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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13279

Title: A Bayesian approach for modeling origin-destination matrices
Authors: Perrakis, Konstantinos
Karlis, Dimitris
Cools, Mario
Janssens, Davy
Vanhoof, Koen
Wets, Geert
Issue Date: 2012
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Citation: TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, 46 (1), p. 200-212
Abstract: The majority of origin destination (OD) matrix estimation methods focus on situations where weak or partial information, derived from sample travel surveys, is available. Information derived from travel census studies, in contrast, covers the entire population of a specific study area of interest. In such cases where reliable historical data exist, statistical methodology may serve as a flexible alternative to traditional travel demand models by incorporating estimation of trip-generation, trip-attraction and trip-distribution in one model. In this research, a statistical Bayesian approach on OD matrix estimation is presented, where modeling of OD flows derived from census data, is related only to a set of general explanatory variables. A Poisson and a negative binomial model are formulated in detail, while emphasis is placed on the hierarchical Poisson-gamma structure of the latter. Problems related to the absence of closed-form expressions are bypassed with the use of a Markov Chain Monte Carlo method known as the Metropolis-Hastings algorithm. The methodology is tested on a realistic application area concerning the Belgian region of Flanders on the level of municipalities. Model comparison indicates that negative binomial likelihood is a more suitable distributional assumption than Poisson likelihood, due to the great degree of overdispersion present in OD flows. Finally, several predictive goodness-of-fit tests on the negative binomial model suggest a good overall fit to the data. In general, Bayesian methodology reduces the overall uncertainty of the estimates by delivering posterior distributions for the parameters of scientific interest as well as predictive distributions for future OD flows.
Notes: [Perrakis, Konstantinos; Cools, Mario; Janssens, Davy; Vanhoof, Koen; Wets, Geert] Hasselt Univ, Transportat Res Inst, BE-3590 Diepenbeek, Belgium. [Karlis, Dimitris] Athens Univ Econ & Business, Dept Stat, Athens 10434, Greece. [Cools, Mario] Res Fdn Flanders FWD, BE-1000 Brussels, Belgium.
URI: http://hdl.handle.net/1942/13279
DOI: 10.1016/j.tra.2011.06.005
ISI #: 000297899700016
ISSN: 0965-8564
Category: A1
Type: Journal Contribution
Appears in Collections: Traffic Safety
Transportation Behaviour
Business Informatics
Transportation Research Institute

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