Please use this identifier to cite or link to this item: http://www.repositorio.ufc.br/handle/riufc/48855
Title in Portuguese: Bayesian inference on dynamic linear models of day-to-day origin-destination flows in transportation networks
Author: Pitombeira Neto, Anselmo Ramalho
Loureiro, Carlos Felipe Grangeiro
Carvalho, Luis Eduardo
Keywords: Modelos lineares (Estatistica)
Inferência Bayesiana
Transportes
Bayesian inference
Origin–destination flows
Dynamic linear models
Transportation networks
Issue Date: 2018
Publisher: Urban Science
Citation: PITOMBEIRA NETO, A. R.; LOUREIRO, C. F. G.; CARVALHO, L. E. Bayesian inference on dynamic linear models of day-to-day origin-destination flows in transportation networks. Urban Science, v. 2, n. 4, dez. 2018.
Abstract: Estimation of origin–destination (OD) demand plays a key role in successful transportation studies. In this paper, we consider the estimation of time-varying day-to-day OD flows given data on traffic volumes in a transportation network for a sequence of days. We propose a dynamic linear model (DLM) in order to represent the stochastic evolution of OD flows over time. DLMs are Bayesian state-space models which can capture non-stationarity. We take into account the hierarchical relationships between the distribution of OD flows among routes and the assignment of traffic volumes on links. Route choice probabilities are obtained through a utility model based on past route costs. We propose a Markov chain Monte Carlo algorithm, which integrates Gibbs sampling and a forward filtering backward sampling technique, in order to approximate the joint posterior distribution of mean OD flows and parameters of the route choice model. Our approach can be applied to congested networks and in the case when data are available on only a subset of links. We illustrate the application of our approach through simulated experiments on a test network from the literature.
URI: http://www.repositorio.ufc.br/handle/riufc/48855
metadata.dc.type: Artigo de Periódico
ISSN: 2413-8851
Appears in Collections:DECC - Artigos publicados em revistas científicas

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