scenario. Inthe future scenario there are 3 322 who uses the new metro in the south direction inEmme and 2 009 people in Visum. The line in opposite direction has 4 894 passengerin Emme and 3 747 in Visum. So in general there are more travellers choosing the newblue metro line in Emme.

On the green line, in the north direction, there are 276 more passengers in Visumcompared to Emme in the base scenario. In the future scenario Visum still have morepassengers, 779 more than Emme.

In Figure 21 the boarding differences from Table 60 can be seen in a diagram.

Figure 21: Diagram over boarding difference between Emme and Visum in respectivescenario

6.2 Software sensitivity

When performing the previously mentioned analyses the results will partly show howsensitive the software assignments are. In the following three sections these analysisresults will be compared between Emme and Visum.

6.2.1 Comparison of the parameter analysis

The following section visualises the results from Section 5.2.1 with graphs from bothEmme and Visum with respect to the different assignment parameters and outputvariables.

The boarding time affects the total travel volume in both Emme and Visum and howmuch the result differs when changing the time is very similar in both software pro-grams, see Figure 22. The total volume increases a bit more in Emme when the

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boarding time is 4.5, about 55 more will travel with public transport. When the timeinstead is 5.5 the volume will decrease more in Visum, 145 less people than in Emme.The number of transfers will be affected more in Visum. When the boarding time is4.5 there will be about 800 more transits in Visum and when the value is 5.5 the resultis about 120 transfers less than in Emme.

(a) Transit volume as a function of the board-ing time weight

(b) Number of boardings as a function of theboarding time weight

(c) Number of transfers as a function of theboarding time weight

(d) Average number of transfers per passengeras a function of the boarding time weight

(e) Passenger kilometres as a function of theboarding time weight

(f) Auxiliary transit volume as a function ofthe boarding time weight

Figure 22: Graphs comparing the result from the parameter analysis of boarding timeweight in each software

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6 ANALYSIS

Regarding the waiting time factor Emme have a lower public transport volume andnumber of transfers when the factor is very low, 0.01. When the factor is 0.4 there isless difference between the two programs, but when it comes to the number of transitsit decreases in Emme than in Visum. When the factor is 0.6 the results are similarin both software products regarding the total transit volume, however regarding thenumber of transfers it will increase with 1 855 more in Emme than in Visum. Allgraphs can be seen in Figure 23.

(a) Transit volume as a function of the waittime factor

(b) Number of boardings as a function of thewait time factor

(c) Number of transfers as a function of thewait time factor

(d) Average number of transfers per passengeras a function of the wait time factor

(e) Passenger kilometres as a function of thewait time factor

(f) Auxiliary transit volume as a function ofthe wait time factor

Figure 23: Graphs comparing the result from the parameter analysis of wait time factorin each software

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When it comes to the wait time weight (factor for OWT and TWT) both softwareprograms are about equally sensitive to value changes. The most relevant simulationresults are shown in Figure 24. The most significant difference occurs when the pa-rameter value is 2 (0.5 higher than originally), where Visum obtains a decrease of 2330 persons that does not choose public transport in comparison to Emme where thedifference from the original result is around 1 650.

(a) Transit volume as a function of the waittime weight

(b) Number of boardings as a function of thewait time weight

(c) Number of transfers as a function of thewait time weight

(d) Average number of transfers per passengeras a function of the wait time weight

(e) Passenger kilometres as a function of thewait time weight

(f) Auxiliary transit volume as a function ofthe wait time weight

Figure 24: Graphs comparing the result from the parameter analysis of wait timeweight in each software

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6 ANALYSIS

Both Emme and Visum receives decreased transit volumes and number of transfers withlower auxiliary weights, as can be seen in Figure 25, this depends on that the travellersdoes not have as high resistance towards walking compared to when the original settingswere used. When the weight is set to 2.1 there are much more transfers in Visum thanin Emme, both compared to each other and compared to the original result. In Emmethe transfers only increase with 400 compared to the original and in Visum there areabout 1 640 more transfers. Figure 25e shows that the passenger kilometres in Visumincreases faster than in Emme when the auxiliary time weight is between one andtwo. That is, in Visum passengers are more likely to use public transport. However,the most frequently used parameter value for the auxiliary time weight is 2, so thedifference between the programs will not be significant.

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(a) Transit volume as a function of the auxil-iary time weight

(b) Number of boardings as a function of theauxiliary time weight

(c) Number of transfers as a function of theauxiliary time weight

(d) Average number of transfers per passengeras a function of the auxiliary time weight

(e) Passenger kilometres as a function of theauxiliary time weight

(f) Auxiliary transit volume as a function ofthe auxiliary time weight

Figure 25: Graphs comparing the result from the parameter analysis of auxiliary timeweight in each software

87

6 ANALYSIS

In Visum there are an extra penalty for the number of transfers made, which does notexist in Emme. With this parameter one can adjust the number of transfers, boardings,and passenger kilometres. The total amount of transit volume and auxiliary volumewill not be significantly affected by adding a transfer penalty as can be seen in thegraphs below, Figure 26. Note that the Emme value is fixed in all graphs and is shownas a straight line in order to compare it with Visum.

(a) Transit volume as a function of NTR (num-ber of transfers)

(b) Number of boardings as a function of NTR(number of transfers)

(c) Number of transfers as a function of NTR(number of transfers)

(d) Average number of transfers per passengeras a function of NTR (number of transfers)

(e) Passenger kilometres as a function of NTR(number of transfers)

(f) Auxiliary transit volume as a function ofNTR (number of transfers)

Figure 26: Graphs comparing the result from the parameter analysis of n.o. transferpenalty in each software

The results conclude that all examined output values will be equal if NTR is increasedto a value between 1 and 2.

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6.2.2 Comparison of the line run time analysis

The run times in Table 43 are similar for both Emme and Visum, and the smalldifferences can depend on rounding of times in the programs. The total number ofboardings, in Table 44, are still higher in Visum and that does not occur due to thedifferences of the travel times at the specific transit lines. This can be seen in Table 45which states that the change of run times in Visum does not result in any significantdifferences. The conclusion of this model test is that Visum have more boardings thanEmme and changing the run times only have a small effect on the boardings on eachtransit line. This also implies that the strategy of how to choose a route is calculatedin a different way in Visum compared to Emme.

6.2.3 Comparison of the algorithm analysis with 100 demand

The mean in-vehicle time indicates if people in general are spending more time ridingvehicles due to longer route choices in aspect of time or length. If more people walkthe distance between origin and destination the mean in-vehicle time will be less thanif more used the existing transit lines. Table 46 shows the mean in-vehicle timesstated for Emme and Visum and below in Table 61 the absolute difference is shown inminutes. As stated in the table there are only small differences between the softwareprograms.

Table 61: Absolute difference between the mean in-vehicle time from the analysis with100 demand

Area (from) Area (to) Absolute difference

T-centralen Gullmarsplan 0.02Saltsöbaden Gullmarsplan 0

Värmdö Slussen 0.05Kvarholmen T-centalen (north) 0.05

Nacka Forum Slussen 0.02

The OD-matrix generates 100 trips from an origin node to a specific end destination.How the 100 passengers travel from these defined nodes are shown in Table 47. Thevolumes will split up exactly the same in both Emme and Visum and that is whythe mean in-vehicle times are almost the same. The difference be caused by that therounding of time can differ between Visum and Emme. Although, one test led to adifference in the number of boardings. This was the test with 100 demand betweenGullmarsplan and the eastern part of Nacka, see Figure 19. In general there are moretotal boardings in Visum, which indicates that those passengers transfer more comparedto the passengers in Emme. As showed in the figure Visum travellers use 11 differenttransit lines compared to Emme, where 8 transit lines are used. This fact, along withthe results from the full matrix, indicates that Visum tend to split the flows on routescontaining more transfers than Emme does.

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6 ANALYSIS

6.3 Comparison of the node analysis

When the five nodes are analysed in the base scenario there are some differences be-tween the software algorithms. The largest differences could be seen at T-centralen.The number of passengers passing through the station is larger in Emme than in Vi-sum. Regarding the total number of alighting passengers there are more in Visum thanEmme at T-centralen, about 3 500 more and that mostly depends on that there aremuch more passengers that alights in order to transfer in Visum. A complete compar-ison between the two scenarios and software results is displayed in Figure 27.

Figure 27: Comparisons between the base and future scenario in Emme and Visum atT-centralen with respect to the number of passengers boarding, alighting or passingthrough the station.

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At Slussen there are more alighting passengers in Visum but less boardings in compar-ison to Emme. The station at Nacka Forum will have some more passengers in totalexcept regarding the ones alighting to reach their final destination. The biggest differ-ence is that there are more through passing passengers in Emme (+750) and more whoboards after transferring (+143). A complete comparison between the two scenariosand software results is displayed in Figure 28.

Figure 28: Comparisons between the base and future scenario in Emme and Visumat Slussen with respect to the number of passengers boarding, alighting or passingthrough the station.

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6 ANALYSIS

Regarding the future scenario there are more differences concerning all the stations. InSofia there are more through passing in Emme, almost 1 600 more than in Visum. InT-centralen the number of final alighting is larger in Visum (+1 379) and the numberalighting to do a transfer is higher in Emme (+603). A complete comparison betweenthe two scenarios and software results is displayed in Figure 29. The base scenario isnot represented in this diagram since Sofia station does not exist before building themetro, i.e. in the base scenario.

Figure 29: Comparisons between the base and future scenario in Emme and Visum atSofia with respect to the number of passengers boarding, alighting or passing throughthe station.

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In Kungsträdgården there are more passengers generally in Emme and the largest dif-ference is the number that does their final alighting, 878 more compared to Visum.Also the volume that passes the station on a vehicle is larger, about 670 more pas-sengers. In Slussen the volumes are overall higher in Visum and mostly because ofmore transfer boarding (+660) and transfer alighting (+598) in comparison to Emme.A complete comparison between the two scenarios and software results is displayed inFigure 30.

Figure 30: Comparisons between the base and future scenario in Emme and Visumat Kungsträdgården with respect to the number of passengers boarding, alighting orpassing through the station.

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6 ANALYSIS

Nacka Forum have a greater number of transfer boarding in Emme (+1 161) but thenumber of through passing passengers is higher in Visum (+97). A complete compar-ison between the two scenarios and software results is displayed in Figure 31.

Figure 31: Comparisons between the base and future scenario in Emme and Visum atNacka Forum (bus stop) with respect to the number of passengers boarding, alightingor passing through the station.

These results might be the most important with respect to the case study, since themetro end-station will be located at Nacka Forum. In the figure one can see that moretravellers will chose the metro in Emme, while travellers go by highway buses in Visuminstead. In this specific case the results show that an investment in a new metro willbe of greater use in Emme than in Visum.

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A comparison between the Emme and Visum results at each node regarding the basescenario can be seen in Figure 32.

Figure 32: Node analysis made in the base scenario

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6 ANALYSIS

Figure 33 shows a comparison between the Emme and Visum results at respective nodefrom the future scenario.

Figure 33: Node analysis made in the future scenario

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6.4 Comparison of the algorithms for public transport assignment

6.4 Comparison of the algorithms for public transport assign-

ment

When analysing the results from both algorithm assignment it follows that there areno major difference between the software sensitivity. That is, both algorithms work ina similar way. Although there are some significant differences in the calculation stepsof the attractive lines. The computational differences are shown in Section 3.5.4 andthey lead to that Visum algorithm obtains more routes containing transfers comparedto Emme optimal strategy. Another effect from the Visum algorithm is that it usesthe maximum wait time in the second attractiveness test is that more transit lineswill be determined as attractive. One could say that Emme optimal strategy is morerestrictive regarding the definition of an attractive line. This can however be adjustedby increasing the spread factor, which leads to more attractive lines.

There are, as previously mentioned, several other algorithms available and all typeshave additional settings for discrete choice model (logit model) regarding the demandshare over stop locations and if the passengers will stay on-board or alight. If thesesettings are not used the passengers will behave according to the optimal strategy inEmme and use the connectors that cost the least in time. In the cases where thelogit model options are used, all passengers cannot choose the shortest connector andinstead they are split between the shortest connectors with a percentage set by theuser. The amount of passengers that will use the next best connector will be assignedaccording to the additional percentage. Since the most simple transit assignment inEmme does not use a logit split between connectors this option was not used in Visumeither. However, to obtain a more realistic model one should use these settings inVisum or Emme 4 (extended transit assignment).

The conclusions stated by Johansson in [5] coincides with this thesis result regardingthat the passengers transfers more obtained with Visum algorithm and there are morelines used for the stated parameter values. This depends on that the Visum algorithmallows more lines to become attractive. In the second attractiveness test the algorithmselects the lines with lower total travel time compared to the combined faster lines ifthe passengers have to wait the maximum time for any of those lines to arrive.

The master thesis conducted by Hägerwall Stein, described in [12], presents a list foreach software with different positive and negative characteristics. The conclusions werethat none of the software products could be determined as the best and that the auto-assignment algorithm with equilibrium settings gave similar results. The only differencebetween the network equilibrium results was how the OD-matrix values was rounded.This is also the case for transit assignment, however the other algorithm differencesgenerate a more significant dissimilarity according to this thesis results.

6.4.1 Extended transit assignment

As mentioned in the previous section no logit split is used in the Emme standard transitassignment. However it is implemented in the latest version of Emme where extendedtransit assignment complements the basic standard assignment. The other settingsthat could be used in the extended assignment will be analysed in this section.

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6 ANALYSIS

There should be the same results in Emme 4 with standard assignments as in Emme4 with extended assignment and optimal strategy settings. However, Table 52 shows adifference. This could be explained by that the extended transit assignment calculatesmore exact and therefore less boardings were obtained for the extended version of theoptimal strategy. The standard assignment in Emme 4 gives exactly the same numberof boardings as in Emme 3, which proves that the same strategy algorithm is used (canalso be confirmed in the manual of Emme 4, [26]).

When using the scale distribution at origins the higher scale value gives more demandon the shortest connector, the highest value will result in the optimal strategy (i.e.all demand on the best connector). When the scale is set to zero all the connectorsfrom the origins will have the same demand and in this case it will result in largernumber of total boarding in the system, see Table 53. When the scale is set to 1 theboardings will be more similar to the result from using the optimal strategy. To obtaina more realistic model one should calibrate the scale value so that the results resemblesa measured traffic behaviour for that specific situation.

Table 54 shows the results from varying the scale factor for flow distribution withauxiliary transit choices. This implies that less travellers board transit lines when thescale factor is lower, i.e. the flow is distributed equally on all available auxiliary transitchoices as well as transit lines. With the scale set to 1 the system behaves like theoptimal strategy, this means that the passengers choose only the best option and thatleads to more boardings on transit lines. A realistic scale factor should be somewherebetween the highest and the lowest value.

In Table 55 the results from using flow distribution based on frequency and the distri-bution based on both frequency and transit time to destination are shown. When usingthe latter option, faster lines with lower frequency will be more attractive compared toif only the frequency is taken into account. When the scale is constant the distributionbased on both frequency and transit time gives a lower amount of total boardings (106529) compared to only frequency based (108 228). However, when studying the totalpassenger kilometres the difference is only 1 341 kilometres. This means that in generalevery passenger travels 2.85 kilometres compared to 2.88 kilometre.

An important result is that the frequency and transit time assignment generate similarnumber of boardings and passenger kilometres as in Visum (base scenario). The numberof boardings in Visum is 106 482 and 106 529 in Emme 4 with the last mentionedassignment settings. Passenger kilometres in Visum is 307 481 and 306 627 in Emme4.

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7Conclusions and future work

This chapter will conclude the comparisons between the algorithms in respective soft-ware. The objective to be answered was What are the differences with modelling apublic transport network in Emme and in Visum, based on that the passengers onlyhave information about the travel times and the line frequency, and why does the dif-ferences occur? The following list answers the main objective in this master thesis aswell as states some important conclusions.

• The differences are:

– That the public transport algorithms take different parameters in more orless consideration

∗ Visum first focuses on shortest total travel time and then considersother lines with respect to maximum waiting time → lines with longheadways and or travel times are excluded

∗ Emme first focuses on shortest travel time and then considers the totaltravel time for other lines with half the waiting time → less routes aredetermined to be attractive than in Visum

– Rounds off the values in the origin/destination matrices

– Standard parameter settings differs between software (at least in Sweden)

• It is important to be aware of the differences in order to conduct a more correctsimulation and knowing how to interpret the results

• However, it is more important for the results to choose the right parameter valuesthan the right software

The algorithms generate differences in the route choices, i.e.: transit line volumes, thenumber of boardings, total transit/auxiliary volume, and passenger kilometres. In thecurrent situation, there is no scientific explanation as to why they have chosen theparameters contained in Trafikverket’s models. The same applies to the parametersof Visum, which seems to have been chosen based on Emme parameters or travellerbehaviour surveys. The difference between those parameter values is that Visum has awaiting factor equal to 1 and waiting weights equal to 2 and only a penalty for the num-ber of transfers made instead of boarding penalty as in Emme optimal strategy.

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7 CONCLUSIONS AND FUTURE WORK

The examples mentioned in Chapter 3 and the analysis with 100 demand stated inChapter 5 shows that the software results sometimes might be the same. This coulddepend on the network construction or that differences propagate with larger amountof transit lines and assigned demand. It could also depend on that the line times areprioritized differently, according to the results seen in brief in Table 62 below.

Table 62: A concluding comparison between the algorithms

Emme Visum

Emme takes the shortest line travel time into account. Visum takes the shortest total travel time compared tothe faster lines combined maximum travel time into account.

This represents a situation where the travellershave to wait the maximum waiting time.

Emme compares the examined lines travel time Visum compares the examined lines travel timewith the faster (in terms of which has the shortest with the faster (in terms of which has the shortest

travel time) lines combined total travel time. total travel time) lines combined total travel time.Thus, if one were to board the examined line Thus, if one were to board the examined lineinstead of waiting for one of the faster lines. instead of waiting for one of the faster lines.

Emme includes the wait time in the flow Visum includes the wait time in both the flowdistribution and in the second attractivity test. distribution, the first and second attractivity test.

Emme can exclude lines that have a little Visum can exclude lines with a long headway, and if thelonger travel time than another line. headway is reduced very little this line may become attractive.

This might exclude those lines that depart more frequently This is not very realistic and might excludeand logically some travellers should choose that line. faster lines with less frequent departures.

To summarize the conclusions with respect to the studied project, Emme optimalstrategy might overestimate the new metro lines effect or perhaps Visum algorithmwill underestimate it. Either way it is interesting to see that there is such a significantdifference between the two assignment algorithms.

An alternative algorithm that is available in Emme 4 was mentioned and studied pre-viously in this thesis, called extended transit assignment. In this extended version ofthe standard transit assignment one can add extra costs for boarding, in-vehicle time,and auxiliary time. It also contains a flow distribution to and from origin/destinationnodes, so called logit distribution. The percentage that chooses a particular routecould be based on line frequency (headway) or a combination of the frequency andtravel time. Regarding the first option the travellers are unaware of the line departuretime and with the second option the travellers have knowledge about the timetable,which is more realistic. This could be managed in Visum as well and as stated previ-ously in the report the differences between the examined results does mostly dependon the parameter settings. For example the auxiliary time weight and wait time factoraffects the results more than the wait time weight and boarding penalty. One canalso adjust the extra penalty for transfers in Visum in order to recreate the resultsfrom Emme optimal strategy. Another option is to change the spread factor in Emme,since increased waiting times can reproduce the results from Visum as well. There-fore the choice between which software to use is mainly based on choice of preferenceand the knowledge of how the software algorithms work along with correct parameteradjustments are more important.

In future important infrastructure projects we suggest that both "extended transitassignment" in Emme and "headway based assignment" in Visum is used. These arefrequency based assignments with some similar functionalities. Further work on thistopic could also be to calibrate the assignment parameters for both Emme and Visum,at least for one or a few projects so that it could be determined if the parameter valuesin each software are realistic in the examined traffic system. One might also continue

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the comparison with the other assignment algorithms available and determine the mostrealistic assignment for different transit systems. Our recommendation is to examinethe significance of assignment parameters even further and to decide suiting values forSweden today. We also suggest that traffic analysts focus more on calibration of theseparameters in the beginning of the calibration process.

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//www.dot.ca.gov/hq/tsip/otfa/microsim/whatis_modeling.html, Septem-ber 2012.

[11] Gunnar Lind. Strategisk utvärdering av intelligenta transportsystem. Report fromKungliga Tekniska Hgskolan, Trafik- och transportplanering, Stockholm, 1998.

[12] Carl Hägerwall Stein. Prognosverktyg för trafikflöden - en jämförelse av emme/2och visum. Master’s thesis, Master thesis from Lunds Tekniska Högskola, LundsUniversitet, 2007.

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Appendix

Table 63: Difference in the number of boardings between base and future scenario inVisum

Absolute boarding difference Relative boarding differenceLine between base and future scenario between base and future scenario

43r 4 0.0043t 241 0.0353r 826 0.1553t 1 503 0.2655r 323 0.1555t 204 0.06

59ra 45 0.1359rb 1 0.0159ta 10 0.2159tb 0 0.0066r 7 0.0266t 77 0.2974r 1 206 0.3074t 2 789 0.3276r 221 0.08

401r 841 0.53401t 27 0.02402r 20 0.02402t 162 0.14403r 314 0.53403t 3 0.01409r 638 0.88410r 0 0.00410t 760 0.86411t

411ra411rb413r 394 0.44413t414r417r417t420t 122 0.22422r 417 0.49425t 163 0.11430r 96 0.33

430ta 43 1.00430tb431t 419 0.05434r 96 0.33435r 107 0.33442t443r 88 1.00443t 380 0.47444r 255 0.94445t448t 32 0.34449t 36 0.15471r 96 0.18471t 229 0.25474r 464 0.30474t821r 87 0.29821t 56 0.33840r 141 0.47840t 227 0.4422r 678 0.5222t 1 106 0.8725r 20 0.0625t 115 0.0826r 37 0.0626t 162 0.09

Blue r 302 1.00Blue t 475 0.95

New metro line, Blue rNew metro line, Blue t

Red r 262 0.01Red t 2 010 0.07

Green r 623 0.02Green t 1 760 0.04

All lines 21 720 0.10

Table 64: Difference in the number of boardings between base and future scenario inEmme

Absolute boarding difference Relative boarding differenceLine between base and future scenario between base and future scenario

43r 6 0.0043t 230 0.02

53ra 882 0.1853ta 1 495 0.2255r 680 0.25

55ta 66 0.0359ra 59 0.2259rb 0 0.0059ta 11 0.3359tb 0 0.0066r 14 0.1066t 48 0.25

74ra 1 489 0.4574ta 3 115 0.3976ra 422 0.18

401ra 194 0.27401ta 119 0.13402rb 20 0.02402tb 47 0.04403ra 73 0.27403ta 19 0.13409r 612 0.82410r 0 0.00410t 646 0.78411t

411ra411rc413ra 139 0.09413ta414rb417r417t

420tb 212 0.43422ra 374 0.30425Xt 45 0.03430ra 30 0.58430tF 0 0.00430ta431t 673 0.09

434ra 30 0.58435r 35 0.64

442ta443r 78 1.00443t 159 1.00444r 40 0.13

445ta448t 57 0.61449t 30 0.12471r 45 0.04471t 436 0.42474r 176 0.65474t821r 133 0.61821t 10 0.08840r 39 0.25840t 14 0.0422ra 833 0.5622ta 2 088 0.8625ra 4 0.0125ta 236 0.1426ra 0 0.0026ta 156 0.09

Blue r 175 0.34Blue t 472 0.95

New metro line, Blue rNew metro line, Blue t

Red r 226 0.01Red t 2 205 0.07

Green r 436 0.02Green t 2 263 0.05

All lines 22 096 0.11

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