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

Abstract

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

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

“…This model permitted the AMB to uncouple from one vehicle and couple to another vehicle in either direction on a bi-directional route. Guo et al [42] applied AMBs to customized on-demand bus services and designed a two-phase method to determine the optimal AMB routing plan and charging plan.…”

“…Figure 1 gives an example of three AMBs coupling together to serve a trip. et al [42] applied AMBs to customized on-demand bus services and designed a two-phase method to determine the optimal AMB routing plan and charging plan.…”

Sustainability Opportunities and Ethical Challenges of AI-Enabled Connected Autonomous Vehicles Routing in Urban Areas

“…The coupling of CAVs and AI can foster innovative approaches for public transport optimisation, supporting adhoc strategies from local authorities aimed at prioritising the use of public transport systems over personal vehicles also by providing more timely and accurate information to users and more frequent services due to the fact that no human drivers will be needed [18], [19]. CAVs can support effective and efficient demand-responsive public transport [20], as well as improve the timeliness of service in fixed-time transport service by supporting prioritisation of traffic signals and by speed recommendations to vehicles to exploit green waves.…”