Policy Evaluation in Multiagent Transport Simulations

Grether, Dominik; Kickhöfer, Benjamin; Nagel, Kai

doi:10.3141/2175-02

Cited by 6 publications

(6 citation statements)

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“…Note that this paper is an extension of Grether et al (2009b), who considered three policy measures: a public transit (pt) price increase, a pt speed increase, and a combination of the two. The results of the combined measure are also reported in Grether et al (2010). In contrast to the latter, the present paper concentrates on the pt speed increase only.…”

Section: Introductionmentioning

confidence: 87%

“…Large-scale multi-agent traffic simulations are capable of simulating the complete daily plans of several millions of individuals (agents) (Meister et al 2008). In contrast to traditional models, all attributes that are attached to the synthetic travelers are kept during the simulation process, thus enabling highly granulated analysis Grether et al 2010;Kickhöfer et al 2010). Being aware of all attributes enables the possibility to attach an individual utility function to every traveler which is used to maximize the individual return of travel choices during the simulation process.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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

2011

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“…To answer the problems of scale and real-time simulation, a variety of ICT solutions (parallel and supercomputing infrastructures) are being designed and tested. To deal with this challenge, agent-based simulations were bent to applications needs, such as policy modeling and traffic optimization (Grether et al, 2010 ), distributed communication over the Internet (Chen, 2009 ), electricity market (Guerci et al, 2010 ), financial crisis (Sornette, 2003 ), epidemics (Pastor-Satorras and Vespignani, 2001 ). This is not the forum for discussing sophisticated technical solutions (but for a review of techniques to that purpose, the reader might be referred to Paolucci et al, 2013 ) to the problem of making ABM more apt to the requirements of BigData science.…”

Section: Agent-based Modeling: a Balancementioning

confidence: 99%

On agent-based modeling and computational social science

2014

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In the first part of the paper, the field of agent-based modeling (ABM) is discussed focusing on the role of generative theories, aiming at explaining phenomena by growing them. After a brief analysis of the major strengths of the field some crucial weaknesses are analyzed. In particular, the generative power of ABM is found to have been underexploited, as the pressure for simple recipes has prevailed and shadowed the application of rich cognitive models. In the second part of the paper, the renewal of interest for Computational Social Science (CSS) is focused upon, and several of its variants, such as deductive, generative, and complex CSS, are identified and described. In the concluding remarks, an interdisciplinary variant, which takes after ABM, reconciling it with the quantitative one, is proposed as a fundamental requirement for a new program of the CSS.

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“…This leads, for example, to purely random "logit" switchers between a base and a policy case (e.g., Grether et al, 2010). Moreover, the default plans removal (Sections 4.5.…”

Section: Frozen Randomness For Choice Dimensions Other Than Destinatimentioning

confidence: 99%

“…Some studies, however, used larger sets of attributes. Grether et al (2010); estimated individual income-contingent utility functions. Horni and Axhausen (2012b,a) incorporated agent-speci c travel preferences and individual income-dependent marginal utilities of money; preference values, however, were assigned randomly.…”

Section: Agent-speci C Preferencesmentioning

confidence: 99%

The Multi-Agent Transport Simulation MATSim

Axhausen¹

2016

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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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Policy Evaluation in Multiagent Transport Simulations

Cited by 6 publications

References 11 publications

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

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

On agent-based modeling and computational social science

The Multi-Agent Transport Simulation MATSim

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