Implementation of a cost-benefit analysis
of Demand-Responsive Transport with a
Multi-Agent Transport Simulation

Conny Grunicke${}^{\text{a},\text{b}}$

Jan Christian Schlüter${}^{\text{b,c,*}}$

Jani-Pekka Jokinen${}^{\text{b}}$

${}^{\text{a}}$ Chair of Statistics, Department of Economics, Georg-August-
University of Göttingen, Humboldtallee 3, 37073 Göttingen, Germany

${}^{\text{b}}$ Department of Dynamics of Complex Fluids, Max-Planck-Institute
for Dynamics and Self-Organization, Am Fassberg 17, 37077 Göttingen, Germany

${}^{\text{c}}$ Institute for the Dynamics of Complex Systems, Faculty of
Physics, Georg-August-University of Göttingen, Friedrich-Hund-Platz 1, 37077
Göttingen, Germany

## Abstract

In this paper, the technical requirements to perform a cost-benefit analysis
of a Demand Responsive Transport (DRT) service with the traffic simulation
software MATSim are elaborated in order to achieve the long-term goal of
assessing the introduction of a DRT service in Göttingen and the surrounding
area. The aim was to determine if the software is suitable for a cost-benefit
analysis while providing a user manual for building a basic simulation that
can be extended with public transport and DRT. The main result is that the
software is suitable for a cost-benefit analysis of a DRT service. In
particular, the most important internal and external costs, such as usage
costs of the various modes of transport and emissions, can be integrated into
the simulation scenarios. Thus, the scenarios presented in this paper can be
extended by data from a mobility study of Göttingen and its surroundings in
order to achieve the long-term goal. This paper is aimed at transport
economists and researchers who are not familiar with MATSim, to provide them
with a guide for the first steps in working with a traffic simulation soft-
ware.

Keywords: Transport Simulation; MATSim; Agent-Based Modeling; Demand
Responsive Transport; Cost-Benefit Analysis

*Corresponding author at: Max-Planck-Institute for Dynamics and Self-Organization, Department of Dynamics of Complex Fluids, Am Fassberg 17, 37077 Göttingen, Germany
E-mail address<EMAIL_ADDRESS>

##  1 Introduction

Due to the significant increase in the volume of traffic in recent decades and
the dominant position of motorized private transport, the negative effects on
people and the environment have intensified (UBA, 2019). To reduce greenhouse
gas emissions and prevent climate change, new solutions and innovations are
needed in sectors such as transport. For this reason, sustainable transport
systems, such as cycling, public transport, and new mobility concepts, are of
increasing interest to scientists of different disciplines and public
decision-makers (Gössling and Choi, 2015; Johansson et al., 2017). The
economic evaluation of transport measures and policies is used to assess their
economic efficiency and benefits. To assess the overall welfare impact of such
measures and policies, many countries frequently conduct a cost-benefit
analysis as a guideline for decisions on infrastructure projects (Bristow and
Nellthorp, 2000; Mackie and Worsley, 2013). To determine the overall impact on
welfare, this assessment method examines the extent of the social and economic
impact. An essential feature of the analysis is that it also monetarises
negative external effects, such as congestion, air ,and noise emissions, and
thus reflects not only private costs but also social costs (Damart and Roy,
2009). In addition, social acceptance has a significant influence on the
prospects of successful implementation of a measure or a new concept.
Therefore, this implies that a consideration of the individual benefits of
transport infrastructure is very helpful for a comprehensive assessment.
A paper by Kickhöfer, (2009) argues that multi-agent transport simulation is
able to illustrate those individual changes in the welfare level which results
from changes in transportation mode, travel times or travel costs. The use of
simulations, in general, is not uncommon in the transport sector, as these
make it possible to predict the long-term effects of new mobility concepts,
even before their introduction and the investments associated with them. The
main difference between conventional transport simulations and the agent-based
simulation is the representation of changes in utility on an individual level.
However, the use of multi-agent transport simulation is relatively new and has
not yet been used for a cost-benefit analysis of new mobility concepts. Part
of these new mobility concepts are flexible intelligent transport systems. One
realization of such a flexible service is Demand-Responsive Transport (DRT).
DRT dynamically adapts to travel requirements by operating a fleet of vehicles
without fixed routes and schedules. This allows journeys to be made without
transferring, as either a door-to-door or a corner-to-corner service is
offered. As there seems to be no study using agent-based simulation for a
cost-benefit analysis of new flexible transport services such as DRT, the
contribution of this paper is to elaborate the technical requirements for it.
Therefore, the intention of this project paper is to prepare the long-term
goal of simulating the traffic in Göttingen and evaluating the offer of a DRT
service in Göttingen and the surrounding area. The traffic in Göttingen shall
serve as an example. The current traffic situation represents the baseline
scenario. In a second step, this will be compared with a scenario in which
there is an additional offer of DRT in the Göttingen area. This comparison is
intended to build the foundation for conducting a cost-benefit analysis and
assesses the choice of means of transport and its impacts on travel times,
internal and external costs such as congestion, noise, and air pollution. It
will then be investigated whether negative external effects can be reduced by
the offer of a DRT service as a traffic measure.
Consequently, this paper has two aims. The main goal of this project paper is
to elaborate the technical requirements, i.e. to get to know the multi-agent
transport simulation software MATSim and to perform first simulations. Since
this software is Java-based, the development of Java knowledge is also a
technical requirement. Secondly, further steps for the implementation of a
simulation with real traffic data and the necessary extensions to perform a
cost-benefit analysis are discussed. As a first approximation, only a certain
route is simulated and not the whole city area. The exemplary route is from
the city centre to the Fassberg in Göttingen, as this is the commuting route
for many employees of the Max Planck Institute. This first simulated route
represents the preparation for a traffic simulation for the entire city area.
In the following section, a brief overview of MATSim with the specific
advantages and disadvantages of its application is given. This is followed by
a description of the simulation model. Thereby, the data collection and the
technical aspects for the generation of all relevant information for the
simulation of the baseline situation and the DRT scenario are considered.
Section 4 describes the results of the simulations, particularly the resulting
reaction patterns. Finally, the next steps for further work are presented and
the conclusion is drawn.

##  2 Multi-Agent Transport Simulation

This paper uses the open-source agent-based microsimulation MATSim. In
traffic-related areas, the use of agent-based simulation is becoming
increasingly popular, and several examples of agent-based approaches can be
found in the literature in recent years (Bischoff and Maciejewski, 2016;
Kaddoura et al., 2015). This section provides a general overview of MATSim
(section 2.1), including the specific advantages and disadvantages of its
application (section 2.2). Thereby the chapter tries to be as general as
possible at the beginning. In the following chapters, relevant features and
concrete examples will be discussed in more detail.

###  2.1 What is MATSim?

The traffic simulation MATSim is a Java-based open-source project jointly
developed by the Technical University of Berlin and the ETH Zurich. The MATSim
software allows a microscopic simulation of agent behaviour with high
computing power as well as the possibility to investigate large traffic
scenarios (Bischoff and Maciejewski, 2016). In this context, the term
microsimulation means that each person is simulated as an independent agent
with any number of individual characteristics. MATSim offers a set of modules
that can be adapted or extended according to individual needs:

  * •

Modelling of transport demand

  * •

Agent-based microsimulation

  * •

Iterative evaluation of the daily plans (scoring)

  * •

Variation of daily plans (replanning)

  * •

Methods for evaluating the results

Together, these modules form an integrated optimization system referred to as
the MATSim Circle. This optimization system is based on a co-evolutionary
principle. Each agent repeatedly optimizes a daily activity plan, competing
for space-time slots with all other agents. This co-evolutionary principle is
implemented in MATSim as a loop. It begins with the modelling of an initial
demand, which results from the daily activity chains of the persons in the
study area. In MATSim these modeled persons are referred to as agents. The
activity chains are usually derived from empirical data or discrete decision
modelling. Agents’ responsiveness to other agents competing for space-time
slots enables them to individually optimize the initial demand during the
iterations111The simultaneous simulation of all agents leads to interactions,
thus longer travel times or even traffic jams can be modelled.. Thereby each
agent has a memory with a fixed number of daily plans, whereby each plan
consists of a daily activity chain and a corresponding score. The score can be
interpreted as an economic utility. During all iterations, every agent selects
a plan from its memory. This selection depends on the scoring of the plans,
which are calculated after each run based on the performance of the executed
plans. A specified share of agents can copy the selected plan and change the
duplicate, which is the replanning phase of the loop. Replanning is possible
for different dimensions. There is the possibility to change the travel time,
the route, the modes of transport and the destination. Besides, there are
various replanning strategies for adapting plans, ranging from random
mutations to approximate suggestions to best-response answers. Each iteration
ends with the evaluation of the agents’ experiences from the selected daily
plans (scoring). The applied scoring function assigns a positive utility for
the performance of activities and a negative utility for traveling, monetary
costs such as tolls or fares and late arrival or early departure. However, the
scoring function can be changed and thus customized to the individual needs of
the user (Horni et al., 2016). This repetitive process typically leads to an
approximate stochastic user equilibrium (Nagel and Flötteröd, 2012). Finally,
MATSim contains some methods for evaluating the results directly out of the
box. However, most analyses are very specific and must be implemented
according to the user’s requirements. Figure 1 represents the loop described
above with the configurable number of iterations that a MATSim run consists
of.

Figure 1: The building blocks of MATSim (Horni et al., 2016)

The basic input data for a MATSim simulation are on the one hand a street
graph222The underlying structure of a street graph can be modelled
mathematically as a graph, thus graph theory is applied in the broadest sense.
that maps the relevant infrastructure of the study area and on the other hand
the initial traffic demand already mentioned above. Beyond these minimum
requirements, additional data can be added to the model to improve detail
accuracy (Horni et al., 2016). The model described in this paper also contains
data exceeding the minimum requirements. Details of the additional data used
are described in section 3.

###  2.2 Why using MATSim?

After the functionality of MATSim was presented above, the advantages and
disadvantages of using this software can be summarized. The conceptual
approach of MATSim differs fundamentally from conventional traffic simulation
software. While traditional simulation software only assigns an aggregated
demand to the respective traffic network and thus only static flows on the
routes can be represented in predefined time intervals, the microscopic,
behavior-based approach of MATSim enables a much lower spatial, individual and
time-dependent analysis (Kickhöfer, 2009). Thus, it can be concluded that
through the individual simulation of each traveler, agent-based modeling can
describe the demand side of the model in great detail. This is an advantage
that traditional transport modeling cannot offer (Ciari et al., 2008). Based
on the individual simulation there is another advantage of the MATSim
software. Namely, the consideration of behavioural aspects, which can be
relevant in the transport sector, e.g. the choice of route, travel time or
mode of transport (Rieser, 2010). Behavioural rules for agents are defined at
the individual level, but the overall response to different scenarios is the
result of both the individual response to such measures and the interaction
between all simulated individuals (Ciari et al., 2008). Because of the
representation of the agents with attributes and a certain logic it is a very
intuitive possibility to simulate the traffic. Another factor is that the
evaluation of the profitability of DRT providers requires a microsimulation.
Aggregation-based methods are unsuitable because the average demand ignores
the effects of the distribution in the temporal and/or spatial dimension. In
addition, the agent paradigm can also be expanded to the supply side of the
transport system, allowing an even more comprehensive representation of the
overall system (Cich et al., 2017). Altogether, MATSim is able to
realistically map the dynamic traffic volume due to the described advantages,
provided that the necessary traffic data is available. Furthermore, it should
be noted that MATSim is a fast dynamic simulation software with high computing
power even for large traffic scenarios(Agarwal et al., 2019). MATSim also
offers high adaptability due to its modular design. There are many plug-in
points to extend functionality. The software can be extended by already
written open source plugins333In these cases, there is often good
documentation. as well as by self-written algorithms. One drawback, however,
is that there is no easy to use interface for the self-written customization,
but very good Java knowledge is required to make all individual customization.
The main disadvantage, however, is the implementation effort, which increases
significantly with each new detail, i.e. with an increasing level of detail
and accuracy of the mapping.

##  3 Simulation Model for Göttingen

As already mentioned, this project paper is intended to prepare the long-term
goal of traffic simulation and the evaluation of the offer of a DRT service in
Göttingen and the surrounding area. For this reason, the road network of
Göttingen, Bovenden, and Rosdorf has been selected as the data to be used for
the forthcoming work will cover this region of interest. These data come from
a household survey on mobility behaviour conducted by the City of Göttingen in
2015/2016 (LK Argus, 2016). This included the availability of means of
transport, the purposes of the journeys made, the time distribution of
journeys over the day and much more. All this information is valuable input
data for the transport simulation and makes it possible to investigate whether
the simulation carried out is representative and thus close to the actual
traffic conditions in Göttingen. This section describes the simulation of the
exemplary route from the city centre to the Fassberg in Göttingen which was
chosen to get to know the technical requirements for a simulation with MATSim.
Since only a small part of the population was simulated, there were no
requirements to represent the population completely or representative for this
project. In the following, the collection of the necessary input data for the
traffic model is described. Subsequently, the baseline scenario, as well as
the DRT scenario, are presented. The focus continues to be on the technical
aspects.

###  3.1 Data collection

During the first phase of the project, the necessary database for the creation
of an agent-based traffic model was developed. In general, the transport model
consists of two components, transport supply, and transport demand. The
transport supply is represented by the transport network and the transport
infrastructure (e.g. public transport lines). Traffic demand is represented by
mobility data or agent activity plans. OpenStreetMap (OSM) data served as the
foundation for mapping the road network of Göttingen, Rosdorf and Bovenden.
These data were supplemented by local knowledge of the road network with the
program JOSM - Java OpenStreetMap Editor. For OSM data to be suitable for
routing by MATSim, it must be converted to a xml data format. MATSim provides
the Java class OsmNetworkReader for this purpose. This reader takes the OSM
data and generates a MATSim network according to predefined settings. In
addition, the simulation software uses a NetworkCleaner and a
NetworkSimplifier to make the network usable for a simulation and to reduce
the computational effort. When creating the MATSim road network, Gauß-Krüger
coordinates were used because the MATSim community recommends using a regional
coordinate system and Gauß-Krüger is mainly used in Germany (Horni et al.,
2016). After this conversion, the network contains 7863 road links and 3680
nodes. A representation of the network is illustrated in figure 2.
In order to map public transport, GTFS444General Transit Feed Specification is
a widely used format for public transport schedules and geographical
information. data was necessary. These data were provided by a subcontractor
of the transport authority in Göttingen. The collected data were used by the
MATSim package PT2MATSim. The exact description of this process is explained
in section 3.2. The data on transport demand will, in the long run, be based
on a household survey on mobility behaviour in Göttingen carried out by the
City of Göttingen in 2015/2016, which was made available for the project.
However, since only one exemplary route is simulated for this project, the
transport demand was generated randomly, so that the population modelling is
initially only approximate. Thus, there was no requirement for completeness or
representativity. The modelling of transport demand is discussed in greater
detail in section 3.2.

Figure 2: Visualization of the Göttingen Network

###  3.2 Baseline Situation

A crucial step in assessing the benefits and costs of a transport project is
to define the baseline situation. In the long term, the current traffic
situation will represent the baseline scenario. However, for this paper, the
base case is the commuting scenario from the city centre to the Fassberg where
only the car and the public transport is allowed. As already mentioned in
section 2.1, there are some basic input data for MATSim: the street graph and
the initial transport demand. It is already explained in the previous section
how to configure the road network for simulation. This transport network data
contains information about the capacities, maximum speeds, link length and
permitted means of transport on the individual routes. In order to map the
base case of road traffic in Göttingen correctly, it was necessary to create a
multimodal road network on which public transport can also run. Apart from the
multimodal road network, some other input data beyond the basic input data is
necessary to simulate the public transport in MATSim. Essentially, there are
two relevant input files. The first is the TransitSchedule, which contains all
public transport lines, routes, stops and departures. It describes all planned
trips of public transport vehicles in the system. Thereby, each vehicle is
simulated separately in the traffic flow simulation. Time delays may occur for
transit vehicles, and actual departures may differ from the planned schedule,
depending on the type of vehicle and the number of passengers boarding and
alighting. The second file is the TransitVehicles, which contains all vehicle
types and all vehicles actually used. As mentioned above, the PT2MATSim
package developed by Poletti, (2016) was used to generate these necessary
input data for public transport. It is based on an algorithm that makes it
possible to see the effects of the interaction of buses and motorized
individual traffic in a traffic simulation. The implementation provides tools
to convert GTFS data into MATSim transit schedules and to create a multimodal
MATSim network with OSM. Therefore, as a first step, the GTFS converter was
used. The converter’s main job is transforming GTFS trips to MATSim transit
routes. For this purpose, the coordinates of the stops are extracted from the
GTFS data. The converter is called by the Java main method
Gtfs2TransitSchedule, followed by the folder where the GTFS files are located.
Afterwards, the date for which the routes are to be extracted is
specified555MATSim normally simulates only one day, so a sample day was
extracted from the GTFS feed.. Then the output coordinate system666EPSG codes
are a widely used means to clearly indicate the spatial reference of geodata.
EPSG:31467 is the code for Gauß-Krüger coordinates. and lastly the output
files of the transit plan and the transit vehicles are specified. The code for
the implementation of the GTFS converter is presented in listing 1.

⬇

1 java -cp pt2matsim-19.2-shaded.jar

2 org.matsim.pt2matsim.run.Gtfs2TransitSchedule

3 "path to the GTFS data"

4 "date" "EPSG:code"

5 "path to output transit schedule"

6 "path to output transit vehicles"

Listing 1: Gtfs2TransitSchedule

For this paper, only the schedules for the bus lines 21, 22 and 23 in
Göttingen were converted, because only these connect the inner city with the
Fassberg and are therefore relevant for the exemplary route. However, those
TransitSchedules are ’unmapped’ after using the GTFS converter, which means
that the schedules do not contain information about the links used by the
vehicles. They simply include the stop order for transit routes. The creation
of these links (i.e. the route taken by a vehicle in a network) is called
’mapping’, a process performed by the PublicTransitMapper. It implements an
algorithm that compiles a series of link candidates for each stop by selecting
the nearest n links from the coordinates of the stop. From these, the
algorithm calculates the least cost path from the transit route’s first to its
last stop777The least cost path is calculated from the path travel cost plus
half the travel costs of the two link candidates the path connects. Then, the
travel cost between the candidates (i.e. the travel cost on the link) is
multiplied by four. This is done to avoid the same link pointing to two stops.
The travel cost on a network link is normally length or travel time (Poletti,
2016).. In the case that no link candidate can be found within the predefined
distance an artificial loop connection is created since all stops need to be
referenced to a link for the simulation to work (Poletti, 2016). This
situation occurred in the base case of this project as the OSM data for the
Göttingen inner city was not complete. This is probably due to the fact that
the city centre is mostly car-free. The resulting artificial links, however,
represent the centre’s road network very well. The reason for this is that the
density of the stops in this area is fairly high and the stops are relatively
close to each other. To map a transit schedule to a MATSim network, a
configuration file with all input parameters is necessary. The code for the
creation of a default configuration file is shown in listing 2.

⬇

1java -cp pt2matsim-19.2-shaded.jar

2org.matsim.pt2matsim.run.CreateDefaultPTMapperConfig

3"path to the default configuration file"

Listing 2: Creation of a default configuration file

In this configuration file, the TransitSchedule file created by the converter
as well as the network file must be integrated as input files. The output
files also need to be specified. Afterwards, the schedule can be mapped to the
network using the PublicTransitMapper. This is done by executing the code from
listing 3.

⬇

1java -cp pt2matsim-19.2-shaded.jar

2org.matsim.pt2matsim.run.PublicTransitMapper

3"path to the default configuration file"

Listing 3: PublicTransitMapper

After this process has been successfully executed, all necessary data for the
simulation of public transport has been created. The PT2MATSim package also
offers the possibility to check implausibilities in the transit routes.
Thereby it is checked whether the route has loops and whether the travel times
specified in the TransitSchedule can be achieved on the network on which the
routes are mapped. The plausibility check for the schedule can be executed by
the code specified in listing 4. The plausibility check is called by the Java
main method CheckMappedSchedulePlausibility. Subsequently, the schedule and
the network files are determined. Then the coordinate system and finally the
output folder are specified.

⬇

1java -cp pt2matsim-19.2-shaded.jar

2org.matsim.pt2matsim.run.CheckMappedSchedulePlausibility

3"path to transit schedule"

4"path to network file" "EPSG:code"

5"path to output folder"

Listing 4: Schedule plausibility check

Finally, the modeling of transport demand is necessary. The population data
set may include a wide range of socio-economic characteristics such as place
of residence, income, place of work, among others. Depending on the
availability and resolution of the data, this module differs significantly
from one scenario to another (Kickhöfer, 2009). As already mentioned, only a
small part of the population was simulated. Socio-economic characteristics,
except for the place of residence and work, were initially omitted, as these
are only additional details that make the model more realistic but are not
relevant for the technical implementation. Since about 1200 employees work at
the Max-Planck-Institut at the Fassberg and 14.1 percent of the population
live in the city centre of Göttingen, 169 pseudo agents are simulated who
commute from the city centre to the Fassberg. These agents either use the car
or public transport to get to work in the baseline situation. Other
activities, such as shopping, were not simulated for the exemplary route. In
general, however, the implementation of trip chains with MATSim is possible.
Therefore, the population data for this commuting scenario to the Fassberg are
fairly simple but sufficient to realize the objectives of this paper. A subset
of a population file is shown in listing 5. Like the network file, the
population file needs to have a xml data format to be compatible with MATSim.

⬇

1<person id="pers_0">

2 <plan selected="yes">

3 <activity type="h" x="3565270.0"

4 y="5711985.0" end_time="08:02:45" >

5 </activity>

6 <leg mode="car">

7 </leg>

8 <activity type="w" x="3567092.2910817387"

9 y="5714427.227577877" end_time="17:22:48" >

10 </activity>

11 <leg mode="car">

12 </leg>

13 <activity type="h" x="3565270.0" y="5711985.0" >

14 </activity>

15 </plan>

16</person>

17

18<person id="pers_101">

19 <plan selected="yes">

20 <activity type="h" x="3565219.0"

21 y="5711797.0" end_time="08:43:50" >

22 </activity>

23 <leg mode="pt">

24 </leg>

25 <activity type="w" x="3567092.2910817387"

26 y="5714427.227577877" end_time="17:24:05" >

27 </activity>

28 <leg mode="pt">

29 </leg>

30 <activity type="h" x="3565219.0" y="5711797.0" >

31 </activity>

32 </plan>

33</person>

Listing 5: Subset of a population file

In order to validate the baseline scenario, the travel costs using the car and
public transport should be varied. If the demand or the modal split changes
according to the economic theory888According to economic theory, the demand
for a normal good increases when the price decreases. In addition, the
majority of Europeans agree that the best way to improve urban transport is to
reduce the price of public transport (TNS Opinion and DG MOVE, 2013)., it can
be concluded that the baseline situation is plausible. Within the
configuration file, MATSim offers several possibilities to adjust the scoring
of the different means of transport. These include, for example, the marginal
utility of traveling, the marginal utility of distance, and the monetary
distance rate. Since the monetary distance rate describes the monetary costs
per distance and therefore reflects the changes in the prices of the modes of
transport, it was varied to test the baseline scenario. The results are
discussed in Section 4.

###  3.3 DRT Scenario

The above explained baseline situation will be compared with a scenario in
which there is an additional offer of DRT in the Göttingen area. This
comparison is intended to build the foundation for conducting a cost-benefit
analysis and assesses the choice of means of transport and its impacts on
travel times, internal and external costs such as congestion, noise, and air
pollution. It will then be investigated whether negative external effects can
be reduced by the offer of a DRT service as a traffic measure. Achieving these
long-term objectives requires establishing the technical foundations for the
implementation of a DRT system in the MATSim software. The simulation of
dynamic transportation modes like taxis or DRT is modeled by MATSim’s
extension for Dynamic Vehicle Routing Problems (DVRP), which enables the
simulation of mobility on-demand. This allows a dynamic disposition of vehicle
fleets during simulation in response to incoming requests and other events
that differ in their objectives and constraints using pluggable algorithms
(Bischoff et al., 2017). Potential restrictions include, for example, the
maximum waiting time or the maximum travel time. Currently, two modes are
available for simulating DRT services. Either a door-to-door service or a
stop-based service. To implement the DVRP extension in MATSim, some
dependencies must be added to the project object model999The project object
model, named pom.xml in MATSim, stores information for a software project
supported by Maven. This file contains all the information about the software
project.. These dependencies are presented in listing 6.

⬇

1<dependency>

2 <groupId>org.matsim.contrib</groupId>

3 <artifactId>otfvis</artifactId>

4 <version>0.11.0-2018w44</version>

5</dependency>

6

7<dependency>

8 <groupId>org.matsim.contrib</groupId>

9 <artifactId>taxi</artifactId>

10 <version>0.11.0-2018w44</version>

11</dependency>

12

13<dependency>

14 <groupId>org.matsim.contrib</groupId>

15 <artifactId>av</artifactId>

16 <version>0.11.0-2018w44</version>

17</dependency>

18<dependency>

19 <groupId>org.matsim.contrib</groupId>

20 <artifactId>drt</artifactId>

21 <version>0.11.0-2018w44</version>

22</dependency>

23

24<dependency>

25 <groupId>org.matsim.contrib</groupId>

26 <artifactId>dvrp</artifactId>

27 <version>0.11.0-2018w44</version>

28</dependency>

Listing 6: Dependencies for the implementation of DRT

After implementation, some adjustments must be made in the configuration file,
then the agents can use DRT as an additional transport mode. Those adjustments
can be seen in listing 7.

⬇

1<module name="drt">

2<param name="operationalScheme" value="door2door" />

3<param name="stopDuration" value="60.0" />

4<param name="vehiclesFile" value="path to the vehicle file " />

Listing 7: Adjustments for DRT in the configuration file

As becomes apparent from the code, the implementation of a DRT service also
requires a drtvehicles file in which the vehicle fleet is described. In this
file, the departure locations of the vehicles, the operating time101010In
MATSim time is measured in seconds after midnight. and the capacity are
defined. In the framework of this project work, 5 DRT vehicles with 8 seats
were implemented, departing from the university, the main station, and the new
town hall and offering a door-to-door service. The deployed drtvehicles file,
which also needs to have a xml data format, is shown in listing 8.

⬇

1<vehicles>

2 <vehicle id="drt0"
start_link="34945-24004-24005-24006-24007-34944-31821-31822-3182" t_0="0.0"

3 t_1="86400.0" capacity="8"/>

4

5 <vehicle id="drt1" start_link="2790-23646-23647-23648" t_0="0.0"

6 t_1="86400.0" capacity="8"/>

7

8 <vehicle id="drt2" start_link="35051-35052-35100" t_0="0.0"

9 t_1="86400.0" capacity="8"/>

10

11 <vehicle id="drt3"
start_link="35044-35045-35046-34990-34991-34992-34993-34994-3499" t_0="0.0"

12 t_1="86400.0" capacity="8"/>

13

14 <vehicle id="drt4" start_link="14899-14901-14903-5087-5089" t_0="0.0"

15 t_1="86400.0" capacity="8"/>

16</vehicles>

Listing 8: drtvehicle.xml

Apart from the implementation of the DRT service, the requirements for the DRT
scenario remain the same as for the baseline situation. However, different
demands were generated for this scenario to compare DRT usage for different
market shares. The simulation of a new launch of the service is compared with
a situation in which the modal split of DRT usage is already 30 percent. Hence
it will be derived on which means of transport the agents switch when
introducing a DRT service and whether the agents make other decisions
regarding the means of transport if there already exists a DRT service. These
and other relevant results are discussed in the following section.

##  4 Results

This section examines the results of the simulation of the exemplary commuter
route. Thereby, transport mode statistics, the variation of the monetary
distance rate and the different initial demands of the DRT scenario are
discussed.
First of all, it is important to consider the preliminary transport mode
statistics of the baseline situation. The transport demand was generated in
such a way that at the beginning of the first iteration that simulates the
baseline scenario, 65 percent of the transport demand is accounted for by car.
Accordingly, the modal split for public transport is 35 percent. Figure 3
illustrates the initial modal split as well as the adjustments of the agents
during the 10 iterations of the simulation. In the baseline scenario, more
than 10 percent of agents switch from public transport to car usage. It is
important to note that in this illustrative example all agents have access to
all means of transport and can, therefore, change to using a car at any time
without barriers. Furthermore, in this illustrative simulation, only 169
agents are on the road network, which means that the probability of traffic
jams is very low. Thus, the scores of the plans where the car is used are much
higher because there are no waiting times when using the car. These changes in
transport mode statistics are therefore plausible.

Figure 3: Initial mode statistic

As already described in section 3.2, the monetary distance rate was varied to
examine the impact of price changes on the choice of means of transport in the
baseline situation. This was achieved by running four simulations using
different monetary distance rates, which are illustrated in table 1. In the
first run, no monetary distance rates are implemented for public transport as
well as for the car. The corresponding transport mode statistic is plotted in
figure 3. In the second and third runs, the price for the distance driven by
car was increased. Primarily, these runs served to get a first impression of
the resulting reaction patterns. Although the monetary distance rate for the
car in the third run increases a hundredfold compared to the second run, the
modal split only changes slightly. This suggests that the main determinant is
the relationship between the monetary distance rates of different means of
transport. Consequently, in the fourth run, the monetary distance rate of the
car is ten times higher than for public transport. Overall, these changes in
prices have altered the choice of the modes of transport as predicted by
economic theory. The baseline situation can therefore generally be regarded as
plausible. It is also interesting to note that the scores of the plans for the
two modes of transport are beginning to converge due to changes in the
monetary distance.

iteration | run
---|---
| 1 | 2 | 3 | 4
| monetary
---
distance
rate:
| car
---
0
| pt
---
0
| car
---
-0.2
| pt
---
0
| car
---
-0.002
| pt
---
0
| car
---
-0.00154
| pt
---
-0.000154
0 | 65 % | 35 % | 65 % | 35 % | 65 % | 35 % | 65 % | 35 %
1 | 62 % | 38 % | 62 % | 38 % | 62 % | 38 % | 62 % | 38 %
2 | 63 % | 37 % | 58 % | 42 % | 58 % | 42 % | 59 % | 41 %
3 | 65 % | 35 % | 52 % | 48 % | 54 % | 46 % | 57 % | 43 %
4 | 70 % | 30 % | 49 % | 51 % | 51 % | 49 % | 55 % | 45 %
5 | 71 % | 29 % | 45 % | 55 % | 47 % | 53 % | 51 % | 49 %
6 | 75 % | 25 % | 42 % | 58 % | 44 % | 56 % | 49 % | 51 %
7 | 75 % | 25 % | 39 % | 61 % | 42 % | 58 % | 48 % | 52 %
8 | 72 % | 28 % | 38 % | 62 % | 42 % | 58 % | 48 % | 52 %
9 | 78 % | 22 % | 38 % | 62 % | 42 % | 58 % | 48 % | 52 %
10 | 76 % | 24 % | 34 % | 66 % | 36 % | 64 % | 45 % | 55 %

Table 1: Simulation runs with different monetary distance rates

Finally, the results of the DRT scenario simulation should be considered more
closely. As previously mentioned, two different demands were generated for the
scenario to compare the usage of DRT for different market shares. The results
are summarized in table 2. First of all, it needs to be pointed out that, in
the situation of a new introduction of a DRT service, the users of the public
transport system are primarily switching to the door-to-door offer. Compared
to the situation without the provision of DRT, the share of public transport
in the modal split is even reduced by two percent. Moreover, in contrast to
the situation without DRT, car traffic does not increase over the ten
iterations of the simulation. Due to the introduction of the new DRT service
there are altogether fifteen cars less on the network111111For comparison, run
1 of the baseline situation is considered, since in the DRT scenario the
monetary distance rate for all means of transport is set to zero.. Similarly,
in a situation where DRT already has a 30 percent share of the modal split,
there are fewer cars on the network. It is interesting to note that this
simulation reveals the smallest fluctuations in the share of DRT and that
public transport users are more likely to switch to cars than to DRT. The
scores obtained for the tours covered with DRT are very close to the scores
for the tours covered by cars. However, the majority of agents decide for car
usage, as it often performs slightly better. It can, therefore, be assumed
that the mode choice will be different if the appropriate costs are properly
integrated into the software. This significant implementation and other
important next steps for a realistic traffic simulation are explained in the
next section.

iteration | Initially no DRT |  | Initial share
---
of about 30 % DRT
| car | DRT | pt | car | DRT | pt
0 | 65 % | 0 % | 35 % | 45 % | 29 % | 26 %
1 | 61 % | 4 % | 35 % | 42 % | 31 % | 28 %
2 | 59 % | 9 % | 32 % | 43 % | 29 % | 28 %
3 | 61 % | 9 % | 31 % | 44 % | 32 % | 24 %
4 | 63 % | 9 % | 28 % | 51 % | 27 % | 22 %
5 | 62 % | 13 % | 25 % | 48 % | 32 % | 19 %
6 | 67 % | 12 % | 21 % | 56 % | 27 % | 17 %
7 | 65 % | 15 % | 20 % | 56 % | 27 % | 17 %
8 | 64 % | 14 % | 22 % | 54 % | 28 % | 18 %
9 | 68 % | 15 % | 17 % | 54 % | 30 % | 16 %
10 | 64 % | 14 % | 22 % | 59 % | 26 % | 15 %
Table 2: Simulation runs with different initial shares of DRT usage

##  5 Next steps

This section describes the necessary extensions and the next steps for
comprehensive traffic simulation and a subsequent cost-benefit analysis of a
DRT offer in Göttingen and the surrounding area. Within the scope of a next
paper, multi-agent simulations based on real survey data from Göttingen,
Bovenden and Rosdorf will be carried out. Therefore, it becomes necessary to
generate agent activity plans from mobility data. There are different
approaches for generating a synthetic agent population with socio-economic
data for each agent by integrating different data sources. Related work aimed
at generating a close-to-reality population is often based on iterative
proportional fitting designs with additional Monte Carlo sampling. Examples of
this are studies by Müller and Axhausen, (2011) and Zhu and Ferreira, (2014).
A further approach regarding the generation of synthetic population is the
application of the concept of discrete choice models as in Anderson et al.,
(2014). Besides the generation of a synthetic agent population, the
implementation of the bicycle as a transportation mode is very important to
derive a realistic traffic forecast, as the current city-wide modal share of
cycling ranges at 28 % in Göttingen (LK Argus, 2016). Therefore, it is
necessary to extend the MATSim framework with cycling as a means of transport.
A proposal for implementation is made by Ziemke et al., (2017). In addition,
for a cost-benefit analysis, it is necessary to integrate some extensions of
the MATSim software. An essential feature of the analysis is the monetization
of negative external effects such as emissions. Therefore, it is necessary,
for example, to implement the emission package for MATSim. Thereby emissions
are calculated every time the vehicle leaves the road and costs vary for
different engine types, ages, displacement, and traffic conditions, e.g. free
flow or congestion (MATSim, 2019). In addition, the time required by motorists
to find a parking lot should be incorporated, as otherwise the costs of
driving would not be fully reflected (Horni et al., 2013). Other factors
relevant for the cost-benefit analysis, such as travel time, waiting time, and
internal costs such as fuel or ticket prices for the various means of
transport, can be integrated into the scoring function as already indicated in
this project work. For a meaningful calibration of the scoring function
parameters of a mode choice model can be used. Finally, it would be helpful to
extend the software with some analytical tools. The only statistic that comes
out of the box is the mode statistic, but a comparison of the scores, the
travel times and the traveled distances for the different means of transport
would be very helpful and important for a comprehensive analysis of the topic.
A comprehensive cost-benefit analysis of a DRT offer should be possible by
adding these crucial next steps to the simulation scenarios. Accordingly, the
conclusion can be drawn.

##  6 Conclusion

This paper investigates the requirements of the technical implementation of
cost-benefit analysis with the transport simulation software MATSim. As an
innovative tool, such simulations could effectively support the work of
planning authorities in the design of transport projects in the future. For
example, it could be investigated what proportion of the population would
support the project under consideration. To test the method of traffic
simulation a small illustrative scenario was designed before applying it to a
real world scenario in an upcoming study. It was shown how to set up a basic
simulation and how this can be extended with public transport and DRT.
Besides, it was demonstrated how the scenarios could be varied, for example by
the monetary distance rates of different means of transport. Finally, the
results were presented and the necessary next steps were explained. The
presented approach should be seen as a demonstration of the capabilities of
the multi-agent transport simulation software MATSim. The possibilities of the
use are manifold and seem to be arbitrarily expandable. Altogether it can be
concluded that the software is suitable for a cost-benefit analysis of a DRT
service. Above all, the most important internal and external costs, such as
usage costs of the various modes of transport and emissions, can be integrated
into the simulation scenarios, enabling an evaluation of the costs and
benefits of a DRT service when using mobile data from Göttingen and the
surrounding area.

## Acknowledgements

We gratefully acknowledge the funding from the European Union’s Horizon 2020
research and innovation programme under the Marie Skłodowska-Curie grant
agreement No 800267.

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