Integrating price-incentive and trip-selection policies to rebalance shared electric vehicles

Zihao Jiao, Lun Ran, Xin Liu, Yuli Zhang, Robin G. Qiu

Research output: Contribution to journalArticlepeer-review

Abstract

Because electric vehicle sharing (EVS) offers the advantages of high flexibility and convenience, it has been receiving increasing attention worldwide as an effective approach to easing traffic congestion and environmental pollution. However, unbalanced electric vehicle distribution is an obstacle in the development of EVS. In this paper, we propose an integrated strategy to mitigate the imbalance issue and enhance customers’ adoption of EVS. We construct an integrated strategy that combines the price-incentive approach with the trip-selection policy and models uncertain travel demand in a continuous trip-adopting process based on our integrated strategy. Aiming to improve EVS operating profits, we apply spatiotemporal nonlinear mixed-integer programming to formulate the travel pricing and rebalancing plan. Additionally, we approximate the model in a tractable form after analyzing the optimal service adoption and develop an efficient exact algorithm to handle the nonlinear items. The computational results of a real-world car2go Amsterdam case study demonstrate several economic and environmental benefits generated by our integrated policy, including (i) higher profits for EVS operators, (ii) improved service satisfaction for consumers, and (iii) a higher level of carbon emissions reduction, from 381 grams per mile to 225 grams per mile, beneficial for the social environment. Moreover, according to the case study, an appropriate initial fleet size, high rebalancing frequency, low labor cost, high potential travel demands, and short charging time also benefit EVS operation.

Original languageEnglish (US)
Pages (from-to)148-173
Number of pages26
JournalService Science
Volume12
Issue number4
DOIs
StatePublished - Dec 2020

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Modeling and Simulation
  • Management Science and Operations Research
  • Marketing

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