Formulating the within-day dynamic stochastic traffic assignment problem from a Bayesian perspective |
| |
| Institution: | 1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;2. Department of Civil and Environmental Engineering, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152-8550, Japan;3. Graduate School of Engineering, Kobe University, 1-1, Rokkodai, Nada, Kobe 657-8501, Japan;1. Huawei Technologies, Paris Research Center, France;2. Samovar, Telecom SudParis, Institut Polytechnique de Paris, France;1. Max Planck Institute for Tax Law and Public Finance, Marstallplatz 1, 80539 Munich, Germany;2. Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, 60629 Frankfurt am Main, Germany;1. Institute of Transportation Engineering, Zhejiang University, Hangzhou, Zhejiang 310058, China;2. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong 999077, China;3. Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, United States |
| |
| Abstract: | This study proposes a formulation of the within-day dynamic stochastic traffic assignment problem. Considering the stochastic nature of route choice behavior, we treat the solution to the assignment problem as the conditional joint distribution of route traffic, given that the network is in dynamic stochastic user equilibrium. We acquire the conditional joint probability distribution using Bayes’ theorem. A Metropolis–Hastings sampling scheme is developed to estimate the characteristics (e.g., mean and variance) of the route traffic. The proposed formulation has no special requirements for the traffic flow models and user behavior models, and so is easily implemented. |
| |
| Keywords: | Dynamic stochastic user equilibrium Bayes’ theorem Posterior distribution Metropolis–Hastings algorithm |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
