A Coupling Approach to Demand Prediction and Repositioning in SAV Systems

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Author(s)

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Detail(s)

Original languageEnglish
Title of host publication2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall) - Proceedings
PublisherIEEE
Number of pages6
ISBN (electronic)979-8-3503-2928-5
ISBN (print)979-8-3503-2929-2
Publication statusPublished - 2023

Publication series

Name
ISSN (Print)1090-3038
ISSN (electronic)2577-2465

Conference

Title98th IEEE Vehicular Technology Conference (VTC 2023-Fall)
Location
PlaceChina
CityHong Kong
Period10 - 13 October 2023

Abstract

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. © 2023 IEEE.

Research Area(s)

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

Citation Format(s)

A Coupling Approach to Demand Prediction and Repositioning in SAV Systems. / Jin, Yang; Jia, Dongyao; She, Yechao et al.
2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall) - Proceedings. IEEE, 2023.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review