A Bayesian Adaptive Inference Approach to Estimating Heterogeneous Gap Acceptance Functions

This paper presents a Bayesian adaptive inference approach to estimating heterogeneous gap acceptance functions. The proposed approach models each individual driver behavior among gaps, which is an extension of the Mahmassani and Sheffi’s gap acceptance model for each individual driver. To estimate the heterogeneous gap acceptance parameters for each individual driver, the authors develop a Bayesian adaptive inference framework combing a trial-to-trial information gain strategy that can estimate multiple dimensional parameters to identify the heterogeneous driver’s behavior on gap acceptance. The authors implement the Bayesian adaptive inference framework and conduct experiment analysis to examine the convergence of the algorithm.

• Supplemental Notes:
  • This paper was sponsored by TRB committee AHB45 Traffic Flow Theory and Characteristics.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Zhu, Juanping
  • Zhang, Kuilin
• Conference:
  • Transportation Research Board 94th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2015-1-11 to 2015-1-15
• Date: 2015

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 17p
• Monograph Title: TRB 94th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Algorithms; Bayes' theorem; Behavior; Drivers; Gap acceptance
• Subject Areas: Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01557768
• Record Type: Publication
• Report/Paper Numbers: 15-4189
• Files: TRIS, TRB, ATRI
• Created Date: Mar 25 2015 4:20PM