A Coupling Approach to Demand Prediction and Repositioning in SAV Systems

Yang Jin*, Dongyao Jia, Yechao She, Meng Xu, Shangbo Wang, Jianping Wang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


In Shared Autonomous Vehicle (SAV) systems, real-time vehicle repositioning plays a crucial role in meeting time-varying traffic demand, which is normally designed by taking advantage of user demand prediction. Nonetheless, most existing studies only predict traffic demand and schedule SAVs separately, ignoring the tight interaction between the two components, e.g. the potential impact of repositioning results on demand prediction. Such a design lacks a deeply integrated design for both and may lead to inaccurate demand prediction and impaired repositioning performance. To tackle this challenge, we propose DRiVe, a coupling approach to Demand prediction and Repositioning for shared autonomous Vehicle system. Specifically, we consider electric SAVs and adopt model predictive control (MPC) to develop the repositioning strategy with the goal of minimizing the operator's repositioning costs and passenger dissatisfaction. An online prediction is then introduced which not only implements the traditional demand prediction but also integrates the additional traffic demand generated by repositioning action. The numerical results demonstrate that the proposed DRiVe method achieves better performance in reducing passenger waiting time and idle distance compared to the state-of-the-art repositioning methods.

Original languageEnglish
Title of host publication2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350329285
Publication statusPublished - 2023
Event98th IEEE Vehicular Technology Conference, VTC 2023-Fall - Hong Kong, China
Duration: 10 Oct 202313 Oct 2023

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference98th IEEE Vehicular Technology Conference, VTC 2023-Fall
CityHong Kong


  • coupling strategy
  • repositioning strategy
  • shared autonomous vehicle system
  • traffic demand prediction


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