Scheduling strategy for transit routes with modular autonomous vehicles

Abstract

The Modular Autonomous Vehicle (MAV) systems allow a vehicle module to join onto and detach from other modules to dynamically adjust vehicle capacity. It potentially renders transit agencies more flexibility to deal with the temporal fluctuations of passenger demand. In this work, we propose a strategy for flexible MAV scheduling on transit routes to meet the time-varying passenger demand. The proposed strategy is formulated as a bi-objective optimization model considering both the utilization of vehicles and service quality. The model determines the scheduled departure times from the terminals, the length of MAV for each scheduled trip, and the assignment of modules to all scheduled trips, simultaneously. The ε-constraint method is adopted to solve the developed model and the fuzzy satisfying approach is employed to select the best possible solution. We implement the proposed strategy in a real-world case study in comparison with a traditional strategy to demonstrate the effectiveness of the proposed strategy. The results show that the proposed strategy can remarkably improve the utilization of vehicles and also make passengers more convenient. Specifically, it leads to an 84.9% reduction in the total empty seat, as well as a 12.62% reduction in the total passenger waiting time.