Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers

Chao Lei, Zhoutong Jiang, Yanfeng Ouyang

Research output: Contribution to journalConference articlepeer-review

Abstract

Rapidly advancing on-demand ridesharing services, including those with self-driving technologies, hold the promise to revolutionize delivery of mobility. Yet, significant imbalance between spatiotemporal distributions of vehicle supply and travel demand poses a pressing challenge. This paper proposes a multi-period game-theoretic model that addresses dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles. A dynamic mathematical program with equilibrium constraints (MPEC) is formulated to capture the interdependent decision-making processes of the mobility service provider (e.g., regarding vehicle allocation) and travelers (e.g., regarding ride-sharing and travel path options). An algorithm based on approximate dynamic programming (ADP), with customized subroutines for solving the MPEC, is developed to solve the overall problem. It is shown with numerical experiments that the proposed dynamic pricing and vehicle dispatching strategy can help ridesharing service providers achieve better system performance (as compared with myopic policies) while facing spatial and temporal variations in ridesharing demand.

Original languageEnglish (US)
Pages (from-to)77-97
Number of pages21
JournalTransportation Research Procedia
Volume38
DOIs
StatePublished - 2018
Event23rd International Symposium on Transportation and Traffic Theory, ISTTT 2019 - Lausanne, Switzerland
Duration: Jul 24 2018Jul 26 2018

Keywords

  • Approximate dynamic programming
  • Bi-level optimization
  • Dynamic pricing
  • MPEC
  • Ridesharing
  • Self-driving vehicle

ASJC Scopus subject areas

  • Transportation

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