Department of Science and Technology Institutionen för teknik och naturvetenskap
Linköping University Linköpings universitet
A comparative study between
Emme and Visum with respect to
public transport assignment
Cisilia Hildebrand
Stina Hörtin
A comparative study between
Emme and Visum with respect to
public transport assignment
Examensarbete utfört i Transportsystem
vid Tekniska högskolan vid
Linköpings universitet
Cisilia Hildebrand
Stina Hörtin
Handledare Ellen Grumert
Examinator Anders Peterson
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http://www.ep.liu.se/A comparative study between Emme and Visum
with respect to public transport assignment
Master Thesis carried out at Division of Communications- and Transport Systems Linköpings University
November 2014
Cisilia Hildebrand
Stina Hörtin
The work presented in this thesis has been carried out in the Division of Communications-and Transport Systems at Linköpings University Communications-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 also like 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 this thesis. Finally, we want to thank our families and friends for their support during the years.
Cisilia Hildebrand and Stina Hörtin Norrköping, November 2014
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 traffic planning. When studying a large traffic network macroscopic traffic simulation can be used to model current and future traffic situations. The two most common software used for traffic simulation in Sweden today are Emme and Visum, developed by INRO respective PTV.
The aim of the thesis is to perform a comparison between the software Emme and Visum with respect to the assignment of public transport, in other words how passengers choose their routes on the existing public transport lines. However, in order to make a complete software comparison the run-time, analysis capabilities, multi-modality, capacity to model various behavioural phenomena like crowding, fares etc. this will not be done in this comparison. It is of interest to study the differences between the two software algorithms and why they might occur because the Swedish Transport Administration uses Emme and the Traffic Administration in Stockholm uses Visum when planning public transport. The comparison will include the resulting volumes on transit lines, travel times, flow through specific nodes, number of boarding, auxiliary volumes and number of transits. The goal of this work is to answer the following objective: What are the differences with modelling a public transport network in Emme
and in Visum, based on that the passengers only have information about the travel times and 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 Kungsträdgården have been used. The motivation for choosing this area and case is due to that it is interesting to see what differences could occur between the programs when there are a major change in the traffic network.
The network of Nacka, and parts of Stockholm City, has been developed from an existing road network of Sweden and then restricted by "cutting out" the area of interest and 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 portal zones was used to collect and retain volumes.
To find out why the differences occur the headway-based algorithms in each software were studied carefully. An example of a small and simple network (consisting of only a
The limited network of Nacka shows how the different software may produce different results in a larger public transport network.
The results show that there are differences between the program algorithms but the significance varies depending on which output is being studied and the size of the network. The Visum algorithm results in more total boardings, i.e. more passengers have an optimal strategy including a transit. The algorithms are very similar in both software programs, since they include more or less parts of the optimal strategy. The parameters used are taken more or less into consideration in Emme and Visum. For example Visum will first of all focus on the shortest total travel time and then consider the other lines with respect to the maximum waiting time. Emme however, first focuses on the shortest travel time and then considers the total travel time for other lines with half the waiting time instead of the maximum wait time. This results in that less transit lines will be attractive in Emme compared to Visum. The thesis concludes that varying the parameters for public transport in each software algorithm one can obtain similar results, which implies that it is most important to choose the best parameter values and not to choose the "best" software when simulating a traffic network.
Keywords: assignment, Emme, macroscopic traffic simulation, public transport,
Preface i
Abstract iii
List of figures vii
List of tables ix 1 Introduction 1 1.1 Aim . . . 2 1.2 Limitations . . . 2 1.3 Method . . . 3 1.4 Paper outline . . . 4 1.5 Research contributions . . . 5 2 Travel forecasting 7 2.1 The four step travel forecasting model . . . 8
2.1.1 Trip generation . . . 9
2.1.2 Trip distribution . . . 9
2.1.3 Mode choice . . . 9
2.1.4 Route assignment . . . 10
2.2 Public transport assignment . . . 11
3 Software products 13 3.1 Overview of macroscopic software products . . . 13
3.2 Emme 3 . . . 15
3.2.1 Public transport assignment . . . 15
3.2.2 Algorithm . . . 17
3.3 Emme 4 . . . 19
3.4 Visum 13 . . . 21
3.4.1 Public transport assignment . . . 22
3.4.2 Algorithm . . . 24
3.5 Assignment parameters and examples . . . 28
3.5.1 Assignment parameter settings in Emme and Visum . . . 28
3.5.5 Comparison between literature examples . . . 37
4 A case study 41 4.1 Model building in Emme . . . 45
4.1.1 Verification . . . 47
4.2 Model building in Visum . . . 48
4.2.1 Verification . . . 49
4.3 Model analysis . . . 53
4.3.1 Parameter analysis . . . 53
4.3.2 Line run time analysis . . . 53
4.3.3 Algorithm analysis with 100 demand . . . 54
4.3.4 Node analysis . . . 55
4.4 Emme 4, extended transit assignment . . . 56
5 Results 57 5.1 Simulation results . . . 57
5.1.1 Emme 3 and Visum: base scenario . . . 57
5.1.2 Emme 3 and Visum: future scenario . . . 60
5.2 Model analysis results . . . 63
5.2.1 Results from parameter analysis . . . 63
5.2.2 Results from line run time analysis . . . 69
5.2.3 Results from algorithm analysis with 100 demand . . . 72
5.2.4 Results from node analysis . . . 73
5.3 Emme 4, extended transit assignment, results . . . 76
6 Analysis 79 6.1 Comparison of the simulation results . . . 79
6.2 Software sensitivity . . . 82
6.2.1 Comparison of the parameter analysis . . . 82
6.2.2 Comparison of the line run time analysis . . . 89
6.2.3 Comparison of the algorithm analysis with 100 demand . . . 89
6.3 Comparison of the node analysis . . . 90
6.4 Comparison of the algorithms for public transport assignment . . . 97
6.4.1 Extended transit assignment . . . 97
7 Conclusions and future work 99
References 103
1 Illustration of the four step travel model and what is included regarding
the decision in each step . . . 9
2 The small network in Emme (not to scale) . . . 30
3 Graphic result from transit assignment of the small network in Emme . 32 4 The small network in Visum (not to scale) . . . 33
5 Graphic result from transit assignment of the small network in Visum . 35 6 Comparison between the reproduced example results in both Emme and Visum . . . 38
7 The network limitation area of Nacka municipality, from Google maps . 41 8 The chosen alternative for Nacka metro . . . 42
9 The network built in Emme . . . 46
10 Transit lines in the base scenario (each line is a separate color) . . . 46
11 Transit lines in the future scenario (each line is a separate color) . . . . 47
12 The network built in Visum . . . 49
13 Areas that the 100 demand will be assigned between . . . 54
14 The circled nodes that will be analysed . . . 55
15 Simulation results from the base scenario in Emme . . . 59
16 Simulation results from the base scenario in Visum . . . 59
17 Simulation results from the future scenario in Emme . . . 62
18 Simulation results from the future scenario in Visum . . . 62
19 The number of boardings . . . 73
20 Diagram over boarding difference between base and future scenario in respective software . . . 81
21 Diagram over boarding difference between Emme and Visum in respec-tive scenario . . . 82
22 Graphs comparing the result from the parameter analysis of boarding time weight in each software . . . 83
23 Graphs comparing the result from the parameter analysis of wait time factor in each software . . . 84
24 Graphs comparing the result from the parameter analysis of wait time weight in each software . . . 85
25 Graphs comparing the result from the parameter analysis of auxiliary time weight in each software . . . 87
26 Graphs comparing the result from the parameter analysis of n.o. transfer penalty in each software . . . 88
alighting or passing through the station. . . 90 28 Comparisons between the base and future scenario in Emme and Visum
at Slussen with respect to the number of passengers boarding, alighting or passing through the station. . . 91 29 Comparisons between the base and future scenario in Emme and Visum
at Sofia with respect to the number of passengers boarding, alighting or passing through the station. . . 92 30 Comparisons between the base and future scenario in Emme and Visum
at 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 Visum
at Nacka Forum (bus stop) with respect to the number of passengers boarding, alighting or passing through the station. . . 94 32 Node analysis made in the base scenario . . . 95 33 Node analysis made in the future scenario . . . 96
1 Line specific notation description for the algorithm section in Emme . . 17
2 Assignment variables that are generated from the simulation . . . 22
3 Line specific notation description for the algorithm section in Visum . . 24
4 Parameter translation . . . 29
5 Characteristics of the small network in Emme . . . 30
6 Weighted times for each transit line . . . 31
7 Algorithm first three steps for computing the optimal strategies in Emme 32 8 Algorithm last two steps for computing the optimal strategies in Emme 32 9 Characteristics of the small network in Visum . . . 33
10 Translation of assignment parameters from Emme to Visum . . . 33
11 In-vehicle, walk and origin wait times for the different routes . . . 34
12 Boarding, transfer and total wait times for the different routes . . . 34
13 First step when computing the optimal strategies in Visum . . . 34
14 Second step when computing the optimal strategies in Visum . . . 35
15 Attractiveness results from Emme . . . 38
16 Attractiveness results from Visum . . . 38
17 Relevant bus lines for the base scenario . . . 43
18 Relevant metro/light rail lines for the base scenario . . . 43
19 Relevant bus lines for the future scenario (the new or changed transit lines are stated in italics) . . . 44
20 Relevant metro/light rail lines for the future scenario (the new or changed transit lines are stated in italics) . . . 44
21 Line run times from Emme and Visum (minutes) in the base scenario . 51 22 Line run times from Emme and Visum (minutes) in the future scenario 52 23 Output from the base scenario simulation in Emme and Visum . . . 57
24 Total number of passengers boarding on each type of transit mode in respective software, base scenario . . . 58
25 Number of passengers boarding on each line in respective software, base scenario . . . 58
26 Number of passengers boarding on each line in respective software, fu-ture scenario . . . 60
27 Total number of passengers boarding on each type of transit mode in respective software, future scenario . . . 60
28 Number of passengers boarding on each line in respective software, fu-ture scenario . . . 61
31 The difference between Visum and Emme with respect to the difference
between 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 wait time in Visum . . . 67
40 Parameter analysis results with factor for origin and transfer wait time in Visum . . . 67
41 Parameter analysis results with factor for access, egress and walk time in Visum . . . 67
42 Parameter analysis results with factor for number of transfers (NTR) in Visum . . . 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 run times 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 and extended 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 with auxiliary transit choices . . . 76
55 The result when using the additional setting to use flow distribution between 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 Trafikverket parameter values and from Visum with the Trafikförvaltning parameter values . . . 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 analysis with 100 demand . . . 89
64 Difference in the number of boardings between base and future scenario in Emme . . . 107
1
Introduction
When studying a large traffic network macroscopic traffic simulation (with aggregated traffic 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 national traffic planning authorities and companies. An advantage with this type of simula-tion is that one can analyse and investigate a larger traffic network without investing in expensive infrastructure first. To obtain realistic results, the model must reflect the reality accurate enough. This is done by calibrating the models, i.e. adjusting model parameters until the results resemble the observed or estimated data. Several macroscopic traffic simulation tools have been developed, with various advantages and disadvantages. 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 software products are Emme and Visum. The Swedish Transport Administration (Trafikverket) and several municipalities use Emme. Visum is the main macro simulation software at the Traffic Administration in Stockholm (Trafikförvaltningen) along with other traffic planning 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 to compare.
This project will provide knowledge of how the macroscopic traffic simulation software Emme and Visum differ regarding traffic assignment in terms of public transport. In order to compare these software algorithms, the existing traffic network of Nacka (a Stockholm municipality) is studied. There are plans for an expansion of the existing metro in Stockholm. This is an infrastructure investment by the Swedish government, Trafikverket, Trafikförvaltningen, Stockholm County Council, Stockholm and Nacka municipalities with on-going preliminary studies and is therefore of interest to study further how it will affect the public transport system. This thesis will use the extension of the metro as an example of a project often used within traffic planning. With the help of macroscopic traffic simulation one can investigate how the metro will affect other parts of the traffic network. The models in Emme and Visum need to be verified and then the infrastructure project, to build a metro to Nacka, added to the modelled networks. The results for both the scenario with and without metro will be compared between the software products in terms of assignment of public transport. Input data needed for both scenarios will be collected, in collaboration with WSP Analysis & Strategy and Trafikverket, from their earlier traffic prediction studies in Sweden.
1.1
Aim
The aim of this master thesis is to perform a comparison between the software products Emme and Visum with respect to the assignment of public transport. Headway based assignment in these programs will be used, which means that the travellers will only have information about the travel times and the line frequency.
The traffic network that will be the study area is Nacka municipality and the new stretch 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 traffic network 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 forecast from Trafikverket. The goal is to provide a greater understanding of how these two macroscopic traffic simulation software products performs and what their differences are 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 in Visum, 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 future scenario are required to be as similar as possible, which means that the networks in Emme and Visum could be in need of adjustment with respect to pathways, metro and bus etc. In order to evaluate the software products sensitivity regarding the public transport 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 available from previous projects in the Stockholm area. There is a ready-made model in Emme from traffic prognoses and this will be imported to Visum in order for the models to be as similar as possible. In the two future scenarios the metro will be added to the base scenarios 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 become too extensive. Therefore the area will be limited to Nacka municipality and the area along the route of the proposed metro. This means that the OD-matrices that are available 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 network may or may not extend to other networks, therefore other examples of smaller networks helps 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 make the project too extensive. The specific assignment procedure that will be analysed in this thesis is called headway-based, which is suited for public transport areas with high frequency transit lines. The headway can be explained as the time between two vehicles of the same transit line serving a node. This type of assignment procedure requires only a few types of input data, i.e. line frequencies and travel times. Since
the analysis regards the future traffic situation there are only frequencies and travel times 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 of lines.
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 models produce realistic results. Due to this, the future scenario results will not show how the metro actually affects travelling in Stockholm and Nacka. They will at best show an elasticity measurement of the movements from bus to metro etc. However, an impor-tant analysis will be the differences in public transport assignment between the software models with and without the new metro, which can be done without calibration of the models.
There is no known scientific basis for these parameters collected from Trafikverket (Emme) or Trafikförvaltningen (Visum). Due to this one cannot draw any conclusions regarding what parameters that are best at representing the reality, since the models used in this thesis are based on future prognoses and cannot be validated. Further studies 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, fares etc. 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 model parameters such as weights for waiting and boarding time, and by adding the new metro line a more thorough comparison can be made. The future scenario models will be used for further comparison between the software products and only to some extent used for evaluation of the distribution of public transport passengers.
An evaluation of the extended transit assignment in Emme 4 will also be performed in order to determine if there are any significant differences between using standard or extended transit assignment. It is also interesting to see if the difference between the extended assignment and the assignment in Visum.
In order to perform a comparison between the two software algorithms, a traffic network with the same conditions is created. To ensure that both the road network and public transport routes are consistent in both Emme and Visum, a network is developed in Emme and then imported to Visum. See Chapter 4 for a more detailed description of the adjustment of the road network, travel matrix and transit lines. In this thesis the most interesting outputs are in-vehicle time (how long time the passenger spends in transit vehicles), origin wait time (how long time the passenger waits at the origin transit stop), transfer wait time (how long time the passenger waits at transfer stops),
total transit volume, total number of transfers (transfer volume), transit volume on different lines, the number of passengers that walks the whole way from origin to destination, and average number of transfers per passenger. These outputs will be used in the comparison between the software algorithms.
To compare the software algorithms, theory regarding macroscopic traffic simulation and the theoretical models in each program will be studied. The manuals that describe and explain the underlying mathematical methods in both software algorithms have been a key part of the comparison. As a complement to the manuals, scientific articles with relevant content have also been used to gain a broader and deeper understanding of 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-Jürgen Don from Visum’s Traffic Customer service. This is done to make sure that nothing important will be overlooked or misinterpreted.
1.4
Paper outline
The report begins with a literature review regarding the travel forecasting, presented in Section 2. An overview of travel forecasting and how it can be used for predicting changes in a traffic network is given. Focus will be on the different simulation methods that can be used as a part of the prediction. It contains a general description of the different simulation approaches and a more detailed description of the advantages and disadvantages of macroscopic simulation.
The four step travel model will be described in Section 2.1, which is a commonly used method for a prediction. The method includes both the estimation and calculation of trips and usage of simulation software which give the travellers itinerary. The four steps 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 simulation software 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 and Visum 13 regarding the public transport assignment with corresponding parameters and algorithms. There is an explanation of the mathematical foundation regarding the assignment for both software programs. It also contains an example of how the optimal strategy is obtained for a small transit network. Some differences and similarities will also be described regarding the transit assignments.
A description of the study area is presented in Chapter 4, and contains the transit line network which is the foundation of the case study (both base scenario and future scenario). It includes an explanation of how the network, with associated demand matrix, was developed and verified.
The results of the simulation runs are showed in Section 5. Tables and diagrams will represent the output measurements of importance.
results of the simulations from the previously mentioned chapter. A comparison of the transit assignment algorithms can also be found in this section.
In Chapter 7 the final conclusions are stated and directions for future research, are made within the subject area.
1.5
Research contributions
The result of this thesis might contribute to a deeper understanding of how the two simulation software algorithms calculates the optimal route for each traveller with respect to travel time. Simulations are often a part of a bigger investigation concerning large changes in the transport system. It is of importance that the simulations are analysed and performed in a correct way. The investigation decision, which is partly based on the simulation results, can cost a lot of money and affect a lot of people using the transport system. In many companies and authorities one of the two simulation software is chosen as a standard program. However, why they have chosen that specific software instead of the other is often rather unclear. The conclusion of this thesis will hopefully help to understand how the algorithms work. It is important to make good decisions from the analytical results since almost everyone in the community will be affected by the changes concerning the transport system which might be done based on the simulations.
When calibrating (adjusting model parameters in order to obtain results which matches measured 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 keeping them fixed. This might produce more accurate and realistic simulation results.
2
Travel forecasting
Travel forecasting can be used for predicting changes in a traffic network. A common method for performing travel forecasting is the four step model where a macroscopic simulation software tool is a part of the procedure. A travel or traffic forecast is a prediction of how the traffic will change in the future and are always based on external conditions and factors. To create a model with good accuracy the model primarily need to include socio-economic data and a transportation network. This chapter aims to provide a short description regarding travel forecasting including the four step model and an overview of how the public transport assignment works. For more information about travel forecasting and the four step model see Ortuzar and Willumsen [6], Hydén et.al. [7], WSP [8], National Cooperative Highway Research Program [9], California department of transportation [10], and Lind [11].
The traffic forecasting can partly be carried out using a computer based software with a model of the traffic network. Regarding the transportation network and specific public transport network (transit network) the coding can be complex. There can be a big range of available modes such as local bus, express bus, light rail, commuter rail and bus rapid line. The lines service is often different in peak and off-peak hours during the day. The total flow is studied in a macroscopic simulation and the individual behaviour of the vehicles is not taken in consideration. The road network is of a larger scale and may include traffic network for an entire city or country and the simulation is made section by section. Emme and Visum are macroscopic simulation software used when carrying out a travel forecast. The model that is created in the traffic simulation software is a simplified version and representation of the real world network that is of interest.
The forecasting method can be used for more than prediction of the future, it is also used to investigate how the travel pattern will change when modifying or changing anything in the traffic infrastructure. The model may be useful when analysing the effect of editing the road design, altering the public transport supply or implementing tolls. The result of prediction models can be a part of the decision making process when deciding about changes regarding the traffic system. By forecasting it is possible to compare the effects of alternative actions so the decision maker easier can determine which action or actions will affect the transport system in the desired direction. The most 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 modification has affected the traffic system in terms of more, less or shifted traffic volume. It can
also be the number of trips and vehicle kilometres for different transport modes on different roads and transit lines. It may as well provide an indication of the expected traffic congestion in the area.
The choice of forecasting procedure is a trade-off between the wanted accuracy of the 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 accurate result because that requires a lot of work, for example extensive data collection and precise 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 of simulation approaches: microscopic, mesoscopic and macroscopic simulation. In a mi-croscopic simulation model the individual vehicles and their behaviour are studied. The road network is relatively small and can for example consist of a roundabout. When designing control strategies for different functions (e.g. traffic lights) and analysing actual investment regarding traffic information, the model needs to be complemented by more detailed models. This type of model requires more input data with additional coding and is therefore significantly more resource intensive. Traffic analysts often limits the network to a smaller geographic area, which is more suitable for microsim-ulation. Mesoscopic simulation is between micro- and macro level. The individual vehicles are simulated but the activities and interactions are described in macroscopic relationships. This approach is often used when evaluating traveller information sys-tems. In a macroscopic simulation model the total flow is studied and the individual behaviour of the vehicles is not taken in consideration. The road network is of a larger scale and may include traffic network for an entire city or country and the simulation is made section by section. Emme and Visum are macroscopic simulation software used when carrying out a travel forecast. The model that is created in the traffic simulation software is a simplified version and representation of the real world network that is of interest.
In the next section the four step travel model is described shortly, where the software programs 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 of four distinct steps which are well described in the literature, for example in Ortuzar and Willumsen [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
Figure 1: Illustration of the four step travel model and what is included regarding the decision 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 each purpose type that begin or ends in each zone. The estimation is based on the amount of activity in the zone. The number of trips generated in this step are the flow used in the model. Usually the trips are vehicle and person trips (auto or transit) which often includes both walking and bicycle modes. The results and the outputs from the trip generation model are trip productions and trip attractions for each zone and trip purpose.
2.1.2 Trip distribution
This step calculates the percentage of the total traffic that will travel between each pair of zones. The result can be presented in a matrix where each row and column represents a zone. Each value in a cell in the matrix represents the number of trips between the zones and this is called a travel matrix or OD matrix, where Tij is the
demand from zone i to zone j. Often the travel demand varies over time and different matrices 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 type of transportation analysis that is required. The modes can commonly be divided into three groups: auto-mobile, transit and non-motorized. The choice of mode can depend
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 one of the more basic versions is the so called linear model, see the formula below:
Pj= e∑aixi(j) ∑ ke ∑ 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 and
k is the available transport mode.
In order to obtain the distribution of the number of trips for each travel mode Tijk,
following calculation is made with the result from the trip distribution and logit model:
Tijk = TijPj (2)
2.1.4 Route assignment
The route assignment calculates how the forecasted travellers will be distributed among different links in the transport network (included non-motorized links) or the transit lines. How the route assignment works depend on the software used and what is being analysed. 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 transit assignment the number of passengers. Another difference between the two assignments regards 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 passengers an impedance function is computed. In the route assignment this function is used in order to divide the demand on each route. The impedance function reflects the unwillingness to travel and increases with longer total travel time. The impedance function in transit assignment, compared to the auto-assignment, also contain level of service variables that are not included in the auto-mobile assignment such as wait time, boarding time, and auxiliary time (walk time). The trip between two nodes can be 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 assigned to different transit lines. There are different types of transit assignments depending on the environment and available time table of the public transport. The assignment procedures 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
evaluate the entire system instead of analysing individual transit lines the transport system-based procedure is used. This requires no transit line network and is used to create a public transport network where the passengers chose the shortest routes. A timetable-based procedure should be used for transport systems that have lines with long headways, e.g. long-distance trains or transit lines in a rural area. To be able to perform a timetable-based assignment it requires complete timetable information, trip arrivals and departure times. Headway-based assignment is based on optimal strategy theory which requires frequencies and travel times for the relevant public transport lines. 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 methods with different purposes, which are described in the manuals [1] and [2]. First of all there is a standard assignment called headway-based, which mainly takes the frequency of the transit lines into consideration. This is best suited for a larger cities with frequently departing transit lines. However it is not suited for rural areas where the lines might depart less frequently. For those cases one might use the timetable-based assignment instead. This variant requires a complete timetable for all transit lines available. There is also a headway/time-based assignment, which uses both frequency and the travel time in order to decide which will be the optimal route. Another variant of the public transport assignment is the transport system-based, which creates a completely new public transport network based on the existing infrastructure.
The weights used in the algorithms are based on some valuation of the economic cost for the community. Wait, walk and transfer time is weighted more than travelling in a 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 time can be categorized and when it comes to public transport system the time is valued differently. The time elapsed while transferring between two lines are interpreted as longer than the actual time is.
The assignment weights can be adjusted in order to obtain a model of the public transport network that resembles the real life network. The parameters in this thesis have been collected from one of the Swedish Transport Administration Emme models, which consists of the recommended parameter settings. The different parameters and their values can be seen later on in Section 3.5.2. Other literature describes calibration methods and how specific networks have been calibrated with respect to the differ-ent assignmdiffer-ent parameters. The network reports have used two kinds of calibration methods; using data from transit lines, using data from surveys regarding traveller behaviour. The first method have been used in Parveen et.al. [13] and Fung [14], while the second methods have been more frequently used by Horowitz [15], Wardman [16], Abrantes and Wardman [17], Kurauchi et.al. [18] and Rydergren [19]. For example Rydergren describes in [19] how a Stockholm network have been calibrated against behaviour surveys with different assignment algorithms. The conclusions drawn from these mentioned reports are that the assignment parameters needs to calibrated after different software products, assignment algorithms, assignment settings, and network
area. Therefore the correct way to handle these parameters is to adjust them according to different situations. No previous parameter translations between Emme and Visum exists, at least according to the authors knowledge, in the headway-based assignment. Therefore an assumption will be made from studying the manuals and examining how the parameters work in each software. The parameters that will be translated are weights, factors and/or penalties for how the passengers perceive boarding, walking, travelling with a public transport vehicle, and waiting time compared to the time they could 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 are split up according to the assigned percentage. The flow will be adjusted according to equation 2, which considers the distribution on different modes, connectors between orgin nodes and the network etc. By using a logit distribution one will force the demand to chose different connector links between origin nodes and the network, boarding a transit line versus walking to another station in order to obtain a shorter travel time, or transferring to another transit line instead of staying on-board. According to Florian and Constantin, [20], the logit distribution will even out the transit travellers on different paths in several situations where they would all choose the same travel strategy. This leads to a more realistic result where passengers choose different routes from origin to destination.
3
Software 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 detailed descriptions of these software products are therefore presented with respect to public transport assignment and the algorithms available. The Section 3.2 and 3.4 describes how the public transport assignment works in the respective software. These sections also contain descriptions of the transit assignment algorithms. Visum have several algorithms, however, only the algorithm that corresponds to the Emme 3 standard algorithm will be thoroughly defined. Other algorithms are also available in Emme 4 and they will be presented in Section 3.3. Then, in Section 3.5.4, the mathematical dissimilarities 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 sources if nothing else is stated.
In the article by Florian and Spiess, [22], there are an explanation of how the optimal strategies work and an example with a small public transport network. Some previous comparisons between Emme, Visum and similar software have been studied and used as a foundation to this thesis. The conclusions of these comparisons have been of interest for further investigations. Also, identification of weaknesses in the comparisons has been important. Some of the comparisons studied are Johansson’s report [5], Larsen’s report [23] and Hägerwall Stein’s master thesis [12], where the first two papers examines both 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 the auto-mobile assignment and has therefore been used for software facts and comparison method.
3.1
Overview of macroscopic software products
The following software products contain more or less macroscopic traffic simulation features; Emme, Visum, Aimsun, TransModeler, and VIPS. Aimsun is, according to Transport Simulation System:s website [3], a hybrid between a micro-, macro-, and mesoscopic traffic simulation software. However, the main focus is on microscopic simulations and therefore it is not often used for macroscopic traffic networks such as Sweden or the Stockholm region. TransModeler simulation software is also a hybrid between the three types of detail levels; macro, micro and meso. The TransModeler
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 micro and macro (the areas of most interest are modelled on micro level and the rest at macro level). There are also public transport assignments that are based on headways or timetables. This software is not commonly used for public transport assignment in Sweden. There are however some Swedish traffic projects that uses TransModeler for dynamic auto-mobile assignment. VIPS (Volvo Interactive Planning System for public transport), described in Johansson [5], was developed by Volvo Transportation System 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 relatively few traffic analysts that still uses VIPS. In Sweden most municipalities and counties uses traffic planning tools such as software with macroscopic traffic simulation for future traffic predictions etc. Trafikverket, for example, have incorporated Emme in their traffic prognosis software that they use for all parts of Sweden and Trafikförvaltningen uses Visum instead. Since these two essential infrastructure operators in Sweden uses Emme and Visum they are of interest for further investigation. They have previously been compared in Johansson’s report [5], Larsen’s [23], and Hägerwall Stein’s master thesis [12] that will be described below.
A comparison was made in 1984 between Emme and VIPS, which was incorporated into a previous version of Visum, described in [5] at Stockholm county council (Trafikontoret Stockholms läns landsting) by Johansson. The software VIPS was used to analyse changes in the transit network, however the new launched Emme software had also recently 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 programs against a made survey. In the survey 100 persons per node-pair were asked about their itinerary. The same transit line network over central parts of Stockholm was used together with an OD-matrix for time period of an average hour between 07 : 00 − 09 : 00 in 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 split up more on different itineraries between an origin and destination in VIPS compared to Emme. 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 difference between the survey and the models regarding the number of boardings on each bus line is 30% for VIPS and 15% for Emme. Johansson, [5], also made a comment on that the penalty 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 transfer penalty 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 an explanation of how he comes to certain conclusions. There are some examples but the calculations are missing and only the final answer is stated. However, the idea of having a simple example to point out how the algorithms works is a good idea and will be used in this master thesis as well.
The traffic (auto-mobile) assignment with network equilibrium was compared, between Emme and Visum, and evaluated by Hägerwall Stein in 2007, [12]. Even though a different assignment was compared the same method as in this report was used. A
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 that the result is similar regarding the flows on the links and routes which implies that both Emme and Visum are based on the same algorithm (network equilibrium). The differences 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 University of Montréal in the seventies. In the eighties the first commercial Emme version was developed at the CRT, called Emme 2. Professor Michael Florian is one of the founders of INRO and the software Emme, and he had a key role in developing the modules used regarding the transit assignment. The Emme 4 manual [21] states that improvements have been made and new versions of the software Emme have been released. Emme 3 includes a graphic interface for network editing, more tools for simulation, analysis etc. In Emme 4 there is a congestion assignment tool that models crowding, discom-fort on vehicles, capacity limits and increasing waiting time. There are continuously ongoing development regarding the interface, analysis, implementation of virtually and zone-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 most European countries.
The macroscopic traffic simulation software Emme is used for modelling urban, regional and 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 the software 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 Florian and Spiess, [22]. The public transport assignments in Emme consists of headway-based, headway/time-based and timetable-based transit assignments, however the transport system-based assignment is not included in the currently marketed versions.
The transit network consists of centroids (zones), regular nodes, links and a set of transit 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 line segments.
The transit assignment algorithm aims to minimize the total travel time containing; wait time, auxiliary time, boarding time and in-vehicle time. The time components are weighted to compare these times with the in- vehicle time. The total travel time is converted into a general cost (T T T ), in other words the traveller wants to minimize the total cost. The time is defined for each line segment and ads up to the total travel time for the entire trip. The wait time factor scales the time a traveller has to wait
at a node for an attractive line. The factor is also used to define the waiting times together with the waiting time at a specific node. The value can differ between 0.01 to 1 and can either be node specific or the same for the entire network. The wait time weight, that describes how much the wait time is valued compared to the in-vehicle time, can be set between 0 − 999.999, as well as the parameter for boarding time. The value can be the same throughout the whole network or be node/line specific. The boarding time weight is also set between 0 − 999.999 and should represent, in relation to the in vehicle time, how much boarding or a transfer is worth. The auxiliary time weight 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 waiting times, boarding times, and average number of boardings. Graphical results are also available 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 lines total travel time, including wait time. This means that it is more profitable to board line i if it arrives directly than it is to wait for a faster attractive line. The waiting time at a node, twt, depends of the combined frequency (λi) for the attractive lines (in
the optimal set of lines i ∈ I∗
), see equation (3) obtained from Nilsson’s educational material, [24]: twt= 1 ∑ i∈I∗λi (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 time
wwt Wait time weight and factor taux Auxiliary time waux Auxiliary time weight
tboarding Boarding time
wboarding Boarding time weight
ttravel Travel time
T T T Total expected travel time, equation (4)
Also called impedance function and generalized cost
a Link index
A All links in the network
A All links which includes the optimal lines
i Line index
I All lines in the network I∗ All optimal lines, i.e. lines selected
according to the optimal strategies algorithm
n Node index
N All nodes in the network λ Line frequency h Line headway
π Combined line probability (share of the total demand), equation (5) T T T′ TTT without wait time (twtwwt), equation (12)
T T T∗ Total expected travel time of the optimal lines, equation (7)
The passengers want to minimize the travel time for the entire trip. The formula for the total expected travel time (TTT) is shown below in equation (4).
T T T = wauxtaux+ wwttwt+ wboardingtboarding+ ttravel (4)
The educational report by Matti Pursula et. al., [25], states that the assignment is performed in two parts, first computing the optimal strategy to reach the destination from 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 a set of strategies. A strategy can be explained as rules that allow the traveller to make feasible decisions and reach the destination node. An example of a strategy, according to the Emme manual [1]: At node 1, take the line that arrives first of the attractive
lines 1 and 2. If line 1 was taken, alight at node 2. If line 2 was taken alight at node 4. At node 2, take the line that arrives first of the attractive lines 3 and 4. If line 3 was taken, alight at node 4. If line 4 was taken, alight at node 3. At node 3, take the attractive line 5 and alight at node 4.
The more information the traveller has the more complex the strategies become. The traveller knows the distribution of inter-arrival times for the transit lines for a specific node and the travel time between nodes. The traveller receives the information when reaching the node and the distribution of passengers is also known. Together it is possible to calculate the combined accepted waiting time for arrival of the first vehicle in the set of transit lines passing the node and the probability of each line to arrive first. 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 combined frequency of the attractive lines that serves the node, see equation (3). This part is reversely calculated, starting in a destination node and continuing backwards to all affected origin nodes. A transportation network G consists of a set of nodes and a set of 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. The
result is the optimal strategy Anr (a sequence of links) with expected total travel time
T T T∗
nr from each node n ∈ N to destination r.
The first part of the algorithm initializes the expected travel time to reach r, T T Tnr,
to infinity for all nodes except for the destination node passengers T T T which is set to zero. The frequency variable, λi, for all i ∈ I∗, contains the combined frequencies of
the attractive lines and is initialized to zero. The set S is used to identify links that have not yet been examined, and it is initialized with all the links in A. The set A is initialized 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 is not empty the link a closest to the destination r is selected. The time T T Tnr′ + ca is
considered to be the time from node n to the destination r without including waiting time at node n. If this time is smaller compared to the previous time at n, T T Tnr the
link a is included in the optimal strategy and both λi and T T Tnr are updated to the
new combined total travel time of the attractive transit lines. It is important to know how T T Tnr changes. The first time it will be λiT T Tnr= ”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 the attractive 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 headway or the perceived headway of a specific transit line or segment (user-determined). The headway in Emme is used to define the waiting times but also used for dividing the passengers on attractive transit lines.
In the second part of the algorithm, the demand from node i to the destination r, gir,
node i at the links a ∈ A corresponds to its frequency, see equation (5). The volumes can be updated simultaneously because the links are evaluated in reverse topological order (decreasing T T Ti′+ ca) and therefore it is possible to examine every link only
ones.
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) is attractive if:
T T Tfirst choices> T T Ti,second choice′ (6) This means that the travel time for line i is lower than the total expected journey time for the first choice. Therefore it will be better to board line i if it would arrive at the stop now than to wait for the first choice line. In order to calculate the final expected total journey time for all the combined attractive lines the following equation is formulated. T T T∗ i = ∑ j∈I∗ πjT T T ′ j+ twtwwt (7)
3.3
Emme 4
If Trafikverket will choose to upgrade their Emme version to Emme 4 there will be some new 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 4 manual [26], the extended transit assignment. Therefore this will be more thoroughly described than the other assignments in Emme 4 and all facts are based on the prompt manual, [26], and the scientific report by Cepeda, Cominetti and Florian, [27], which describes some of the new features in Emme 4.
Extended transit assignment
This assignment is based on the standard transit assignment and the theory of optimal strategy but in this extended version it is possible to model a connector choice. In other words the travellers can be divided among more connectors instead of only choosing the shortest connector. Also the choice of route is more sensitive to travel times (in addition to the headway), so lines with lower frequencies and shorter travel times still can be an attractive option.
In the extended transit assignment there are still the parameters used in the standard transit assignment and some extra optional parameters such as boarding, in-vehicle and auxiliary transit cost. The boarding cost is a penalty associated with every boarding that is done (both initial and transfer). The in-vehicle cost will be multiplied with the in-vehicle time weight and can be constant, segment, link, node or transit line specified. The cost will be added to the total travel time. The auxiliary transit cost can be constant, node or link specified and is multiplied with the weight and will be added to the total auxiliary time in the TTT-equation.
There is an optimal choice at the origin where a logit distribution can be used to split the flows on different connectors. If the logit distribution is not used all travellers will leave 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 or the ones with a special attribute. At the choice points the distribution may be applied on all connectors or just the efficient ones, which means the connectors that brings the traveller closest to their destination node. When using the logit distribution for nodes with specified attributes it makes it possible to use different choice sets at different origins (1 indicates to use logit on all connectors and −1 to apply only to the efficient ones).
The logit function in the program contains two parameters, scale and truncation. The scale 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 higher proportions. The truncation parameter is used to drop connectors that have propor-tions that are considered being too small. The connectors with smaller proporpropor-tions than the given truncation parameter are not included until the remaining connectors have proportions larger than the parameter value.
The proportions computed for the connectors can be changed to fixed proportions using a user-defined link attribute. The proportions must be between 0 and 1 and the sum of for all the connectors from an origin must be 1. This can only be done on a subset of 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 at the regular nodes with auxiliary transit choices. When using the logit assignment the travellers 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 transit link
All travellers in an assignment without logit wait for a vehicle or leave the node by an auxiliary transit link. When using the logit assignment it is also possible to split the travellers between stay on board and alighting. The travellers that alight must leave by 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 based on the impedance to the destination. The same logit function parameters are used as in the function for original nodes.
In the standard transit assignment the flow distribution is based on the frequency but in the extended assignment there is a choice to use a distribution based on frequency and transit time to the destination. This means that fast lines with lower frequency are more attractive, which results in smoother flow changes. This option can be chosen in the whole network or for certain nodes.
