Predictive Distance-Based Toll Optimization Under Elastic Demand for Real-Time Traffic Management

Real--time network control strategies such as congestion tolling are a widely used means of mitigating traffic congestion in urban transportation networks. Recent advances in Global Satellite Navigation System (GSNS) technologies have led to an increasing interest in distance or usage based tolling as an effective alternative to traditional facility, cordon and area based tolling that typically rely on fixed infrastructure. In this context, this paper proposes a framework for predictive distance-based toll optimization under elastic demand. Given a set of charging zones (defined as collection of links) on the road network, the parameters of the tolling functions are optimized using a simulation based Dynamic Traffic Assignment (DTA) model operating within a rolling horizon framework. The optimization is based on state predictions making it proactive (as opposed to reactive) and is integrated with the generation of guidance information. Behavioral models capture drivers' responses to the tolls in terms of trip cancellation, mode, route and departure time changes. The evaluation of the proposed framework and solution algorithm using a real--world network of Singapore expressways and major arterials under different settings and toll designs demonstrates the effectiveness of distance-based toll optimization in improving total social welfare as well as in mitigating congestion compared to traditional tolling schemes.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ABE25 Standing Committee on Congestion Pricing.
  • Authors:
    • Vu, Vinh-An
    • Hashemi, Hossein
    • Seshadri, Ravi
    • Gupta, Samarth
    • Tan, Gary
    • Prakash, A Arun
    • Ben-Akiva, Moshe
  • Conference:
  • Date: 2018

Language

  • English

Media Info

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

Subject/Index Terms

Filing Info

  • Accession Number: 01660451
  • Record Type: Publication
  • Report/Paper Numbers: 18-06678
  • Files: TRIS, TRB, ATRI
  • Created Date: Feb 20 2018 9:28AM