A unified framework of proactive self-learning dynamic pricing for high-occupancy/toll lanes

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

This article presents a unified framework to determine dynamic pricing strategies for high-occupancy/toll (HOT) lanes. The framework consists of two critical steps, system inference and toll optimisation. The first step is to mine traffic data in a real time manner to learn motorists' willingness-to-pay, estimate traffic state and predict short-term traffic demand. The attained knowledge is then used in the second step to explicitly optimise toll rates for the next rolling horizon to maximise the freeway throughput while ensuring a free-flow travel speed on HOT lanes. This article discusses the details of each step and how to implement them. The framework is validated in a simulation environment based on a multi-lane hybrid cell transmission model. It is demonstrated that the framework is efficient, effective and flexible, and has the potential to be readily implemented in practice.

Original languageEnglish (US)
Pages (from-to)205-222
Number of pages18
JournalTransportmetrica A: Transport Science
Volume9
Issue number3
DOIs
StatePublished - Mar 1 2013
Externally publishedYes

Keywords

  • dynamic pricing
  • high-occupancy/toll lanes
  • self-learning

ASJC Scopus subject areas

  • Transportation
  • Engineering(all)

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