A dynamic graph-based many-to-one ride-matching approach for shared autonomous electric vehicles

Ning Wang, Yelin Lyu (), Shengling Jia, Chaojun Zheng, Zhiquan Meng and Jingyun Chen
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Ning Wang: Tongji University
Yelin Lyu: Tongji University
Shengling Jia: Tongji University
Chaojun Zheng: State Grid Zhejiang Electric Vehicle Service Co., Ltd.
Zhiquan Meng: State Grid Zhejiang Electric Vehicle Service Co., Ltd.
Jingyun Chen: State Grid Zhejiang Electric Power Co., Ltd.

Transportation, 2024, vol. 51, issue 5, No 13, 1879-1905

Abstract: Abstract Shared autonomous electric vehicles (SAEVs) have recently attracted significant public interest. The dynamic ride-sharing using SAEVs appears to have the advantages of reducing travel costs and relieving urban traffic congestion. It is meant to improve the practical application value of the dynamic ride-sharing mode of SAEVs. In this paper, to reduce the solution time complexity, a pre-matching algorithm considering the driverless and charging characteristics of SAEVs is developed, and then a two-stage, graph-based many-to-one ride-matching (GMOM) algorithm is proposed for the dynamic ride-sharing problem in the Autonomous Mobility-on-Demand system (AMOD). The dataset from DiDi during the peak travel time and the real-time traffic flow from the AutoNavi map were used to verify the effects of the method. The results demonstrate that the GMOM approach can effectively reduce computational complexity and improve user satisfaction. The dynamic ride-sharing mode based on the GMOM algorithm has a 5.67% higher service rate and 45.56% more vehicle calls than the non-ride-sharing mode under the same conditions. It is found that the cost-effectiveness of using ride-sharing services is relatively high for 10–20 km trips during peak travel time and the dynamic ride-sharing may extend total travel time but will reduce passengers’ waiting time.

Keywords: Autonomous mobility-on-demand system; Dynamic ride-matching; Ride-sharing; Graph theory; Edmonds algorithm; Robotaxi (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11116-023-10391-3

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