A generalized Bayesian traffic model

The rapid growth of transportation data offers new opportunities to analyze the interaction between travel behavior and transportation system performance, and particularly when issues such as uncertainty and reliability are considered. Many previous studies in this area described the stationary behavior of stochastic transportation systems using user equilibrium (UE) conditions. In contrast, this paper develops a generalized Bayesian model to analyze the dynamic behavior of stochastic transportation systems. In the proposed model, the variability of link volume and travel time stems from the stochasticity in travel demand, transportation supply (e.g. link capacity, free flow travel time, etc.) and route choice. To the best of the authors' knowledge, this is among the first work that considers the three sources of stochasticity simultaneously. In addition, the authors propose a Bayesian updating approach based on the Dirichlet model to describe the route choice behavior. This approach allows researchers to consider a wide range of route choice behavior of bounded rationality in day-to-day traffic dynamics, including a knowledge updating mechanism based on different memory lengths and weighting factors. This framework is particularly suitable for data-driven studies supported by emerging transportation data due to the computing efficiency of the Dirichlet-based Bayesian updating mechanism and the sound behavioral foundation. This paper shows that the proposed Bayesian model with infinite memory leads to UE conditions under stochastic demand and supply. Subsequently, a numerical case study is conducted to illustrate different day-to-day route choice dynamics with different memory lengths of system performance. This paper also discusses the influence of the three sources of stochasticity towards the aggregated variance of link volumes and travel time. With enough longitudinal travel choice and transportation system performance data, the proposed Bayesian framework could be empirically calibrated and tested, which offers an attractive descriptive alternative to the conventional UE-based transportation system models.

• Record URL:
  https://doi.org/10.1016/j.trc.2019.09.011
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X18318199
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhu, Zheng
  • 0000-0002-9318-1861
  • Zhu, Shanjiang
  • Zheng, Zhengfei
  • Yang, Hai
  • 0000-0001-5210-8468
• Publication Date: 2019-11

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 182-206
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 108
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Bayes' theorem; Networks; Route choice; Stochastic processes; Traffic equilibrium; Traffic flow; Traffic models
• Subject Areas: Operations and Traffic Management; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01722174
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 14 2019 9:30AM