Target encirclement of moving ride-hailing vehicle under uncertain environment: a multi-vehicle mutual rescue model

To solve the emergency rescue of driver-hijacking-passengers event, this paper proposes a multi-vehicle mutual rescue (MMR) model for ride-hailing platform to solve the emergency rescue of driver-hijacking-passengers event. Firstly, the upper model is constructed minimize the movable link lengths of the target vehicle, the path risk of rescue vehicles according to robust optimization theory. Secondly, the lower model is formed by the user equilibrium (UE) model for traffic assignment problems by using mutual and positioning information. Thirdly, the strength Pareto evolutionary algorithm (SPEA Ⅱ), the Frank-Wolfe algorithm, and the method of two-stage coding are comprehensively applied in this paper for the NP-hard problem of the MMR model. Finally, the mutual information and O-D flows of links on the 4th Ring Road of Beijing are utilized to examine the MMR model, and analyzed the utility of the hybrid algorithm and the MMR model. The experimental result indicates that when the robust control parameters are set to 0, 30, and 60, then 3, 2, and 3 groups of the MMR schemes with different sensitivity to the uncertain environment can be obtained. The algorithm utility analysis results indicate that the hybrid algorithm with intimacy calculation can significantly enhance the convergence of the algorithm. Compared with the traditional convex hull algorithm, the model utility analysis results indicate that, in addition to effectively making up for the application defects of the traditional algorithm, the MMR model adopts the mutual rescue strategy that is superior to the traditional algorithm in the Target encirclement nodes selection of target vehicle, the paths planning of rescue vehicles, the collaborative scheduling of ride-hailing platform, and can well complete the mutual rescue task under an uncertain environment for driver-hijacking-passengers event.

• Record URL:
  https://doi.org/10.1016/j.cor.2022.105901
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0305054822001630
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/1793974
• Supplemental Notes:
  • © 2022 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Luo, Dongyu
  • Wang, Jiangfeng
  • Lu, Wenqi
  • Chen, Lei
  • Gao, Zhijun
  • Dong, Jiakuan
• Publication Date: 2022-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Illustrations; Maps; References; Tables;
• Pagination: 105901
• Serial:
  • Computers & Operations Research
  • Volume: 146
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0305-0548
  • Serial URL: https://www.sciencedirect.com/journal/computers-and-operations-research

Subject/Index Terms

• TRT Terms: Automatic vehicle location; Genetic algorithms; Hijacking; Ridesourcing; Search and rescue operations; Vehicle occupant rescue
• Geographic Terms: Beijing (China)
• Subject Areas: Operations and Traffic Management; Passenger Transportation; Security and Emergencies;

Filing Info

• Accession Number: 01853240
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 26 2022 5:11PM