Chapter
Jun 29, 2016
A Joint Estimation of Network Origin-Destination Traffic Flow and Route Choice Model Parameters Using the Bayesian Approach
Authors: Guoshan Xu [email protected], Lin Cheng [email protected], and Xiaoming Lou [email protected]Author Affiliations
Publication: CICTP 2016
Abstract
This study proposes a statistical model to estimate link traffic flows in a congested network. In this study, it is assumed that traffic flows conform to the stochastic user equilibrium (SUE) principle. In the case of SUE, as we know, the network traffic flow pattern is obviously affected by the dispersion parameter adopted in the logit formula. Hence, we make some efforts to estimate this dispersion parameter in this paper. In this study, the Bayesian theorem is applied to derive the probability density function of the conditional distribution. While the calculation of Bayesian posterior will typically be intractable, a Markov chain Monte Carlo (MCMC) algorithm is used to estimate the traffic flow. The performance of the proposed method is tested on the Nguyen-Dupuis network through a numerical example.
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Published In
CICTP 2016
Pages: 1449 - 1460
Copyright
© 2016 American Society of Civil Engineers.
History
Published online: Jun 29, 2016
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Affiliations
School of Transportation, Southeast Univ., Nanjing, Jiangsu Province, China. E-mail: [email protected]
School of Transportation, Southeast Univ., Nanjing, Jiangsu Province, China. E-mail: [email protected]
School of Transportation, Southeast Univ., Nanjing, Jiangsu Province, China. E-mail: [email protected]
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