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Bayesian demand calibration for dynamic traffic simulations
École Polytechnique Fédérale de Lausanne (EFPL), School of Architecture, Civil and Environmental Engineering (ENAC), Lausanne, Switzerland.
2011 (English)In: Transportation Science, ISSN 0041-1655, E-ISSN 1526-5447, Vol. 45, no 4, 541-561 p.Article in journal (Refereed) Published
Abstract [en]

We present an operational framework for the calibration of demand models for dynamic traffic simulations, where calibration refers to the estimation of a structurally predefined model's parameters from real data. Our focus is on disaggregate simulators that represent every traveler individually. We calibrate, also at an individual level, arbitrary choice dimensions within a Bayesian framework, where the analyst's prior knowledge is represented by the dynamic traffic simulator itself and the measurements are comprised of time-dependent traffic counts. The approach is equally applicable to an equilibrium-based planning model and to a telematics model of spontaneous and imperfectly informed drivers. It is based on consistent mathematical arguments, yet it is applicable in a purely simulation-based environment and, as our experimental results show, is capable of handling large scenarios.

Place, publisher, year, edition, pages
2011. Vol. 45, no 4, 541-561 p.
Keyword [en]
disaggregate demand calibration, dynamic traffic assignment, microsimulation, path flow estimation, Bayesian estimation
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-76743DOI: 10.1287/trsc.1100.0367ISI: 000297208700006OAI: oai:DiVA.org:kth-76743DiVA: diva2:491116
Funder
TrenOp, Transport Research Environment with Novel Perspectives
Note
TSC import 811 2012-02-06. QC 20120207Available from: 2012-02-06 Created: 2012-02-06 Last updated: 2017-12-08Bibliographically approved

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  • apa
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  • de-DE
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  • nn-NB
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  • Other locale
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  • asciidoc
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