Measuring and Optimizing the Disequilibrium Levels of Dynamic Networks through Ridesourced Vehicle Data

Transportation systems have been reshaped by emerging travel alternatives, such as ride sourcing and shared mobility services. The transportation networking companies (TNCs) have been collecting high-granular ridesourced vehicle (RV) data over the past decade, while studies on how the RV data can improve current dynamic network modeling and management models are still lacking. This paper proposes a network disequilibrium level (NDL) measure of to what extend the dynamic user equilibrium (DUE) conditions are violated on real-world networks. An estimation method for the NDL is proposed with the trajectory-level RV data. To avoid the leaking of personal information from the trajectory-level RV data, we build up a data sharing scheme for TNCs so that TNCs can release aggregated RV data for transportation management. An estimation method for the NDL with the aggregated RV data is also proposed. We further propose a NDL based traffic management method to perform user optimal routing with the aggregated RV data. The NDL measures and traffic management framework are examined on two real-world large-scale networks: Chengdu with trajectory-level RV data and Pittsburgh with aggregated RV data. Experiment results are compelling, satisfactory and interpretable. Based on the proposed management algorithm, controlling 1% of the vehicle will reduce the total network congestion by around 7%.

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

    Transportation Research Board

  • Authors:
    • Ma, Wei
    • Qian, Zhen (Sean)
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01698232
  • Record Type: Publication
  • Report/Paper Numbers: 19-01565
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:49AM