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Discrete Dynamics in Nature and Society
Volume 2014, Article ID 192470, 9 pages
http://dx.doi.org/10.1155/2014/192470
Research Article

A Bayesian Network Model for Origin-Destination Matrices Estimation Using Prior and Some Observed Link Flows

School of Transportation, Southeast University, Nanjing 210096, China

Received 1 November 2013; Accepted 11 March 2014; Published 13 April 2014

Academic Editor: Wuhong Wang

Copyright © 2014 Lin Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a Bayesian network model for estimating origin-destination matrices. Most existing Bayesian methods adopt prior OD matrixes, which are always troublesome to be obtained. Since transportation systems normally have stored large amounts of historical link flows, a Bayesian network model using these prior link flows is proposed. Based on some observed link flows, the estimation results are updated. Under normal distribution assumption, the proposed Bayesian network model considers the level of total traffic flow, the variability of link flows, and the violation of the traffic flow conservation law. Both the point estimation and the corresponding probability intervals can be provided by this model. To solve the Bayesian network model, a specific procedure which can avoid matrix inversion is proposed. Finally, a numerical example is given to illustrate the proposed Bayesian network method. The results show that the proposed method has a high accuracy and practical applicability.