Bayesian Inference on Dynamic Linear Models of Day-to-Day Origin-Destination Flows in Transportation Networks

Urban Science

Pitombeira-Neto, AnselmoRamalho¹  Carvalho, LuisEduardo²  Loureiro, CarlosFelipe Grangeiro³ 

[1] Department of Industrial Engineering, Federal University of Ceará, 60455-760 Fortaleza, Brazil;Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA;Department of Transportation Engineering, Federal University of Ceará, 60455-760 Fortaleza, Brazil

关键词: origin–destination flows;    transportation networks;    dynamic linear models;    Bayesian inference;   

DOI  :  10.3390/urbansci2040117

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY