Article (Scientific journals)
Bayesian inference for transportation origin–destination matrices: the Poisson–inverse Gaussian and other Poisson mixtures
Perrakis, Konstantinos; Karlis, Dimitris; Cools, Mario et al.
2015In Journal of the Royal Statistical Society. Series A, Statistics in Society, 178 (1), p. 271-296
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Keywords :
Hierarchical Bayesian modelling; Integrated nested Laplace approximation; Origin–destination matrix; Overdispersion; Poisson mixtures
Abstract :
[en] Transportation origin–destination analysis is investigated through the use of Poisson mixtures by introducing covariate-based models which incorporate different transport modelling phases and also allow for direct probabilistic inference on link traffic based on Bayesian predictions. Emphasis is placed on the Poisson–inverse Gaussian model as an alternative to the commonly used Poisson–gamma and Poisson–log-normal models. We present a first full Bayesian formulation and demonstrate that the Poisson–inverse Gaussian model is particularly suited for origin–destination analysis because of its desirable marginal and hierarchical properties. In addition, the integrated nested Laplace approximation is considered as an alternative to Markov chain Monte Carlo sampling and the two methodologies are compared under specific modelling assumptions. The case-study is based on 2001 Belgian census data and focuses on a large, sparsely distributed origin–destination matrix containing trip information for 308 Flemish municipalities.
Research center :
Lepur : Centre de Recherche sur la Ville, le Territoire et le Milieu rural - ULiège
LEMA - Local Environment Management and Analysis
Disciplines :
Special economic topics (health, labor, transportation...)
Civil engineering
Author, co-author :
Perrakis, Konstantinos
Karlis, Dimitris
Cools, Mario  ;  Université de Liège - ULiège > Département Argenco : Secteur A&U
Janssens, Davy
Language :
English
Title :
Bayesian inference for transportation origin–destination matrices: the Poisson–inverse Gaussian and other Poisson mixtures
Publication date :
2015
Journal title :
Journal of the Royal Statistical Society. Series A, Statistics in Society
ISSN :
0964-1998
eISSN :
1467-985X
Publisher :
Blackwell Publishing, London, United Kingdom
Volume :
178
Issue :
1
Pages :
271-296
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 02 May 2014

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