Modular Autonomous Electric Vehicle Scheduling for Customized On-Demand Bus Services

The emerging customized bus system based on modular autonomous electric vehicles (MAEVs) shows tremendous potential to improve the mobility, accessibility and environmental friendliness of a public transport system. However, existing studies in this area almost focus human-driven which face some striking limitations (e.g., restricted crew scheduling fixed vehicle capacity) can weaken overall benefits. This paper proposes two-phase optimization procedure fully unleash MAEVs by leveraging strengths MAEVs, including automatic allocation charging modules. In first phase, mixed integer programming model is established space-time-state framework jointly optimize MAEV routing charging, passenger-to-vehicle assignment capacity management for reserved passengers. A Lagrangian relaxation algorithm developed solve efficiently. second three dispatching strategies are designed optimized dynamic properly adapt operation travel demands. case study conducted major urban Beijing, China, demonstrates high efficiency adoption terms resource utilization across range demand distributions, supply module scenarios.