Dynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservations

Martin Repoux, Mor Kaspi, Burak Boyacı, Nikolas Geroliminis

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic staff-based relocation policies. Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. This policy is compared to a state-of-the art staff-based relocation policy and a centralistic relocation model assuming full knowledge of the demand. Numerical results from a real-world implementation and a simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy.

Original languageEnglish
Pages (from-to)82-104
Number of pages23
JournalTransportation Research Part B: Methodological
Volume130
DOIs
StatePublished - Dec 2019

Keywords

  • Carsharing
  • Markov chain
  • Operations
  • Prediction
  • Simulation

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