Clustering Based Online Coordinated In-Vehicle Routing Built upon Understanding the Competition Potential among Travelers on Network Route Resources

This study considers that the collective route choices of many travelers driving en route represent a resolution of their competition on route resources in a traffic network. Well understanding and coordinating this competition will help mitigate urban traffic congestion. Motived by this view, this research first develops mathematical and graphical approaches to understand the competition potential between two travelers and among multiple travelers. Built upon that, the authors develop the adaptive centroid-based clustering algorithm (ACCA), which splits a large scale of travelers en route in a local network into multiple travelers clusters so that the travelers in each cluster presents strong competition potential. According to the statistical analysis, this study demonstrates that the ACCA is able to find a local optimal clustering solution which balances the inner-cluster and inter-cluster competition potential. To demonstrate the value of this study, the athors conduct the numerical experiments on Sioux Falls city network. The authors' experiment results indicate that implementing a coordinated online in-vehicle routing mechanism (CRM) on multiple coordination groups generated by the ACCA is able to reduce computation load with a minor sacrifice in the system performance under various traffic conditions and penetrations.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Du, Lili
    • Peng, Wang
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 18p

Subject/Index Terms

Filing Info

  • Accession Number: 01698249
  • Record Type: Publication
  • Report/Paper Numbers: 19-03102
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:50AM