Adding Mode Choice to Multiagent Transport Simulation

Rieser, Marcel; Grether, Dominik; Nagel, Kai

doi:10.3141/2132-06

Cited by 15 publications

(13 citation statements)

References 9 publications

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“…There is not yet any distinction based on the location of an activity. For simplicity, a physical network simulation of public transport is replaced by a "teleportation mode" that moves travelers on public transport trips at half the speed of a car in uncongested conditions Rieser et al, 2009). This fairly simplistic approach was chosen due to the lack of a proper public transport simulation in MATSim, which, however, will be available in the near future Rieser (2010).…”

Section: Zurich Field Studymentioning

confidence: 99%

Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation

Flötteröd

Nagel

2011

Netw Spat Econ

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This article reports on the calibration and analysis of a fully disaggregate (agentbased) transport simulation for the metropolitan area of Zurich. The agent-based simulation goes beyond traditional transport models in that it equilibrates not only route choice but all-day travel behavior, including departure time choice and mode choice. Previous work has shown that the application of a novel calibration technique that adjusts all choice dimensions at once from traffic counts yields crossvalidation results that are competitive with any state-of-the-art four-step model. While the previous study aims at a methodological illustration of the calibration method, this work focuses on the real-world scenario, and it elaborates on the usefulness of the obtained results for further demand analysis purposes.

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Section: Zurich Field Studymentioning

confidence: 99%

Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation

Flötteröd

Nagel

2011

Netw Spat Econ

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“…The Router is a time-dependent best path algorithm (Lefebvre and Balmer 2007), normally using the travel times of the previous iteration as the link's generalized costs. Mode choice will not be simulated by a seperate module, but instead by making sure that every agent has at least one ''car'' and at least one ''public transit'' plan (Grether et al 2009a;Rieser et al 2009). …”

Section: Overviewmentioning

confidence: 99%

Income-contingent user preferences in policy evaluation: application and discussion based on multi-agent transport simulations

2011

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Standard economic policy evaluation allows the realization of projects if the aggregated economic benefit outweighs their costs. The use of one single aggregated welfare measure for evaluating and ranking projects has often been criticized for many reasons. A major issue is that differentiated effects on individuals or subgroups of the population are not taken into consideration. This leads to the need for transport planning tools that provide additional information for politicians and decision makers. The microscopic multi-agent simulation approach presented in this paper is capable of helping to design better solutions in such situations. In particular, it is shown that the inclusion of individual income in utility calculations allows a better understanding of problems linked to public acceptance. First, individual income-contingent utility functions are estimated based on survey data in order to describe human mobility behavior. Subsequently, using the MATSim framework, the implementation is tested in a test scenario. Furthermore, and going beyond Franklin (2006), it is shown that the approach works in a large-scale real world example. Based on a hypothetical speed increase of public transit, effects on the welfare distribution of the population are discussed. It is shown that the identification of winners and losers seems to be quite robust. However, results indicate that a conversion or aggregation of individual utility changes for welfare analysis is highly dependent on the functional form of the utility functions as well as on the choice of the aggregation procedure.

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“…Mode innovation is described by Rieser et al (2009);Meister et al (2010); Ciari et al (2008Ciari et al ( , 2007.…”

Section: Mode Innovationmentioning

confidence: 99%

“…Departure time, mode and route choice are the heart of the transport modeling enterprise and were addressed in MATSim almost from the start (Raney and Nagel, 2004;Balmer et al, 2005b;Rieser et al, 2009). Work in Zürich addressed further behavioral dimensions, as shown in Figure 46.1.…”

Section: "Best Response" and Further Choice Dimensions (Eth Zürich Trmentioning

confidence: 99%

The Multi-Agent Transport Simulation MATSim

Axhausen¹

2016

482

123

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PrefaceDeveloping complex so ware for over a decade with a heterogeneous group of engineers and scientists, each with widely di erent skill levels and expertise across multiple locations around the world, requires dedication and mechanisms unusual for a university environment. This book is one of these mechanisms. It allows us, collectively, to take stock and present a coherent state-of-the-system: for us and anyone interested in this approach. It highlights basics for the student who wants to undertake a small rst research project as part of his or her degree, provides a description of the main functionalities, in detail, for the engineer setting up MATSim (MultiAgent Transport Simulation) to conduct a policy analysis and, nally, ts the approach into the theoretical background of complex systems in computer science and physics.The choice of the additional e-book format is an advantage, as it allows us to keep the book upto-date with future chapters, revisions and, if necessary, errata. Equally importantly it allows you, the readers, to select those sections relevant to your needs.The book comes at an important time for the system; for most of the rst decade, its use was limited to the original developers and users in Berlin and Zürich. It is now much more widely consulted around the world, as we document in the chapter summarizing contributions on scenarios so far.Scenario: This term will occur again and again. In MATSim context, it is de ned as the combination of speci c agent populations, their initial plans and activity locations (home, work, education), the network and facilities where, and on which, they compete in time-space for their slots and modules, i.e., behavioral dimensions, which they can adjust during their search for equilibrium. Within these scenarios, the user can experiment and explore with behavioral utility function parameters, with the sampling rate of the population between 1 % and 100 %, with algorithm parameters, e.g., the share of the sample engaged in replanning in any iteration, or behavioral dimensions or exact settings necessary to avoid gridlock due to the tra c ow dynamics. The creation of a scenario is a substantial e ort, and the framework makes a number of tools available to accelerate it: population synthesizers, network editors, network converters between popular formats and the MATSim representation, e.g., OSM (OpenStreetMap) or GTFS (General Transit Feed Speci cation), semi-automatic network matching to join information, among others.A large group of colleagues has been involved and many of them are contributors to this book; this is a list of those involved, other than ourselves, in Berlin, Singapore and Zürich. Additional contributors are mentioned as authors of their respective chapters in this book. We hope to acknowledge the contributions of more colleagues from other groups in future versions of this book and in the so ware.Special thanks go to a number of people who greatly helped improving this book beyond their own chapters. Benjamin Kickhöfer's deep knowledge of ...

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Adding Mode Choice to Multiagent Transport Simulation

Cited by 15 publications

References 9 publications

Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation

Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation

Income-contingent user preferences in policy evaluation: application and discussion based on multi-agent transport simulations

The Multi-Agent Transport Simulation MATSim

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