Model and Algorithm for Continuous Time-Varying Shortest Path Problem

The shortest path problem is a common problem in traffic areas. In an actual traffic network, arc costs are usually time-varying functions. Under such conditions, the issue of how to find a shortest path is called a time-varying shortest path problem. There is little research on this so far, and the research is all aiming at the following situations: arc costs are discrete time-varying functions, piecewise functions, or probability distribution functions. However, arc costs are often continuous time-varying, and there is hardly any research on the shortest path problem under this condition. Therefore, the authors establish a nonlinear programming model and design a corresponding dynamic Dijkstra algorithm on a continuous time-varying path problem, whose correctness and effectiveness are verified through a case study.

• Record URL:
  • https://doi.org/10.1061/41184(419)46
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784411841
• Supplemental Notes:
  • Copyright © 2011 ASCE
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Hu, Lu
  • Peng, Bo
  • Jiang, Yangsheng
  • Zhang, Gai
  • Du, Haitao
• Conference:
  • Third International Conference on Transportation Engineering (ICTE)
  • Location: Chengdu , China
  • Date: 2011-7-23 to 2011-7-25
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 271-276
• Monograph Title: ICTE 2011

Subject/Index Terms

• TRT Terms: Functions (Mathematics); Nonlinear programming; Routing; Shortest path algorithms
• Uncontrolled Terms: Cost functions
• Subject Areas: Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01458094
• Record Type: Publication
• ISBN: 9780784411841
• Files: TRIS, ASCE
• Created Date: Dec 31 2012 5:55PM