Comparative Evaluation of Road Trafﬁc Simulators based on Modeler’s

Speciﬁcations: An Application to Intermodal Mobility Behaviors

Azise Oumar Diallo

1 a

, Guillaume Lozenguez

1 b

, Arnaud Doniec

1 c

and Ren

e Mandiau

2 d

CERI Syst

emes Num

eriques, Institut Mines-T

ecom (IMT) Lille-Douai, Villeneuve d’Ascq, France

LAMIH UMR CNRS 8201, Universit

e Polytechnique Hauts-de-France, Valenciennes, France

Keywords:

Agent-based Modeling, Trafﬁc Simulator, Intermodality.

Abstract:

Today, large cities and peri-urban areas experience problems in the mobility of their population. Faced with

this problem, decision-makers must have reliable tools to help them to build and evaluate their policies of mo-

bility. Computer simulations especially trafﬁc simulation tools are, therefore, the solution to better understand

(study) the problem and test different resolution scenarios. Unfortunately, there are numerous simulation tools

and the choice can be very difﬁcult for trafﬁc modelers. In this paper, we present, based on a generic method,

a comparison of the most popular trafﬁc simulation tools in two steps: 1) a comparison part using a weighted

system of evaluation criteria to automatically select the candidate simulators. 2) a deeper study of the candi-

date simulators according to a simulation scenario corresponding to the study case. Finally, this paper presents

an application of this method for the selection of a simulator for the study of intermodal mobility behaviors

where MATSIM and SUMO were studied in deeper.

1 INTRODUCTION

Nowadays, transportation is one of the vital needs

of humans in the same way as access to water,

health care, and education. In addition, faced with

the issues of global warming and expensive living,

policymakers are increasingly focused on people’s

transportation systems to provide both economic and

customer-friendly solutions. In order to experiment

with their solutions, decision-makers use most often

trafﬁc simulation tools(Pursula, 1999).

The initial motivation for this work was the study

of the capability to simulate intermodality

and to in-

tegrate new behaviors into the simulation. There is

a need to easily add such behaviors in a multimodal

simulation. Fortunately, recent developments in traf-

ﬁc simulation tools allow simulating these kinds of

behaviors.

Modern trafﬁc simulation tools are based on dif-

ferent principles and have two main origins: indus-

https://orcid.org/0000-0002-8865-9760

https://orcid.org/0000-0001-6875-7702

https://orcid.org/0000-0002-3843-6729

https://orcid.org/0000-0001-7722-9848

Intermodality is usually deﬁned as the capability of us-

ing several transportation modes during the same trip.

trial and academic ones. Industrial simulators (e.g.,

Vissim, Aimsun), developed for commercial purposes,

are generally easier to use and offer user assistance.

However, they are less extensible and offer less ﬂe-

xibility and limit the user to predeﬁned cases. The

second group of simulators comes from research labs

(Lopez et al., 2018; Horni et al., 2016; Mandiau et al.,

2008). Developed as part of research work, these so-

lutions are usually less complete and not easy to use

for an uninitiated user.

Simulation tools offer different levels of traf-

ﬁc modeling usually classiﬁed into three following

groups: macroscopic, microscopic, and mesoscopic.

In macroscopic models, vehicle ﬂow is assimilated to

the runoff of a ﬂuid in a pipe and modeled through

the equations of ﬂuid mechanics. In contrast, in mi-

croscopic and mesoscopic approaches, each actor of

the trafﬁc is respectively considered individually and

in small homogeneous groups. These simulations are

implemented through a behavioral approach: road

trafﬁc is considered as an emergent phenomenon re-

sulting from interactions between road users modeled

as autonomous agents (or group) and a realistically

modeled infrastructure. Some works have even inves-

tigated the integration of these different levels inside

a same agent-based simulation (Mathieu et al., 2018).

In light of this diversity of trafﬁc simulation tools,

Diallo, A., Lozenguez, G., Doniec, A. and Mandiau, R.

Comparative Evaluation of Road Trafﬁc Simulators based on Modeler’s Speciﬁcations: An Application to Intermodal Mobility Behaviors.

DOI: 10.5220/0010238302650272

In Proceedings of the 13th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2021) - Volume 1, pages 265-272

ISBN: 978-989-758-484-8

265

the modeler

can have some difﬁculties in choosing

the appropriate simulator that meets his/her speciﬁ-

cations or the requirements of a project. Therefore,

in this paper, we propose a simple method to quickly

compare different simulation tools as a ﬁrst step of a

selection process. This comparison is based on fun-

damental criteria and those relating to the targeted

project. As a second step, the two selected solutions

SUMO and MATSim are investigated regarding an in-

termodal transport scenario.

The remainder of the paper is organized as fol-

lows. Section 2 presents the goal of this study and

provides an overview of related works on the compar-

ison of trafﬁc simulators. Then, Section 3 describes

the approach used to compare and select simulators.

Section 4 presents the comparison results of SUMO

and MATSim. Finally, Section 5 concludes the paper.

2 PROBLEM STATEMENT

Comparison studies in literature of trafﬁc simulators

can be split into two groups mainly depending on the

method used. These two approaches are described in

the two following subsections.

2.1 Qualitative Methods of Comparison

Qualitative approaches are based on some comparison

criteria in order to see the ability of software to meet

these criteria.

Ejercito et al. (Ejercito et al., 2017) performed

a comparison of trafﬁc simulators in order to choose

the most reliable and suitable to allow researchers to

simulate road trafﬁc in the EDSA (Epifanio de los

Santos Avenue) in Manila (Philippines). The simu-

lators studied were MATSim, SUMO, AIMSUN, and

PTV VISSIM. Several criteria were considered in this

work such as nature of software (e.g., free, open-

source, commercial), portability operating systems,

creation of road trafﬁc networks and associated ve-

hicle models, and graphical simulation and quality

of graphical representation. The authors only focus

on how the different functionalities in the simulators

are deﬁned based on the comparison criteria. There-

fore, conclusions drawn by the authors are not pre-

cise enough to guide the choice of a given modeler in

the selection of a trafﬁc simulator for another speciﬁc

case.

Saidallah et al. (Saidallah et al., 2016) and Ghar-

iani et al. (Ghariani et al., 2014) start with a pre-

sentation of the simulators studied by highlighting

In the following, we will use the term modeler to des-

ignate a person who uses a trafﬁc simulation software.

their different characteristics. Then, they perform a

comparative study (still qualitative) based on a set

of selected criteria such as general characteristics

(e.g., software category), integration of transit com-

ponents (e.g., roads, stops), and simulation models

(microscopic or macroscopic). The work in (Ghariani

et al., 2014) focused on the study of seven simulators

(SUMO, TRANSMIS, ARCHISIM, AIMSUN, Param-

ics, VISSIM, and CORSIM) according to their ability

to simulate public transport. The study carried out

in (Saidallah et al., 2016) focused on four simulators

(MATSim, SimTrafﬁc, MITSIMLab, and TransMod-

eler) in addition to the seven previously mentioned.

The qualitative comparison framework proposed

by these authors (Saidallah et al., 2016; Ghariani

et al., 2014) is quite explicit. Through the summary

table, one is briefed about the functionalities that sim-

ulators can satisfy. However, this information is only

limited to the functionalities deﬁned by the authors.

Moreover, some features have not been studied such

as portability, generation of trafﬁc demand, and soft-

ware scalability.

The main drawback of the above-mentioned ap-

proaches (qualitative) is that they are subjective since

the evaluation criteria are too speciﬁc in most cases

to the project or to the modelers (authors). The re-

sults of the study just indicate whether the simulator

can meet these criteria or not. Thereby, conclusions

of these studies are general and difﬁcult to tailor to a

particular modeler’s needs. Furthermore, a quantita-

tive evaluation of each simulator (as described in the

next section) should be added based on the conclu-

sions drawn from the qualitative comparison.

2.2 Quantitative Approaches of

Comparison

The quantitative approaches aim to supplement the

qualitative methods by proposing a notation to the

simulators studied. To our knowledge, only one study

has focused on this type of comparison by using a sys-

tem of classiﬁcation to compare AIMSUN and VIS-

SIM(Xiao et al., 2005). The authors proposed in ad-

dition a weighting system to give a mark to each sim-

ulator. Unlike the studies above mentioned, the eval-

uation criteria are both qualitative (e.g., functional

capabilities and input/output features) and quantita-

tive (e.g., accuracy of the simulator). This system

takes into account the modeler’s priority by assigning

weights to each evaluation criterion. One of the main

limitations of this approach is the qualitative evalua-

tion performed by the authors. The conclusions drawn

are not explicit enough as in (Saidallah et al., 2016)

and (Ghariani et al., 2014). In addition, some criteria

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

266

designated by the authors as quantitative such as the

accuracy of the simulator and the setting time are dif-

ﬁcult to assess without studying the simulators a little

more in-depth. Finally, the system of classiﬁcation is

quite difﬁcult to reproduce and extend to other simu-

lators.

In general, the choice of the simulator depends on

some criteria (qualitative and/or quantitative) as spe-

ciﬁed in the works above mentioned. It will be in-

terested to have a classiﬁcation system that will al-

low the modeler to perform a quick selection without

studying the simulators a little more in-depth. The

aim is to save time by avoiding in-depth study of sim-

ulators that are not likely to respond to the case study.

Considering the limitations of existing studies, it is,

therefore, necessary to propose a more intuitive eval-

uation approach to guide modelers in the choice of

their trafﬁc simulation tools.

3 CRITERIA FOR EVALUATING

EXISTING PLATFORMS

This section describes the ﬁrst step of our comparison

approach. It consists ﬁrst in deﬁning the comparison

criteria. Then, the most popular simulators are evalu-

ated against their ability to meet the comparison cri-

teria deﬁned by the modeler. At the end of this step, a

restricted list of simulators is carried out for a deeper

study.

3.1 Deﬁnition of Our Criteria

We have grouped the comparison criteria into ﬁve cat-

egories: 1) Nature of software, 2) Creation of Road

Network and Transport Demand, 3) Simulation

realism, 4) Documentation and GUI, 5) Modeler’s

speciﬁcations.

A coefﬁcient is assigned to each category of cri-

teria deﬁning its importance for the modeler. We

propose a scale from 1-not important at all to 5-

highly mandatory. Then, a mark (mark

cat

) values

each simulator for each category of criteria. This

mark assesses the simulator’s ability to meet the crite-

ria/functionality in the category. Afterward, the score

ScoreSim assigned to a simulator is computed as a

weighted average (with mark

cat

value bounded to 10).

ScoreSim =

∑

cat∈[1..n]

mark

cat

× coeff

cat

∑

cat∈[1..n]

coeff

cat

(1)

with coeff

cat

corresponding to the coefﬁcient assigned

to the category of criteria cat according to the mod-

eler’s speciﬁcations and n the number of the category

of criteria cat.

This approach allows the modeler to quickly eval-

uate several simulation tools using the same formula.

Thus, he/she can explore the functionalities of sev-

eral types of simulators at the same time. Moreover,

the modeler can designate a criterion or functionality

as “redhibitory”. Thus, any simulator that does not

satisfy this criterion will be systematically eliminated

for the rest of the study. A list of the most popular

simulators used in this study is described in the next

subsection.

3.2 Studied Trafﬁc Simulators

Several simulation software have been considered to

test the comparison approach. They can be organized

into two groups deﬁned as follows:

1. Microscopic Agent-based Road Transport

Simulators. These are speciﬁc simulation

platforms (intended only) for road trafﬁc. The

simulators chosen are: MATSim (Multi-Agent

Transport Simulation Toolkit) - Version 0.10.1

(Horni et al., 2016), SUMO (Simulation of Urban

Mobility) - Version 1.0.1 (Lopez et al., 2018),

Aimsun Next (Advanced Interactive Microscopic

Simulator for Urban and Nonurban networks)

- Version 8.1.4

, and PTV Vissim (Planung

Transport Verkehr AG Verkehr In St

adten -

SIMulationsmodell) - Version 10

2. Generic Multi-Agent System (MAS) Simula-

tors. Being generic, they can be adapted for mod-

eling several systems including road trafﬁc. Our

choice fell on GAMA (GIS Agent-based Modeling

Architecture) - Version 1.8 (Grignard et al., 2013).

Although the above-mentioned simulation software

are not the only ones in the literature, they are those

which are the most used in their respective ﬁelds.

3.3 Evaluation based on Comparison

Criteria

Considering our case study which consists in know-

ing whether or not there is a suitable trafﬁc simulator

to simulate intermodality policies, we have assigned

coefﬁcients to each category of criteria as presented

in Table 1.

The results of the comparative study of simulation

software are presented in Tables 2 to 6 corresponding

to each category of criteria.

www.aimsun.com/aimsun-next (Oct. 25th 2018).

vision-trafﬁc.ptvgroup.com/fr/accueil (Oct. 25th

2018).

Comparative Evaluation of Road Trafﬁc Simulators based on Modeler’s Speciﬁcations: An Application to Intermodal Mobility Behaviors

267

Table 1: Assigning coefﬁcients to criteria categories based

on the modeler’s priorities.

Category of criteria Coefﬁcient

(Cat. 1) Nature of the software 4

(Cat. 2) Creation of road network and transport demand 5

(Cat. 3) Quality of visualization of the simulation 3

(Cat. 4) Documentation and user’s interface 4

(Cat. 5) Modeler’s speciﬁcations 5

Total of coefﬁcient 21

Table 2: Evaluation of simulators according to the nature of

the software (Cat. 1).

Criterion

Simulator Open

source

Free Dev.

team

Single

acqu.

Win. Linux Mac

Mark

MATSim X X X X X X X 10

SUMO X X X X X X X 10

Aimsun Next X X X X X 7

PTV Vissim X X X 4

GAMA X X X X X X X 10

Table 3: Evaluation of simulators according to the possibi-

lity of creation the road network and transport demand (Cat.

2).

Criterion

Simulator Visual

tool inte-

grated

Network

from

OSM

Transport

demand

PT net.

and

sched.

Mark

MATSim X X X 8

SUMO X X X X 10

Aimsun Next X X X ?

PTV Vissim X X X ? 8

GAMA X X 5

Table 4: Evaluation of simulators according to the quality

of visualization of the simulation (Cat. 3).

Criterion

Simulator 2D 3D Realism

Few memory

Mark

MATSim !

X 3

SUMO X X 5

Aimsun Next X X X X 10

PTV Vissim X X X X 10

GAMA X X X 8

Some simulators do not allow the creation of the road

network directly. In this case, one needs to use other soft-

ware for creating road trafﬁc (details in Section 4).

Commercial software evaluation versions did not allow

us to test this functionality.

The criterion of realism strongly depends on the expec-

tations of the modeler. It can be decomposed at the macro-

scopic and microscopic levels. At the macroscopic level,

realism relates to the physical quantities observed: density,

Table 5: Evaluation of the simulators according to the qual-

ity of the documentation and the GUI (Cat. 4).

Criterion

Simulator On

line

PDF Forum Confe-

rence

Commu-

nity

Trai-

ning

GUI Mark

MATSim X X X X X X X 10

SUMO X X X X X 7

Aimsun Next X X X X 6

PTV Vissim X X X X X X 9

GAMA X X X X X X 9

3.4 Short Listing of the Solutions

Table 7 proposes an overview through the ﬁve cate-

gories. It makes it clear that no simulator is ideal to

meet all the needs and requirements of the modeler.

The simulators dedicated to trafﬁc, outperform

our test with a score of up to 7.4. The delimitation

between commercial and OpenSource software is also

clear. Commercial software are well integrated into

the GIS solution to build and set up simulation and

provide qualitative GUI and simulation view. How-

ever, those solutions remain obscure considering the

possibility to enter into the simulation source code

and provide customized behaviors and simulation.

The simulators chosen to be more deeply inves-

tigated by implementing intermodality scenarios are

MATSim and SUMO. For this work, we were interes-

ted in the intermodal routing module in particular in

the cost function of the modal choice (intermodal) im-

plemented by these two simulators.

4 FOCUSING ON INTERMODAL

ROUTING PROBLEM FOR

MATSim vs SUMO

This second step of comparison consists in studying

more deeply the selected simulators on a road trafﬁc

scenario. To compare the two simulation tools with

respect to the intermodal routing problem, the em-

phasis will put on the four aspects: 1) creation of a

complete multimodal transportation network, 2) def-

inition of the availability of transportation modes, 3)

ﬂow, average speed, etc. At the microscopic level, the real-

ism relates to the observable behavior of users (car driver,

pedestrian) and the way they move (walking, car steering).

It is assumed that a need of less than 16 GB of RAM is

sufﬁcient for a 3D display and realistic effects.

MATSim does not allow the visualize simula-

tion results. Another tool such Simunto VIA (see

https://www.simunto.com/via/) is used for this.

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

268

Table 6: Evaluation of simulators according to the modeler’s speciﬁcations (Cat. 5).

Criterion

Simulator Model mi-

cro./meso.

Scaling User and mode

characteristics

Statistics

output

Intermodality Calibration Dynamic

behaviors

API Source code

access

Mark

MATSim X X X X X X X 8

SUMO X X X X X X X X 9

Aimsun Next X X X X X X ? 7

PTV Vissim X X X X X X ? 7

GAMA X X X X 4

Table 7: Comparison of simulators according to chosen cri-

teria (ﬁrst step).

Category of criteria

Simulator Cat.1 Cat.2 Cat.3 Cat.4 Cat.5 ScoreSim

SUMO 10 10 5 7 9 8.48

MATSim 10 8 3 10 8 8.05

Aimsun Next 7 8 10 6 7 7.48

PTV Vissim 4 8 10 9 7 7.48

GAMA 10 5 8 9 4 6.90

intermodal routing algorithm (the cost function of the

modal choice), 4) degree of difﬁculty in the deﬁnition

of new intermodal behaviors.

After a description of trafﬁc simulation scenario

generation in MATSim and SUMO in the ﬁrst subsec-

tion, the two other subsections detail these different

points for each simulator.

4.1 Trafﬁc Simulation Scenario

Generation Framework

The use of both SUMO and MATsim relies on several

tools to generate simulation conﬁgurations from clas-

sical data format. Figures 1 and 2 present a global

framework of the generation of a trafﬁc scenario from

OpenStreetMap (OSM)

and mobility data (usually

the Households Travel Survey (HTS) and/or the Cen-

sus of the population) in SUMO and MATSim respec-

tively.

The road network is generally produced from

the OSM data of the study area extracted from

OpenStreetMap. Moreover, it is possible to include

GTFS

data, allowing a better description for the

public transport supply in MATSim context. Details

on the network generation tools are presented in the

subsections dedicated to each simulator.

OpenStreetMap is an open data platform that

provides map information of roads, trails, etc.:

https:/https://www.openstreetmap.org/about

General Transit Feed Speciﬁcation (GTFS) is a stan-

dard for transit schedules and geographic information:

https://gtfs.org/

Figure 1: General process to create trafﬁc scenarios from

OSM and mobility data (HTS/Census) in SUMO.

Figure 2: General process to create trafﬁc scenarios from

OSM and mobility data (HTS/Census) in MATSim.

The initial transport demand is generally created

from a synthetic agent population synthesizer. This

population reﬂects the mobility data, for example

from household travel surveys (HTS) and/or census

data. It should be noted here that this population is

not necessarily speciﬁc to the format of the simula-

tor. Therefore, from the mobility information of the

population generated, it is possible to create a traf-

ﬁc demand in the simulator format (xxx.trips.xml in

SUMO and populationFile.xml in MATSim for exam-

ple). This demand can then be directly simulated

without deﬁning travel routes. On the other hand,

it takes a long time with regard to the routing time.

Thus, the tool Duarouter allows creating a trafﬁc de-

Comparative Evaluation of Road Trafﬁc Simulators based on Modeler’s Speciﬁcations: An Application to Intermodal Mobility Behaviors

269

mand (xxx.rou.xml) with cleaned routes which can be

easily simulated by SUMO.

4.2 Intermodal Routing in SUMO

To perform intermodal routing, we need a multi-

modal network, the availability of several transporta-

tion modes to combine them, and the deﬁnition of a

cost function of the modal choice. These different

steps will be studied for each of the two simulators.

4.2.1 Creation of a Complete Multimodal

Transportation Network

The multimodal network is well modeled in SUMO.

Netconvert creates the road network for SUMO from

OpenStreetMap data. Netedit allows corrections on

the network created because some information may

be incorrect from the OpenStreetMap site, such as the

use of a trafﬁc light instead of priority stop at an inter-

section. Each speciﬁc lane to a category of transporta-

tion mode can be modeled. SUMO also allows the co-

habitation of several transportation modes in the same

lane such as cars, buses, and motorcycles. Interac-

tions between pedestrians and motorists are also pos-

sible at zebra crossing at intersections. Trafﬁc control

systems like trafﬁc lights are also taken into account

in SUMO.

The public transport network is generated from

OSM data. Thus, the network only includes the stops

and lines listed on OpenStreetMap. Information on

transit schedule and the types of vehicles assigned to

each line are not taken into account. However, the

tool Netedit aims to complete and to modify the road

network generated from OSM data. This allows to

create a model of the network close to reality and to

add news elements of the road infrastructure to it.

4.2.2 Availability of Transportation Modes

The deﬁnition of the transportation modes available

or accessible by the traveler is essential to properly

model an intermodal transport supply. For example,

it would not be possible to use a bicycle or a car and

then take the train if the traveler does not know these

modes.

SUMO takes into account the transportation

modes available in an intermodal trip through the

modes attribute in the personTrip module, which will

contain the name of these modes. For example,

modes=“car public” means that the agent can use

the car, or public transport or a combination of both.

Walking is assumed to be available to all agents while

the other modes need to be supplied explicitly.

4.2.3 Cost Function of the Modal Choice

After deﬁning the available transportation modes, the

modal choice cost function is used to determine the

mode(s) of transport to use to perform the trip. The

choice of one mode or the combination of modes

is generally based on several characteristics such as

the cost, the travel time, and some user’s socio-

demographic attributes such as age and social profes-

sional categories.

Currently, SUMO only takes into account the

travel time of the transportation modes. Thus, the

mode(s) chosen is/are determined by the one/those

which has/have the shortest travel time according to

the route calculated by Duarouter. To calculate the

travel time, Duarouter uses a shortest-path routing

algorithm such as Djikstra or A

∗

. The modeler can

choose the routing algorithm that suits him/her best.

The travel time of an edge is generally calculated in

the free-ﬂow trafﬁc according to the maximum speed

of the mode (car, bike, walking) and the speed limit

of the edge. Travel times by public transport are cal-

culated from the time of departure, the waiting time,

and the difference in intermediate times between suc-

cessive stops.

4.2.4 Difﬁculty for New Intermodal Behaviors

In SUMO, a person can be in three states during

his/her trip: riding, walking, or stopping. The rid-

ing state, through the attribute modes of the person-

Trip module, allows the use of multiple transporta-

tion modes to perform a trip. Therefore, it is possible

to deﬁne intermodal mobility behavior between the

origin and destination points (edges) by deﬁning the

transportation modes available for an agent. However,

the availability of transportation modes is not the only

factor inﬂuencing the modal choice. The human di-

mension and the transport supply should be taken into

account. Such modiﬁcations cannot be carried out by

simply customizing the attributes of agents and the

transportation modes. Therefore, some adjustments

or additions to the source code may be necessary to

consider new intermodal mobility behaviors. How-

ever, the complexity of the SUMO source code makes

this operation very tedious.

4.3 Intermodal Routing in MATSim

This subsection presents the steps required to perform

intermodal routing in MATSim as presented above.

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

270

Table 8: Main characteristics of agent, network and modal choice in SUMO and MATSim.

Agent’s behaviors Network Modal choice

Simulator riding walking stopping in

activity

crossing

interaction

multimodal transit

schedule

control

system

utility

function

multi-

criteria

trip-

based

tour-

based

shortest-

path

SUMO X X X X X X X X

MATSim X X X X X X X X X X X

4.3.1 Creation of a Complete Multimodal

Transportation Network

The multimodal network for MATSim is generated

with the pt2matsim module (Poletti et al., 2017).

Therefore, cars and buses can interact in the same

link. However, there is no dedicated lane for pedes-

trians and therefore no interactions between drivers

and pedestrians. To do this, pedestrians are tele-

ported between the origin and destination points at

a certain time duration normally corresponding to

their walking times. Some trafﬁc control systems like

trafﬁc lights are not modeled in MATSim. Adjust-

ments such as the deﬁnition of free speed and the re-

moving/adding link are possible via the tool JOSM

through an additional plugin (Neumann and Zilske,

2018). It should be noted that this plugin allows to

create a unimodal transportation network for private

cars.

4.3.2 Availability of Transportation Modes

MATSim takes into account the availability of trans-

portation modes when an agent performs a trip. Walk-

ing is still possible (teleportation in this case). The

modes must be deﬁned either globally (parameter

mainMode in the QSim module) or individually for

each agent (mode attribute of the leg parameter in the

plan of a person). If a mode is not deﬁned in QSim,

the agent using this mode will be teleported.

4.3.3 Cost function of the Modal Choice

To understand the modal choice cost function, it is

necessary to have an overview on the overall function-

ing of MATSim. Figure 3 presents the general MAT-

Sim simulation cycle.

Figure 3: MATSim simulation cycle (ref. (Horni et al.,

2016)).

The choice of transportation mode is performed in the

Decision making phase. This phase assigns a score to

https://josm.openstreetmap.de/

all plans of an agent in each simulation cycle (itera-

tion) based on a utility function. The score of a plan

depends on the utility of the activities carried out and

the utility for traveling (typically negative) to those

activities based on the transportation mode used. To

improve the score of a plan, several replanning strate-

gies are possible: changing the route, changing the

transportation mode or changing the starting time of

the activity. These strategies are conﬁgurable by the

modeler who can choose the percentage of agents that

will change plans between two iterations. In this case

study, we are interested in changing the transportation

mode. Currently, the utility function of the transporta-

tion mode is based on a single factor, the travel time

which is calculated at each end of the cycle. MATSim

is also based on shortest-path routing algorithms such

as Dijkstra to estimate travel times during the simula-

tion.

4.3.4 Difﬁculty in the Deﬁnition of New

Intermodal Behaviors

MATSim allows to simulate the daily movements of

a given population including commutes, leisure trip,

etc. Each agent is modeled taking into account his/her

capacity (e.g., age and possession of a driving li-

cense) to use a given transportation mode. The modal

choice is based on a utility function that takes into

account the human dimension. Thus, by customiz-

ing some attributes/parameters of utility function such

as marginal utility of money and marginal utility of

travel time, it is possible to reproduce certain basic

intermodality behaviors. On the other hand, to deﬁne

new behaviors it is necessary to modify some func-

tionalities in the source code. Fortunately, these func-

tionalities are modularly designed and MATSim offers

a lot of ﬂexibility in the modiﬁcation of its modules.

4.4 Discussion

Table 8 summarizes our analysis (agents, network and

modal choice). The multimodal network of SUMO is

better modeled than in MATSim. It should be noted at

this level the difﬁculty of correcting the SUMO mul-

timodal network for larger study areas. This can take

a long time to work without being sure of the ﬁnal re-

sult. Li et al. (Li et al., 2018) showed, for example,

ﬂow problems at intersections where vehicles could

Comparative Evaluation of Road Trafﬁc Simulators based on Modeler’s Speciﬁcations: An Application to Intermodal Mobility Behaviors

271

get stuck without entering the roundabout, thus caus-

ing unrealistic congestions. Taking GTFS data into

account, MATSim multimodal network offers a more

realistic public transport supply. Integration of GTFS

data into SUMO are still in progress.

MATSim, thanks to its activity and agent-based ap-

proach, allows better modeling intermodal mobility

behaviors by taking into account, for example, the trip

purpose, the chains of daily activities, and some at-

tributes of the user such as car availability. The modal

choice is also based on a utility function that can con-

sider several criteria such as travel time and cost. This

choice can be performed upstream of the simulation

through a pairing with a discrete choice model (H

orl

et al., 2019). However, due to a high abstraction level,

MATSim does not simulate microscopic interaction as

the pedestrian crossing. SUMO takes advantage if the

studies focus on a speciﬁc multimodal area such as

a town square with a train station, buses, bikes, car

parking plot. In addition, through the MATSim loop,

it is possible to better assess the travel behavior of

agents who, thanks to replanning, can modify their

habits. This is quite interesting because one can imag-

ine new users learning to deﬁne the routes as they

discover the road network and the state of the traf-

ﬁc. SUMO also has an iteration module (dua-iterate),

but it only relies on travel times to make the choice of

transportation mode.

5 CONCLUSION

In this paper we evaluated different simulation plat-

forms with the aim of choosing the most suitable to

simulate intermodality policies. The paper ﬁrst pre-

sented a state of the art of trafﬁc simulators compar-

ison. These works can be divided into two groups

depending on the evaluation method used: qualitative

or quantitative. Based on these works, we proposed a

new comparison approach that is both qualitative and

quantitative and which takes into account criteria spe-

ciﬁc to the modeler. It consists ﬁrst in evaluating the

simulators on their ability to meet the evaluation crite-

ria. From this step, a shortlist is retained for a deeper

study. Finally, the paper presented a case of study

where we evaluated ﬁve simulators: four speciﬁc to

trafﬁc and one generic. We retained SUMO and MAT-

Sim and compared their capabilities to simulate the

intermodal mobility behaviors of a given population.

MATSim was selected as the most appropriate simula-

tion tool to reproduce intermodal mobility behaviors

on large scale.

As future works, we plan to focus our studies on

MATSim, working on embedding an intermodal mode

choice module. The aim will be to estimate the pa-

rameters of this model from actual mobility data such

as HTS and then to implement it inside MATSim.

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