Stochastic Traffic Assignment Algorithm Based on Probit Model

To improve the two major drawbacks of the Logit-based traffic assignment model, a stochastic Probit-based traffic assignment model, on the basis of assuming that the random error of road link utility follows the normal distribution, is proposed and the Monte-Carlo algorithm is applied to solve the Probit-based model. During the process of the algorithm, all or nothing traffic flow assignment is taken under normal sampling all the road link random errors. Emphasis is placed on the normal distribution variance influence over the traffic flow assignment results. A system optimal quantitative indicator based on minimum origin destination (OD) average cost principal is developed to evaluate the traffic flow assignment result. The numerical example for a small traffic network is used to assess the traffic assignment model and computational algorithm proposed. Results show better traffic assignment can be achieved when decreasing variance.

• Record URL:
  • https://doi.org/10.1061/9780784413159.245
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784413159
• Supplemental Notes:
  • © 2013 American Society of Civil Engineers
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Yang, Yong
  • Yan, Yusong
  • Hu, Zuoan
• Conference:
  • Fourth International Conference on Transportation Engineering
  • Location: Chengdu , China
  • Date: 2013-10-19 to 2013-10-20
• Publication Date: 2013-10

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 1682-1688
• Monograph Title: ICTE 2013: Safety, Speediness, Intelligence, Low-Carbon, Innovation

Subject/Index Terms

• TRT Terms: Algorithms; Monte Carlo method; Probits; Stochastic processes; Traffic assignment
• Subject Areas: Highways; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 01517446
• Record Type: Publication
• ISBN: 9780784413159
• Files: TRIS, ASCE
• Created Date: Mar 8 2014 5:00PM