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, we 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 we plan for the controllable passengers to improve their experience in terms of their service preference? We formalize this problem as a multiagent 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. We 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 we have a large passenger size and multiple types of passengers requesting various types of service resources. We demonstrate the use of SC-M* in a case study of the bus transit system in Porto, Portugal. In the case study, we 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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