Reliable Shortest Path Guidance in Stochastic Road Networks Using Convolution-Based Path Finding Algorithm

Most existing research on routing guidance only pays attention to the average value of path travel time, which fails to consider travel time variability (TTV) and travel time reliability preferences by different travelers. In this study, a convolution-based modified genetic algorithm (CMGA) is proposed to find the reliable shortest path in stochastic road networks. By accounting for traveler risk tolerance, the algorithm enables the provision of personalized routing guidance for individual travelers. To support online applications in a large-scale network, reasonable heuristic constraints are imposed to help reduce the computational workload and accelerate the convergence speed of the search process. Numerical case studies based on a grid network with random offsets are provided, and the results help verify that the algorithm has the potential to solve reliable shortest path searching problems in a large-scale network with desirable efficiency.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 1342-1351
  • Monograph Title: CICTP 2018: Intelligence, Connectivity, and Mobility

Subject/Index Terms

Filing Info

  • Accession Number: 01868284
  • Record Type: Publication
  • ISBN: 9780784481523
  • Files: TRIS, ASCE
  • Created Date: Dec 23 2022 10:04AM