Optimal dynamic pricing strategies for high-occupancy/toll lanes

Yingyan Lou, Yafeng Yin, Jorge A. Laval

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

Abstract

This paper proposes a self-learning approach to determine optimal pricing strategies for high-occupancy/toll lane operations. The approach learns recursively motorists' willingness to pay by mining the loop detector data, and then specifies toll rates to maximize the freeway's throughput while ensuring a superior travel service to the users of the toll lanes. In determination of the tolls, a multi-lane hybrid traffic flow model is used to explicitly consider the impacts of the lane-changing behaviors before the entry points of the toll lanes on throughput and travel time. Simulation experiments are conducted to demonstrate and validate the proposed approach, and provide insights on when to convert high-occupancy lanes to toll lanes.

Original languageEnglish (US)
Pages (from-to)64-74
Number of pages11
JournalTransportation Research Part C: Emerging Technologies
Volume19
Issue number1
DOIs
StatePublished - Feb 2011
Externally publishedYes

Keywords

  • Dynamic pricing
  • High-occupancy/toll lanes
  • Lane changes
  • Self-learning

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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