Agent-Based Modeling of Traffic Behavior in Growing Metropolitan Areas

Available online at www.sciencedirect.com ScienceDirect

Transportation Research Procedia 10 ( 2015 ) 306 – 315

18th Euro Working Group on Transportation, EWGT 2015, 14-16 July 2015, Delft, The Netherlands Agent-based modeling of traffic behavior in growing metropolitan areas

Karsten Hager a,*, Jürgen Rauh b, Wolfgang Rid a,c

aInstitute of Urban Planning and Design, University of Stuttgart, Keplerstraße 11, 70174 Stuttgart bInstitute of Geography and Geology, University of Würzburg, Am Hubland, 97074 Würzburg cDepartment Urban and Spatial Planning, University of Applied Sciences Erfurt, Schlüterstraße 1, 99089 Erfurt

Abstract

The urban settlement development of the past centuries was characterized by the process of suburbanization. Cur- rently, the process of (re-) urbanization in metropolitan areas leads to population growth. This increase results in even more traffic participants in highly condensed areas and thus challenging the urban mobility system. Capacity and frequency of service of public transportation, a city’s layout and road capacities constrain urban traffic. Transi- tions in travel behavior in Germany (e.g. less young adults consider cars as status symbols, and thus car ownership decreases) as well as the introduction of new types of mobility, such as sharing systems and electric mobility exac- erbate the challenges for the future urban mobility. Both an expansion and modernization of the transportation system and an intelligent shift of traffic streams will help to overcome those challenges. Traffic simulation software is naturally used to derive results about public transportation in metropolitan areas. With the help of agent-based modeling, however, human behavior in a certain research area can be modeled according to pre-defined rules and variables. Not every single inhabitant of the metropolitan area is represented by traffic simulation software, whereas in agent-based modeling all inhabitants will be featured in the model. Agent-based modeling is used for scenarios of the future traffic behavior and the ability to easily adapt to new constraints in the general framework of the model. The metropolitan area of Stuttgart is used as case study and MATSim is used as agent modeling software. Following the model setup, different scenarios are evaluated. The main research question focuses on the alteration of the urban mobility system when newly-built residential or industrial areas or rehabilitated areas are connected to the transport system. First results regarding the demographic development of Stuttgart are presented as well as the current status of the model itself.

* Corresponding author. Tel.: +49-(0)711-685-83352 E-mail address: [email protected]

2352-1465 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Delft University of Technology doi: 10.1016/j.trpro.2015.09.080 Karsten Hager et al. / Transportation Research Procedia 10 ( 2015 ) 306 – 315 307

©© 2015 201 5The The Authors. Authors. Published Published by Elsevierby Elsevier B.V. B.This V. is an open access article under the CC BY-NC-ND license (Selectionhttp://creativecommons.org/licenses/by-nc-nd/4.0/ and peer-review under responsibility). of Delft University of Technology. Peer-review under responsibility of Delft University of Technology Keywords: agent-based modeling, traffic simulation, urban structure

1. Introduction

Metropolitan areas are growing due to the constant population increase caused by the process or urbanization. This process is followed by an increasing demand for mobility in cities (Wegener 2013). Additionally, demographic change, commuter traffic and the tendency towards smaller households is altering urban mobility demands. An analysis of the urban mobility system from only one perspective falls short because of the interdisciplinary nature of mobility (Bläser and Hellali-Milani 2014). Thus, from a theoretical point of view, at least four different aspects need to be considered for a holistic study of the urban mobility system: demography, transportation system, urban structure and travel behavior. Elementary attributes of the population from a demographic perspective are: age distribution, growth or contrac- tion processes, the percentage of working population and the development of residential areas with respect to the price levels and location. Because of the continuous flow of people relocating to or leaving a metropolitan area, these attributes are fluctuating. However, newly-arrived inhabitants alter the existing mobility routines throughout the research area, especially in metropolitan areas (Scheiner 2009). Information about urban area and housing development are essential for a comprehensive analysis of the demographic structure variables describing the population (Gaube and Remesch 2013). Either transportation itself or the constraints of the transportation network are boundaries of the urban mobility system. Transportation is limited in its capacities and its structure because it is utilized by several different user groups with every user group having their own needs (commuters, bicycle tourists, elderly people, etc.) (Schlaich 2011). The transport network influences the traffic itself in various ways: e.g. improved access to stops, promotion of multi- and intermodality, increase of the transportation frequency, increase of transportation lanes and reduction of access and egress times (Legara et al. 2014). Hubs and crossings, for example, are formed by the built-up area. Thus, urban structures set the constraints for the development and control of mobility. Metropolitan areas, in particular, are continuously transitioned by, in most cases, a population increase. However, there is no generally valid mobility solution for the whole metropolitan area because of the heterogeneous inhabitant constitution. Density, compactness and mixed use development were identified to be the driving forces of urban structures (Batty 2012). Additionally, urban structure is connected to travel distances (Holz-Rau et al. 2014) as well as travel behavior (Næss 2011). Human travel behavior is influenced strongly by psychology. Parameters such as price or availability of transportation do not sufficiently enough describe travel behavior. Comfort or ecological awareness, for example, are rele- vant to travel mode choice as well. In summary, it can be stated that travel behavior combines subjective and objective decisions. Thus the inclusion of travel behavior in a comprehensive analysis with demography, transportation and urban structures (which are only based on hard facts) requires careful decisions on the selection of measurement methods (Anable 2005). This study focuses on the alteration of the urban mobility system due to population growth and thus a higher number of traffic participants. The consequences for the urban mobility system differ significantly depend on the traffic modes used (e.g. an additional commuter using his/her own car requires more space than a commuter using public transportation). Also, the location of workstations in the urban area or other activities shape the flow of traffic. The traffic modes utilized in this study are: car, bike, walk and public transportation (bus, tram and train). Ini- tially, the presentation of the intended materials and methods occurs, followed by firsr results regarding the research question and the research area. Lastly, a discussion of the presented approach takes place considering possible alter- native research attempts, potential add-ons for the MATSim model as well as possible first implications of the model for the city of Stuttgart.

308 Karsten Hager et al. / Transportation Research Procedia 10 ( 2015 ) 306 – 315

2. Materials and Methods

To conduct a study about the urban mobility system in its entire dynamic structure, the research approach needs to combine all above-mentioned perspectives in a certain way. Therefore, agent-based modeling is utilized as research method in this study. A definition of agent-based mode ling is provided by Axtell (2000): “An agent-based model consists of individual agents, commonly implemented in software as objects. Agent objects have states and rules of behavior. Running such a model simply amounts to instantiating an agent population, letting the agents interact, and monitoring what happens”. Instead of utilizing traffic flows to represent mobility, as traffic modeling software (e.g. VISUM) does, agent-based modeling represents every traffic participant as agent and is capable of using a specific time schedule for certain activities for each agent. Agent-based modeling in transportation research is used to efficiently reproduce human decision making and travel behavior for a certain research objective (Bazzan and Klügl 2014). The importance of human decision making in this study’s objective therefore leads to the choice of agent-based modeling as research approach. A variety of software packages for agent-based modeling in transportation research exists (Bazzan and Klügl 2014). Considering the functionalities of each available package, MATSim is chosen as software for this study. The main core of MATSim works with a four-step process structure of scenario creation, initial individual demand modeling, demand optimization and statistical analysis (Balmer et al. 2009). Agents choose their activities based on an activity-based demand generation utilizing genetic algorithms. A scoring function is then applied for the final plan selection utilizing activities like home, work or leisure (Charypar and Nagel 2005). Agents perform their daily activity plan on integrated network data with time as dependent variable. Due to its modular programming structure, several different extensions have been programmed for MATSim enabling additional use-case-scenarios with the possibility of Within-Day-Replanning (Dobler 2009), to allow agents multimodal trips (Dobler et al. 2014), of the usage of public transportation (Neumann and Nagel 2013) as well as the inclusion of waiting and walking times for access and egress procedures using public transportation (Ordóñez Medina and Erath 2013). Furthermore, different transport modes require different network topologies based on their layout, their average speed and their capacity (Vitins 2014). The utilization of MATSim for large-scale scenarios is proven (Meister et al. 2010). The foundation of the model depends on various data sources listed in Table 1. All data are currently converted into filetypes recognizeable by MATSim. From a traffic point of view, the existing data are adequate to set up the model in MATSim. Additional data for locating the activities over the research area are presently acquired. The model is validated using data from household surveys about travel behavior from the City of Stuttgart in 2010. Data from the statistical department of Stuttgart are used for modelling the initial situation and for theoretically deducing the research question.

Table 1. List of data available for the implementation in the agent-based model Data sources Traffic model Household survey Statistical depart- Other data sources Stuttgart Region Stuttgart Region ment Stuttgart (e.g. IREUS, IVLZ) List of available Traffic nodes, residents and Vital statistics, POIs, activities, data from each traffic routes, children, number population counting stations, data source POIs, counting of cars, sex, date gain/loss, migra- modal split values stations, stopping of birth, education, tion movement, points for all workplace, availa- sex, age, income, modes, PT routes, bility of traffic household size PT schedules, modes, daily- route-diaries

Karsten Hager et al. / Transportation Research Procedia 10 ( 2015 ) 306 – 315 309

Fig. 1. Illustration of the research design based on an exemplary activity chain (different colors imply different traffic modes used)

Fig. 1 represents an illustration of the research design based on an exemplary activity chain (home, work, leisure/shopping, home). T0 marks the initial situation: an employee leaves his home and walks to his workplace. His route guides him through a park in the middle of a city and he passes it on his way to work. In the second point of time t1, a new block of buildings was built on the location of the park, the installation of a new bus station followed the realization of new buildings. As a consequence, the employee must change his daily travel behavior because he is not able to use his former route – the ground plan changed. The employee might choose one of three different mapped situations. In situation (a) the employee is walking to his workplace regardless the building measure, but his route requires more time and length. Situation (b) pictures the employee walking from his home to the newly- installed bus station and utilizing the bus for the rest of the route to his workplace; he is utilizing an additional traffic mode. In situation (c) the employee decides to change his traffic mode for the entire route, thus utilizing his own car for his daily activity chain. Possible reasons are the difference in time and length of the route to his workplace, a poor public transit schedule or even the change of scenery along his way to work. This research is focusing on changes in the ground plan on different spatial scales and their consequences on human travel behavior. Agent- based modelling is the appropriate method to illustrate and evaluate the results. Fig. 1 however is illustrating neither the immigrants in case of a residential development, nor the commuters in case of an industrial development. Addi- tionally, when a certain amount of immigrants or commuters utilize the new buildings as part of their daily activity chain, consequences not only for the near surroundings, but for the whole urban mobility system will occur. We expect different consequences between various ground plans (e.g. checkered pattern against a radial layout) for the urban mobility system as well. The measurement of these effects are additional research questions for this study. As a next step, the agent-based model needs to be specified. Several patterns to describe the functionality and the framework of agent-based models exist. To present initial information about the proposed agent-based model, the ODD-Concept (Overview, Design concepts and Details) has been chosen for this study (Grimm et al. 2006). After the publication of the ODD-Concept, two updates of the framework have been released (Grimm et al. 2010, Müller et al. 2013). First suggestions about the ODD-Concept for our research design will be presented.

310 Karsten Hager et al. / Transportation Research Procedia 10 ( 2015 ) 306 – 315

Overview: The purpose of our model focuses on the alteration of the urban mobility system when newly-built residential or industrial areas or rehabilitated areas are connected to the transport system. We expect it to be a beneficial tool for researchers and urban designers to forecast mobility demands for urban areas. Two types of entities exist in our model: the travelers in the city of Stuttgart (e.g. inhabitants or commuters) and vehicles. Socio-demographic variables characterize the travelers, whereas frequency and capacity define the vehicles. Exogenous factors for the entities are urban structure, ground plans, location of public transport stops as well as their sizing. The City of Stuttgart is the model’s spatial extent, the temporal resolution accounts for one day with time steps of one second. Travelers will utilize the existing circulation areas. Every traveler pursues his personal activity chain (Fig. 1) and chooses between the existing traffic modes to travel on the existing circulation areas. Public transport uses time schedules on their respective areas. Design Concepts: The theoretical background consists of urban morphology and population growth and their respective effects on the urban mobility system. Agents choose their daily activity chain based on a utility function which is implemented in MATSim (Balmer et al. 2009) Individual decision-making consists of route, time, location and mode choice. The decision-making basis is the utility function, afterwards the agents move based on a traffic flow simulation (Flötteröd et al. 2011). Agents com- pete with each other for space-time-slots on the circulation areas to pursue their daily activity chain, either utilizing empirical data or discrete-choice modelling (Horni et al. 2015). Agents observe each other and the environment. Activity plans with the highest score in the utility function are chosen (Nagel et al. 2015). Agents react to traffic situations and choose to drop certain activities in their daily plan after recognizing they will be too late at the location of their next activity (replanning). Agents utilize their newly-acquired knowledge for future activity plans. The individual sensing of agents consists of the grasp and utilization of each traffic mode. Agents know the location of stopping points and their travel choices affect the movement of other agents. When agents choose to alter their activity chain, it might guide to worse results (time and/or length) than before. The traffic model is based on the first-in-first-out-principle (Flötteröd et al. 2011). As soon as agents predict are not able to stick with their chosen plans, they search for alternatives. In that case, plans with the next highest score will be used (Nagel et al. 2015). The results of this process may be erroneous. Agents perceive their environment in different ways. Interaction between agents and additional information (traffic news) is possible (Dobler 2009). Trains, busses and cars with several passengers are collectives. Agents are heterogeneous based on socio- demographic data and their daily activity chain. The utility function consists of several variables, who differ between agents, thus leading to heterogeneous decision-making. Initialization of the agents and the assignment of the daily activity chain is partly random. The observation of the model’s results conduct to several different possible analyses: variances in the modal split of the research area, congestion effects, distribution of traffic flows. Scenario-analysis are the calculation method for this model. Details: The goal is to set up an agent-based model using MATSim for the city of Stuttgart. The initialization utilizes the current modal split values of the city of Stuttgart and endures one day. The model’s input data is listed in Table 1. The modular structure of MATSim permits the utilization of additional modules. The following modules will be included in our agent-based model: within-day replanning, public transport, multi-modal contribution, parking, signals and lanes, cadyts and matrix-based pt router (Horni and Nagel 2015).

In summary, the functionalities of the existing MATSim version cover most of the required features for this study. Fig. 1 represents the proposed research design and its anticipated fields of impact. Following this, we presented a concept for agent-based models. Before finally setting up the model, demographic data need to be utilized to validate the hypothesis of this study that the City of Stuttgart experienced a significant amount of population growth during the last years. First results regarding the demographic status of the City of Stuttgart are presented.

Karsten Hager et al. / Transportation Research Procedia 10 ( 2015 ) 306 – 315 311

3. Research Area and first results

The data utilized in this chapter is provided by the statistical department of the City of Stuttgart and refers to the period from 2004 to 2014. The City of Stuttgart is located in Baden-Wuerttemberg in the southwest of Germany and has 592898 inhabitants (December 31st 2014). Fig. 2 depicts the population alteration over the past 11 years. Despite 2 years of a slight decrease in the vital statistics (2006 and 2009), the total amount of population rose by nearly 35.000 during this period. Approximately 80 percent of this increase occurred in the last 5 years. This chart demon- strates the significant amount of immigrants who enhanced traffic and strained the urban mobility system. This trend will most likely continue in the future, thus highlighting the necessity of tools in transportation planning to forecast potential immigration residences and their effects on the urban mobility system.

Fig. 2. Vital statistics for the City of Stuttgart (2004-2014) in persons per year (Statistical department of the City of Stuttgart 2015)

After analyzing the vital statistics of the entire City of Stuttgart, the migration movement of the surrounding municipalities is depicted (Table 2). The 4 municipalities of Böblingen, Esslingen, Ludwigsburg and Waiblingen are surrounding the City of Stuttgart. For the past 11 years, 22 % of the immigrants had their last residence in one of the 4 municipalities, whereas nearly 30% of the emigrants from the City of Stuttgart chose one of the 4 municipalities as their future residence. Additionally, over the course of the past 25 years, the city of Stuttgart had a positive commuter proportion of over 130.000 (Statistical department of the City of Stuttgart 2015). These numbers are the result of the attraction of both the number of available jobs in the metropolitan area of Stuttgart. Every third emigrant stays in the metropolitan area of Stuttgart and is only relocating from the city itself to Stuttgart’s suburbia. This leads to an increased demand for mobility due to the raise in the average commuting distance from the surrounding municipalities to the job locations in the city center. All things considered, this development emphasizes the growth of the metropolitan area of Stuttgart, a higher load for the urban mobility system and the need for further tools in transportation planning. Additionally, this trend showcases the importance of the spatial boundary of the model. The City of Stuttgart is chosen as model outline for this study, although the commuter traffic from the surrounding municipalities has an impact on the urban mobility system as well. Therefore, a certain amount of commuters needs to be included into the model.

312 Karsten Hager et al. / Transportation Research Procedia 10 ( 2015 ) 306 – 315

Table 2. Migration movement of the surrounding municipalities with the City of Stuttgart (Statistical department of the City of Stuttgart 2015) Integration of the City of Stuttgart in Germany Immigration Emigration (2004-2014) Surrounding Municipalities (BB, ES, LB, WN) 108282 134656 Total Sum 493291 458155 % 22,0% 29,4%

After discussing the size of and the migration movement between the surrounding municipalities of the City of Stuttgart, the migration movement in the city itself need to be analyzed as well. Fig. 2 represents the net migration gain and loss (in persons per district) for the city districts of Stuttgart. Red and orange colors indicate a large net migration loss, whereas green colors indicate a net migration gain. These data only correspond to inhabitants who are already living in the City of Stuttgart and are relocating within the city. Most of the city districts experiencing a net migration loss are located in the city center. People living in the outskirts of Stuttgart are rarely relocating to the city center, thus leaving the city districts in the center of Stuttgart with a net migration loss.

Table 3. Available socio-demographic data for the city district of Pfaffenwald (Statistical department of the City of Stuttgart 2015) Sex Age groups Household size Income Index 2.155 male 103 persons < 18 2.005 1-person 52 of 100 years households 1.101 female 2.520 persons 18- 510 2-person (100 is the mean 30 years households value for Stuttgart) 413 persons 30-45 74 > 2-person years households 220 persons > 45 years

The University of Stuttgart is located in the city district of Pfaffenwald. It has the highest net migration loss in the southwestern part of the city. Universities attract a powerful number of people from outside the city, consequent- ly leading to a significant amount of immigration, whereas students already living in Stuttgart will most likely not relocate to the vicinity of the university. Table 3 lists additional socio-demographic data for the Pfaffenwald district. The majority of registered people in the Pfaffenwald district are between 18 and 30 years old and live in 1-person households. These data correspond to the assumption of a high amount of students living in the Pfaffenwald district, although only the majority of students register in the local administrative council. Additionally, the income index is only half as high as the average income index from the city of Stuttgart (only 262 registered persons in the Pfaffen- wald district are taxable) which emphasizes our assumption as well. When studies are finished and the students apply to their first job in Stuttgart, they will most likely relocate to an- other city district, thus the university district is left behind with the highest net migration loss. The city districts with the highest net migration gain are situated in the outskirts of Stuttgart. People appear to relocate within the city pref- erably to the outskirts. This effect might be linked to the density of families in the city with people searching for a quieter place to raise the children, whereas in younger ages these people lived near the city center.

Karsten Hager et al. / Transportation Research Procedia 10 ( 2015 ) 306 – 315 313

Fig. 3. Migration movement within the city districts of Stuttgart (2004-2014)

Fig. 3 outlines the tendency of the inhabitants of Stuttgart to relocate to the outskirts of the city again resulting in an enhanced load for the urban mobility system. The average commuting distance is increasing, because of the focal point for jobs in the city center, and the choice of the transport mode utilized for commuting might be changing as well. In conclusion of the demographic analysis of the City of Stuttgart, the migration movement within the city also affects the urban mobility system.

4. Conclusion and further research

The metropolitan area of Stuttgart is attractive for immigrants due to its high amount of jobs in the industrial and service sectors. This trend is expected to continue over the upcoming years and the City of Stuttgart will experience an ongoing population increase. The amount of migration movement in the surrounding municipalities as well as between the city districts needs to be compared with several different equal-sized cities to estimate the impact for a metropolitan area. Also, the amount of the immigration processes for the underlying time period in this study is relatively high compared to the number of inhabitants in the City of Stuttgart. Reference values need to be explored. To summarize, the three different aspects of the (socio-) demographic perspective lead to an increased demand for mobility. The different types of migration movement in metropolitan areas incur various types of mobility, thus straining the urban mobility system in several ways. Urban concepts such as the city of short distances or mixed-use development alter the mobility demand as well. This is in particular the case for newly-built residential areas. Addi- tional research needs to focus on the correlation of the demographic perspective, different urban concepts and their consequences on the urban mobility system. In further research, the MATSim model is deployed. The high resolution-data required for a successful setup of the model is currently being converted to the appropriate data formats. The research design of this study requires a holistic approach for the entire City of Stuttgart which might lead to a lower level of detail for the modeling of existing transport modes (Lämmel et al. 2014). Nevertheless, agent-based modeling and MATSim in particular support an increasing number of extensions for specific research questions. The modeling of urban mobility systems present several possibilities for detailed analysis making agent-based modeling the perfect fit for transportation research. The navigation of traffic flows corresponds in great measure to the successful implementation of traffic signals into 314 Karsten Hager et al. / Transportation Research Procedia 10 ( 2015 ) 306 – 315

the model (Kesting et al. 2008, Płaczek 2014). Changing procedures between transport modes relate in particular with human travel behavior. Access and egress procedures, passenger movement in public transport and also crowd- ing effects need to be taken into consideration for detailed analysis of public transportation in urban mobility systems (Brands et al. 2013, Tlig and Bhouri 2011). Detailed research on pedestrian movement in cities considers crosswalks and traffic signals for example (Torrens 2012). Additional transport modes (sharing systems) and types of car engines (electric, hydrogen) enhance the urban mobility system and enable new types of mobility. The share of these new types of mobility in the current modal split is miniscule, but is expected to increase in the future. Therefore, new types of mobility will not be included in the model, although ongoing research for MATSim takes place (Waraich 2013). Nevertheless, several studies attempt to predict the future urban mobility system and its implications (Jansen et al. 2014). Further research considering travel behavior and joint-decision making has implications on the urban mobility system as well (Dubernet and Axhausen 2014). To project the results of the demographic perspective from a programming point of view, the implementation of the facilities in MATSim deserves special attention. Demonstrating the effects of different urban concepts on the urban mobility system, it is necessary to define certain standards towards facilities e.g. regarding mixed-use development when several different scenarios are conducted. To improve the speed and efficiency of the simulation, MATSim developers suggest to run scenarios with only 10% of the population in the research area. This sample is sufficient for deriving plausible results (Balmer et al. 2009). The calibration of the model is done utilizing survey data about travel behavior in households in the metropolitan area of Stuttgart. When the model is set up, several different scenarios will be conducted. The main research question focuses on newly-built residential or industrial areas or rehabilitated areas and their effects on the urban mobility system. Con- struction projects, which are soon to be realized in the City of Stuttgart, such as the Rosensteinviertel, a daughter element of the new underground main station of Stuttgart (the project is called “Stuttgart 21”), will be used to exam- ine the quality of the model. In cooperation with local authorities, various case studies and also futuristic scenarios are developed. Current and future Bottlenecks for the mobility system are identified and proposals for solution will be provided. The implementation of the agent-based model together with the presented research question provides a useful tool for planning authorities to forecast future demands of urban mobility systems.

References

Anable, J., 2005. ‘Complacent Car Addicts’ or ‘Aspiring Environmentalists’? Identifying travel behaviour segments using attitude theory. Transport Policy 12 (1), 65–78. Axtell, R. 2000. Why agents? On the varied motivations for agent computing in the social sciences. Working paper 17, Center on Social and Economic Dynamics, Washington D.C. Batty, M., 2012. Building a science of cities. Cities 29, 9-16. Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K., 2009. MATSim-T: Architecture and Simulation Times, in “Multi- Agent Systems for Traffic and Transportation Engenieering”. In: Bazzan, A.L.C., Klügl, F. (Ed.). Information Science Reference, Hershey, pp. 57. Bazzan, A., Klügl, F., 2014. A review on agent-based technology for traffic and transportation. The Knowledge Engineering Review 29 (03), 375–403. Bläser, D., Hellali-Milani, S., 2014. Elektromobilität, in „Radikale Innovationen in der Mobilität“. In: Proff, H. (Ed.). Springer, Wiesbaden, pp. 549. Brands, T., de Romph, E., Veitch, T., Cook, J., 2013. Modeling Public Transport Route Choice, with Multiple Access and Egress modes. Transportation Research Procedia 1, 12-23. Charypar, D., Nagel, K., 2005. Generating complete all-day activity plans with genetic algorithms. Transportation 32, 369-397. Dobler, C. 2009. Implementations of Within Day Replanning in MATSim-T. Working paper 598, IVT ETH Zürich. Dobler, C., Lämmel, G., Axhausen, K.W., 2014. Integration of a multi-modal simulation module into a framework for large-scale transport systems simulation, in “Pedestrian and Evacuation Dynamics 2012”. In: Weidmann, U., Kirsch, U., Schreckenberg, M. (Ed.). Springer, Berlin Heidelberg, pp. 739. Dubernet, T., Axhausen, K.W., 2014. Solution Concepts for the Simulation of Household-Level Joint Decision Making in Multi-Agent Travel Simulation Tools. 14th Swiss Transport Research Conference, Monte Verita, Ascona. Flötteröd, G., Chen, Y., Nagel, K., 2011. Behavioral calibration and analysis of a large-scale travel microsimulation. Networks and Spatial Economics 12(4), 481-502. Gaube, V., Remesch, A., 2013. Impact of urban planning on household’s residential decisions: An agent-based simulation model for Vienna. Environmental Modeling & Software 45, 92-103. Karsten Hager et al. / Transportation Research Procedia 10 ( 2015 ) 306 – 315 315

Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S. K., Huse, G., Huth, A., Jepsen, J.U., Jørgensen, C., Mooij, W.M., Müller, B., Pe’er, G., Piou, C., Railsback, S.F., Robbins, A.M., Robbins, M.M., Rossmanith, E., Rüger, N., Strand, E., Souissi, S., Stillman, R.A., Vabø, R., Visser, U., DeAngelis, D.L., 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198 (1-2), 115-126. Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., Railsback, S.F., 2010. The ODD protocol: A review and first update. Ecological Modelling 221 (23), 2760–2768. Holz-Rau, C., Scheiner, J., Sicks, K., 2014. Travel distances in daily travel and long-distance travel: what role is played by urban form? Environment and Planning A 46 (2), 488-507. Horni, A., Nagel, K., 2015. Available Functionality and How to Use It, in “The Multi-Agent Transport Simulation MATSim”. In: Horni, A., Nagel, K., Axhausen, K.W. (Ed.). in print, pp. 55 Horni, A., Nagel, K., Axhausen, K.W., 2015. Introducing MATSim, in “The Multi-Agent Transport Simulation MATSim”. In: Horni, A., Nagel, K., Axhausen, K.W. (Ed.). in print, pp. 3 Jansen, H., Wehmeyer, H., Garde, J., Schmidt, J.A., 2014. Die Zukunft urbaner Mobilität, in „Radikale Innovationen in der Mobilität“. In: Proff, H. (Ed.). Springer, Wiesbaden, pp. 461. Kesting, A., Treiber, M., Helbing, D., 2008. Agents for Traffic Simulation, in “Multi-Agent Systems: Simulation and Applications”. In: Uhrmacher, A., Weyns, D. (Ed.). CRC Press, Boca Raton, pp. 325. Lämmel, G., Steffen, B., Seyfried, A., 2014. Large scale and microscopic: a fast simulation approach for urban areas. TRB 2014. Legara, E.F., Monterola, C., Lee, K.K., Hung, G.G., 2014. Critical capacity, travel time delays and travel time distribution of rapid mass transit systems. Physica A: Statistical Mechanics and its applications 406, 100-106. Meister, K., Balmer, M., Ciari, F., Horni, A., Rieser, M., Waraich, R., Axhausen, K.W., 2010. Large-scale agent-based travel demand optimization applied to Switzerland, including mode choice. 12th World Conference on Transportation Research, Lisbon. Müller, B., Bohn, F., Dreßler, G., Groeneveld, J., Klassert, C., Martin, R., Schlüter, M., Schulze, J., Weise, H., Schwarz, N., 2013. Describing human decisions in agent-based models – ODD + D, an extension of the ODD protocol. Environmental Modelling & Software 48, 37-48. Næss, P., 2011. ‘New urbanism’ or metropolitan-level centralization? The Journal of Transport and Land Use 4 (1), 25-44. Nagel, K., Kickhöfer, B., Horni, A., Charypar, D., 2015. A Closer Look at Scoring, in “The Multi-Agent Transport Simulation MATSim”. In: Horni, A., Nagel, K., Axhausen, K.W. (Ed.). in print, pp. 27. Neumann, A., Nagel, K., 2013. Passenger Agent and Paratransit Operator Reaction to Changes of Service Frequency of a Fixed Train Line. Procedia Computer Science 19, 803-808. Nguyen, Q. T., Bouju, A., Estraillier, P., 2012. Multi-agent Architecture with Space-time Components for the Simulation or Urban Transportation Systems. Procedia – Social and Behavioral Sciences 54 (2012), 365-374. Ordóñez Medina, S.A., Erath, A., 2013. New dynamic events-based public transport route for agent-based simulations. Arbeitsberichte Verkehrs- und Raumplanung, 921, Zürich. P łaczek, B., 2014. A self-organizing system for urban traffic control based on predicitive interval microscopic model. Engineering Applications of Artificial Intelligence 34, 75-84. Scheiner, J., 2009: Sozialer Wandel, Raum und Mobilität. Empirische Untersuchungen zur Subjektivierung der Verkehrsnachfrage. Verlag für Sozialwissenschaften, Wiesbaden. Schlaich, J., 2011: Verkehrsmodellierung für die Region Stuttgart. Analyseverkehr 2009/2010 – Schlussbericht. PTV AG, Karlsruhe. Tlig, M., Bhouri, N., 2011. A Multi-Agent System for Urban Traffic and Buses Regularity Control. Procedia Social and Behavioral Sciences 20, 896-905. Torrens, P.M., 2012. Moving Agent Pedestrians Through Space and Time. Annals of the Association of American Geographers 102, 35-66. Vitins, B., 2014. Shape grammars for urban network design. PhD-Thesis 22615, ETH Zürich. Waraich, R., 2013. Agent-based simulation of electric vehicles: design and implementation of a framework. PhD-Thesis 21633, ETH Zürich. Wegener, M., 2013. The future of mobility in cities: Challenges for urban modeling. Transport Policy 29, 275-282.