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Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data

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Abstract

Synthetic populations of travelers and their detailed mobility behavior are an important basis for agent-based transport simulations, which are increasingly used in transport planning and research today. To date, research based on such simulations is rarely replicable as it is based on proprietary data and tools. To foster the discussion and steer research towards reproducible transport simulations, this paper introduces a process for generating a synthetic travel demand with individual households, persons, and their daily activity chains for Paris and its surrounding region Île-de-France — entirely based on open data and open software and replicable by any researcher. The resulting travel demand is published for others to use as a comprehensive data basis for agent-based transport simulations and as a test bed for population and demand synthesis algorithms. Furthermore, it is discussed how implicit correlation structures impact the potential use cases of the synthetic travel demand for simulation and analysis purposes and how the common practice of using population samples for downstream simulations affects the results.

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... In order to prepare the MATSim simulation inputs, and more importantly a simplified microscopic representation of the actual population, the Eqasim project, as developed by Hörl and Balac (2020), can be used. This project consists of a modeling pipeline of data consumption and processing steps, making the transition from available public census data and other open source databases to a fully functional MATSim simulation in the French context. ...
... generate a schedule, i.e. the sequence of activities that the agents will try to follow during the day, for every agent. Applying a procedure inspired by statistical matching algorithms, trip chains from household travel survey data are attached to the synthetic population (Hörl and Balac, 2020). ...
... As regards the multi-agent traffic simulation component, Zughe et al. (2019) showed the population scaling factor to be the most influential parameter in MATSim simulations. Some authors (Hörl and Balac, 2020;Ben-Dor et al., 2020Zughe et al., 2019) Lastly, a few limitations are specific to the Eqasim Project when using it to compute noise emissions, namely: ...
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... For this study, the following three pre-existent scenarios are used: Munich Metropolitan Region (hereinafter abbreviated as MUC), Île-de-France (PAR), and San Francisco Bay Area (SFO), as listed in Table 1. While the MUC scenario has been authored by Moeckel et al. [36], PAR and SFO originate from Hörl and Balać [37], Balać and Hörl [38], with both sets of authors using different methods for generating and calibrating their respective scenarios. ...
... Hörl and Balać [37] developed and calibrated an agent-based scenario for Île-de-France. Their scenario was built using only publicly-accessible data from sources such as population census, national and local household travel surveys, and tax registries to form a synthetic population with activity plans; and general transit feed specification (GTFS) schedules and OpenStreetMap to generate a multi-modal transport network. ...
... Their scenario was built using only publicly-accessible data from sources such as population census, national and local household travel surveys, and tax registries to form a synthetic population with activity plans; and general transit feed specification (GTFS) schedules and OpenStreetMap to generate a multi-modal transport network. By using their selfdeveloped framework eqasim [42,43], which builds on MATSim's functionality, but replaces plan scoring with discrete mode-choice models [44,45], Hörl and Balać [37] obtained each agent's mobility choices by applying a multinomial logit model. The calibrated PAR scenario includes trips towards six activity types' home (41%), leisure (13%), work (13%), errand (13%), shop (11%), and education (8%), using four distinct modes of transportation walk (43%), car (33%), PT (22%), and bike (1%). ...
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... Currently, the main benefit of helicopter transport services seem to 33 be their relatively short travel times that-generally-seem unaffected by conventional 34 ground-based traffic congestion. 35 This study aims at exploring the potential travel time savings that various UAM 36 implementations might allow. The main objectives of this study are to provide answers 37 to the following three key research questions: 38 • ...
... but replaces plan scoring with discrete mode-choice models [41,42], Hörl and Balać [36] 198 obtained each agent's mobility choices by applying a multinomial logit model. The Table 1). ...
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The advent of electrified, distributed propulsion in vertical take-off and landing (eVTOL) aircraft promises aerial passenger transport within, into, or out of urban areas. Urban air mobility (UAM), i.e. the on-demand concept that utilizes eVTOL aircraft, might substantially reduce travel times when compared to ground-based transportation. Trips of three, pre-existent, and calibrated agent-based transport scenarios (Munich Metropolitan Region, Île-de-France, and San Francisco Bay Area) have been routed using the UAM-extension for the multi-agent transport simulation (MATSim) to calculate congested trip travel times for each trip's original mode - i.e. car or public transport (PT) - and UAM. The resulting travel times are compared and allow the deduction of potential UAM trip shares under varying UAM properties, such as the number of stations, total process time, and cruise flight speed. Under base case conditions, the share of motorized trips for which UAM would reduce the travel times ranges between 3% and 13% across the three scenarios. Process times and number of stations heavily influence these potential shares, where the vast majority of UAM trips would be below 50 km in range. Compared to car usage, UAM's (base case) travel times are estimated to be competitive beyond the range of a 50-minute car ride and are less than half as much influenced by congestion.
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An activity-based approach to transport demand modeling is considered the most behaviorally sound procedure to assess the effects of transport policies. This paper investigates whether it is possible to transfer an estimated model for activity generation from elsewhere (the estimation context) and use local area (application context) traffic counts to develop a local area activity-based transport demand representation. Here, the estimation context is the Los Angeles, California, area, and the application context is Berlin. Results in this paper suggest that such a transfer approach is feasible, according to a comparison with a Berlin travel survey. Additional studies need to be undertaken to examine the stability of the results obtained in this paper.
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Population synthesis is concerned with the generation of agents for agent-based modelling in many fields, such as economics, transportation, ecology and epidemiology. When the number of attributes describing the agents and/or their level of detail becomes large, survey data cannot densely support the joint distribution of the attributes in the population due to the curse of dimensionality. It leads to a situation where many attribute combinations are missing from the sample data while such combinations exist in the real population. In this case, it becomes essential to consider methods that are able to impute such missing information effectively. In this paper, we propose to use deep generative latent models. These models are able to learn a compressed representation of the data space, which when projected back to the original space, leads to an effective way of imputing information in the observed data space. Specifically, we employ the Wasserstein Generative Adversarial Network (WGAN) and the Variational Autoencoder (VAE) for a large-scale population synthesis application. The models are applied to a Danish travel survey with a feature-space of more than 60 variables and trained and tested using cross-validation. A new metric that applies to the evaluation of generative models in an unsupervised setting is proposed. It is based on the ability to generate diverse yet valid synthetic attribute combinations by comparing if the models can recover missing combinations (sampling zeros) while keeping truly impossible combinations (structural zeros) models at a minimum. For a low-dimensional experiment, the VAE, the marginal sampler and the fully random sampler generate 5%, 21% and 26% more structural zeros per sampling zero when compared to the WGAN. For a high dimensional case, these figures increase to 44%, 2217% and 170440% respectively. This research directly supports the development of agent-based systems and in particular cases where detailed socio-economic or geographical representations are required.
Article
Over the last decades, technological advances have allowed the capturing of travel behaviour at large-scale. Despite the unprecedented volume and the variety of personal mobility data, aggregate Origin-Destination (OD) matrices are still the most widespread means to organise and represent travel demand. Nonetheless, standard ODs cannot adequately capture significant elements affecting travel behaviour such as trip-interdependency and trip-chaining, therefore they are not particularly suitable for travel behaviour analysis at person-level. The currently presented modelling framework enables the in-depth study of personal mobility by firstly combining the trips present in OD matrices into home-based trip-chains (i.e. tours) and subsequently into sequences of activities (activity schedules). The above-mentioned process is completed based on advanced graph-theoretical and combinatorial optimisation concepts. The applicability of the methodology is meticulously verified through a large-scale test case where a set of multi-period, purpose dependent ODs is converted into realistic activity schedules able to incorporate more than 99% of the inputted travel demand. The accurate and highly detailed results showcase the significant potential of the proposed methodology to support the comprehensive analysis of travel behaviour at person level.
Article
An increasing amount of research is dedicated to the consideration of tour formation in freight transportation demand models. While empirical tour formation models so far have been starting from limiting assumptions about the resulting trips, we develop a generalized shipment-based model. We formulate a random utility model embedded in an iterative algorithm to construct tours through the incremental allocation of shipments. It considers different objectives and constraints and acknowledges the difference between commodity, vehicle and location types. Parameters are estimated on a large and comprehensive shipment database. The model reproduces observed tour statistics well for the given set of shipments.
Article
The classical monocentric city model suggests that property prices decrease and transport costs rise with distance to the urban centre, implying that employees face a trade-off between long commutes and high housing costs when making location decisions. Accordingly, some commuters might be forced to take on longer commutes due to rising rents in central locations. In this study, we investigate empirically whether the rental differential between employment centres and residential areas predicts changes in average commuting times. To this end, we consider a gravity model of commuting flows for Ireland over 2011–2016. We present results for Ireland and the metropolitan area of Dublin, which constitutes the largest commuting region in Ireland. The results imply that a 10% rise in rents in employment centres is associated with an up to 0.6 minute rise in one-way daily average commuting times nationally (about 2.2% of the average commute duration).
Article
Autonomous vehicles (AV) create new opportunities to traffic planners and policy-makers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator’s profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator’s side, the routing- and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users’ side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both, the SAV and CC mode in order to improve a city’s transport system.
Article
Public transport lines, especially train lines, have historically played an important role as economic lifelines of rural areas. They are one of the most important factors contributing to economic prosperity as they provide access to mobility for all the inhabitants of these regions. Maintaining such rural public transport lines can be a challenge due to the low utilization inherent to rural areas. Today, with the emergence of fully self-driving cars, on-demand mobility schemes in which autonomous robotic taxis transport passengers, are becoming possible. In this work, we analyze if rural public transport lines with low utilization can be replaced with autonomous mobility-on-demand systems. More specifically, we compare the existing public transportation infrastructure to hypothetical mobility-on-demand systems both in terms of cost and service level. We perform our analysis, which focuses on the operational aspects, using a simulation approach in which unit-capacity robotic taxis are operated in a street network taking into account congestion effects and state-of-the-art control (dispatching and rebalancing) strategies. Our study considers the case of four rural train lines in Switzerland that operate at low utilization and cost coverage. We show that a unit-capacity mobility-on-demand service with self-driving cars reduces both travel times and operational cost in three out of four cases. In one case, even a service with human driven vehicles would provide higher service levels at lower cost. The results suggest that centrally coordinated mobility-on-demand schemes could be a very attractive option for rural areas.
Article
Mobility as a Service (MaaS) is an attempt to overcome market segmentation by offering transport services tailored to the individual traveler's needs. An alternative to prior investment into single mobility tools, it may allow less biased mode choice decisions. Such a setting favors shared modes, where fixed costs can be apportioned among a large number of users. In turn, car-sharing, bike-sharing or ride-hailing may themselves become efficient alternatives to public transport. Although early field studies confirm the expected changes away from private car use and towards public or shared modes, impacts are yet to be studied for larger transport systems. This research conducts a first joint simulation of car-sharing, bike-sharing and ride-hailing for a city-scale transport system using MATSim. Results show that in Zurich, through less biased mode choice decisions alone, transport-related energy consumption can be reduced by 25%. In addition, introduction of car-sharing and bike-sharing schemes may increase transport system energy efficiency by up to 7%, whereas the impact of ride-hailing appears less positive. Efficiency gains may be higher if shared modes were used as a substitute for public transport in lower-density areas. In summary, a MaaS scheme with shared mobility may allow to slightly increase system efficiency (travel times & cost), while substantially reducing energy consumption.
Article
Today, driverless cars, as a new technology that allows a more accessible, dynamic and intelligent form of Shared Mobility, are expected to revolutionize urban transportation. One of the conceivable mobility services based on driverless cars is shared autonomous vehicles (SAVs). This service could merge cabs, carsharing, and ridesharing systems into a singular transportation mode. However, the success and competitiveness of future SAV services depend on their operational models, which are linked intrinsically to the service configuration and fleet specification. In addition, any change in operational models will result in a different demand. Using a comprehensive framework of SAV simulation in a multimodal dynamic demand system with integrated SAV user taste variation, this study evaluates the performance of various SAV fleets and vehicle capacities serving travelers across the Rouen Normandie metropolitan area in France. Also, the impact of ridesharing and rebalancing strategies on service performance is investigated. Research results suggest that the performance of SAV is strongly correlated with the fleet size and the strategy of individual or shared rides. Further analysis indicates that for the pricing scheme proposed in this study (i.e., 20% lower for ridesharing scenario), the standard 4-seats car with shared ride remains the best option among all scenarios. The results also underline that enabling vehicle-rebalancing strategies may have an important effect on both user and service-related metrics. The estimated SAV average and maximum driven distance prove the importance of vehicle range and charging station deployment.
Article
Population synthesis is concerned with the generation of synthetic yet realistic representations of populations. It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models. In this paper, a new methodological framework for how to 'grow' pools of micro-agents is presented. The model framework adopts a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE). Compared to the previous population synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs sampling and traditional generative models such as Bayesian Networks or Hidden Markov Models, the proposed method allows fitting the full joint distribution for high dimensions. The proposed methodology is compared with a conventional Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary. It is shown that, while these two methods outperform the VAE in the low-dimensional case, they both suffer from scalability issues when the number of modeled attributes increases. It is also shown that the Gibbs sampler essentially replicates the agents from the original sample when the required conditional distributions are estimated as frequency tables. In contrast, the VAE allows addressing the problem of sampling zeros by generating agents that are virtually different from those in the original data but have similar statistical properties. The presented approach can support agent-based modeling at all levels by enabling richer synthetic populations with smaller zones and more detailed individual characteristics.
Conference Paper
A simulation framework is presented that equili-brates a given automated taxi fleet with (a) consistent prices to provide the service and (b) customer behaviour that reacts to costs and level of service alike. In a first attempt, a hypothetical AMoD service within the highway ring of Paris is considered. The "dynamic demand" case yields a demand of around 1.2M trips per day for such a service at the optimal fleet size of 25k vehicles. This number is considerably lower than 2.3M trips that potentially could be served in a "static maximum demand" case which has often been used as a basis for previous fleet sizing studies. While the authors acknowledge a multitude of assumptions that constitute the present model, clear pathways to its improvement, methodologically and data-wise, are provided.
Chapter
Durch sich verändernde Lebensmuster, neue Strukturen in der Arbeitswelt sowie weiterhin ungelöste Fragestellungen bzgl. Umweltwirkungen des Verkehrssektors stehen die Verkehrssysteme der Städte und Regionen vor großen Herausforderungen. Diese werden begleitet durch technische Neuerungen wie autonome Fahrzeuge und neue Antriebstechnologien. In der Region Ruhr bietet sich im Zuge der Umgestaltung der Emscherregion (vgl. EGLV, 2018) eine besondere Gelegenheit, Veränderungen im Verkehrssystem herbeizuführen, welche obige Herausforderungen aufgreifen.
Article
This paper investigates optimal congestion pricing strategies using a real-world oriented agent-based simulation framework which allows for complex user behavior. The applied simulation approach accounts for iteratively learning transport users, stochastic demand, and only approximates the user equilibrium, which may be considered as closer to real-world than a model where transport users behave completely rational, have a perfect knowledge about all travel alternatives, and travel behavior strictly follows the user equilibrium. Two congestion pricing rules are developed and investigated. The first one directly builds on the Pigouvian taxation principle and computes marginal external congestion costs based on the queuing dynamics at the bottleneck links; resulting toll payments differ from agent to agent depending on the position in the queue (QCP approach). The second one uses control-theoretical elements to adjust toll levels depending on the congestion level in order to reduce or eliminate traffic congestion; resulting toll payments are the same for all travelers per time bin and road segment (LP approach). The pricing rules are applied to Vickrey's bottleneck model and the case study of the Greater Berlin area. The simulation experiments reveal that with and without mode and departure time choice, the rather simple LP rule results in a higher system welfare compared to the more complex QCP approach. The LP rule appears to better take into account the system's dynamics and the agents’ learning behavior. The results also reveal that pricing significantly reduces traffic congestion, however, there is still a remaining delay, even with departure time choice. Overall, this paper points out further need for research and contributes to the exploration of optimization heuristics for real-world oriented simulation approaches.
Conference Paper
Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.
Article
Urban freight transport suffers from a significant modelling gap compared to passenger transport. The target of this paper is to expose one of the first widely operable methods used in urban freight modelling and its main outputs through the FRETURB design. By reviewing the complete process of its construction, starting with the identification of urban policy needs, the relevant survey and databases construction methods, the modelling allowing the simulation of the effects of governance, we illustrate through the French case what are the elements of success for such a scientific endeavour. The adaptation of freight models to the urban environment strongly relies on an efficient unit of observation, which is introduced as the spine of the model after discussing the main approaches of urban freight transport modelling. The main features of the model are then presented from the generation to the distribution of freight operations and are confronted to elements of validation. The robustness of the model is also discussed through time by analysing elements of its initial calibration 20 years ago and the latest results of urban freight surveys in order to highlight its strengths and weaknesses. We show that the model is able to summarize efficiently the urban freight transport phenomenon and is still statistically robust twenty years after its first design although it needs marginal adaptations on organisational elements.
Article
In this paper, we investigate the influence of scalability on the accuracy of different synthetic populations using both fitting and generation-based approaches. Most activity-based models need a base-year synthetic population of agents with various attributes. However, when several attributes need to be synthesized, the accuracy of the synthetic population may decrease due to the mixed effects of scalability and dimensionality. We analyze two population synthesis methods for different levels of scalability, i.e. two to five attributes and different sample sizes – 10%, 25% and 50%. Results reveal that the simulation-based approach is more stable than Iterative Proportional Fitting (IPF) when the number of attributes increases. However, IPF is less sensitive to changes in sample size when compared to the simulation-based approach. We also demonstrate the importance of choosing the appropriate metric to validate the synthetic populations as the trends in terms of RMSE/MAE are different from those of SRMSE.
Article
Instead of arguing about whether results hold up, let’s push to provide enough information for others to repeat the experiments, says Philip Stark. Instead of arguing about whether results hold up, let’s push to provide enough information for others to repeat the experiments, says Philip Stark.
Article
For the optimization of daily activity chains a novel method has been elaborated, where flexible demand points were introduced. Some activities are not necessarily fixed temporally and spatially, therefore they can be realized in different times or locations. By using flexible demand points, the method is capable of finding new combinations of activity chains and choosing the optimal set of activities. The optimization algorithm solves the TSP-TW (Traveling Salesman Problem – Time Window) problem with many flexible demand points, which resulted in high complexity and long processing times. Therefore, two extensions were developed to speed up the processes. A POI (Point Of Interest) search algorithm enabled to search demand points in advance and store them in an offline database. Furthermore GA (genetic algorithm) was applied and customized to realize lower optimization times. During the implementation, three different transportation modes were defined: car, public transport, and combined (public transport with car-sharing opportunity). The simulations were performed on arbitrarily chosen test networks using Matlab. Promising test results were obtained for all transportation modes with total travel time reduction of 10–15 percent. The application of the extended optimization method produced shorter activity chains and decreased total travel time for the users.
Article
Destination choice models can be embedded in transport and land use models to understand travel and location choice behavior and to forecast scenarios. Utility-maximizing destination choice models can account for individual behavior and make them suitable for agent-based models, while processing destination capacities is also in line with agentbased modeling. This paper addresses the possibility and impact of introducing capacity constraints, their effect on choice behavior, and the feasibility of applying an approach like this in agent-based microsimulations with individual characteristics for each agent. Here, a comprehensive workplace choice model and its application in a large-scale simulation case study for Singapore are described; one technical and one methodological achievement are highlighted. Technical achievement benefits from recent computational advances; the workplace choice model is estimated with a comprehensive utility function on a large data set with 103 destinations. Reasonable model fit and robust parameters are achieved while obviating sampling techniques; resulting parameters are efficiently applied to the entire 5.4 million Singapore population and validated with survey data. For methodological innovation, capacity limitations are introduced at workplaces to avoid oversaturation. A robust optimization method based on shadow prices is proposed to accommodate capacity limitations at all workplaces during the choice model application defined above. The proposed method efficiently assigns commuters to unused workplaces while respecting individual commuter preferences. Validation of the simulation results, by comparing travel time distributions for commuting trips reported in travel diary data, shows that the model fits well with observed data.
Conference Paper
This paper presents the new IVT 2015 baseline scenario for MATSim. It represents a typical workday in Switzerland in the year 2015. The main population is available as a 1% and 10% sample, as well as undiluted. It features attributes, preferences, facilities and households. A cross- border population and a freight population complement the main population. The former represents people coming to Switzerland from abroad, whereas the latter represents freight traffic on a typical workday. The network and the public transport system are based on Open Street Map data and on the official SBB HAFAS schedule. Furthermore, the code, which combines the different input data to coherent, fully functional MATSim scenarios, is discussed from a technical point of view. In a third part, the paper presents the default scenario configuration (configuration file, scoring function and replanning strategies). This configuration is tailored to the population and network described above. The ensemble provides a solid base for future MATSim transport studies at the IVT.
Article
The language and conceptual framework of "research reproducibility" are nonstandard and unsettled across the sciences. In this Perspective, we review an array of explicit and implicit definitions of reproducibility and related terminology, and discuss how to avoid potential misunderstandings when these terms are used as a surrogate for "truth". Copyright 2016 by the American Association for the Advancement of Science; all rights reserved.
Book
The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations.The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.