Reference : A Bayesian approach for modeling origin–destination matrices
Scientific journals : Article
Engineering, computing & technology : Civil engineering
Business & economic sciences : Special economic topics (health, labor, transportation…)
A Bayesian approach for modeling origin–destination matrices
Perrakis, Konstantinos [Universiteit Hasselt - UH > > > >]
Karlis, Dimitris [Athens University of Economics and Business > > > >]
Cools, Mario mailto [Universiteit Hasselt - UH > > > >]
Janssens, Davy [Universiteit Hasselt - UH > > > >]
Vanhoof, Koen [Universiteit Hasselt - UH > > > >]
Wets, Geert [Universiteit Hasselt - UH > > > >]
Transportation Research. Part A : Policy & Practice
Pergamon Press
Yes (verified by ORBi)
[en] 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.
Lepur : Centre de Recherche sur la Ville, le Territoire et le Milieu rural ; LEMA - Local Environment Management and Analysis
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