Improving the On-Vehicle Experience of Passengers Through SC-M*: A Scalable Multi-Passenger Multi-Criteria Mobility Planner

The rapid growth in urban population poses significant challenges to moving city dwellers in a fast and convenient manner. This paper contributes to solving the challenges from the viewpoint of passengers by improving their on-vehicle experience. Specifically, the authors focus on the problem: Given an urban public transit network and a number of passengers, with some of them controllable and the rest uncontrollable, how can they plan for the controllable passengers to improve their experience in terms of their service preference? They formalize this problem as a multi-agent path planning (MAPP) problem with soft collisions, where multiple controllable passengers are allowed to share on-vehicle service resources with one another under certain constraints. The authors then propose a customized version of the SC-M* algorithm to efficiently solve the MAPP task for bus transit system in complex urban environments, where they have a large passenger size and multiple types of passengers requesting various types of service resources. They demonstrate the use of SC-M* in a case study of the bus transit system in Porto, Portugal. In the case study, the authors implement a data-driven on-vehicle experience simulator for the bus transit system, which simulates the passenger behaviors and on-vehicle resource dynamics, and evaluate the SC-M* on it. The experimental results show the advantages of the SC-M* in terms of path cost, collision-free constraint, and the scalability in run time and success rate.


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  • Accession Number: 01768817
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Feb 19 2021 1:58PM