Modular Autonomous Electric Vehicle scheduling for demand-responsive transit services with modular charging strategy

Modular Autonomous Electric Vehicles (MAEV) have shown to provide in-motion transfer and flexible capacity to the demand responsive transit (DRT). However, needs-based charging strategy for the MAEV based DRT systems may reduce the utilization of the MVs during the peak hours. To address this issue, this paper proposes a mixed integer linear programming model for optimizing the route and charging planning of the DRT service, where passenger transfers are assigned to schedule partial charging time between service trips. To deal with the hard problem, an adaptive large neighbourhood search algorithm is developed. A case study regarding the realworld parameters and three numerical testing sets is conducted to show the efficiency and effectiveness of the proposed method. Results show the proposed method has 19.62 %, 12.65 % and 26.81 % reductions on the total system cost in comparison to the MAEV based DRT with the needs-based charging strategy, the comparable system considering transferring at a point, and non-transfer DRT, respectively.