Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand Systems in Mixed Traffic

Area of Science:

• Transportation Engineering
• Operations Research
• Urban Planning

Background:

• Autonomous Mobility-on-Demand (AMoD) systems are emerging as a transformative mobility solution.
• Understanding the impact of AMoD on traffic congestion is crucial for sustainable urban transportation.
• Existing models often overlook the endogenous effects of AMoD on travel times and exogenous impacts from private vehicles.

Purpose of the Study:

• To develop and evaluate congestion-aware route-planning policies for intermodal AMoD systems.
• To optimize AMoD routing and rebalancing strategies considering mixed traffic conditions.
• To assess the benefits of integrating AMoD with public transit and other mobility options.

Main Methods:

• Devised a network flow model for joint optimization of AMoD routing and rebalancing.
• Incorporated congestion effects from both AMoD and private vehicles using a sequential approach.
• Developed algorithms to derive explicit user routes from link flow results.
• Validated the framework using case studies in Eastern Massachusetts and New York City.

Main Results:

• Pure AMoD systems can be detrimental at high demand due to rebalancing traffic.
• Blending AMoD with public transit, walking, and micromobility significantly enhances system performance.
• High-throughput public transit combined with flexible micro-mobility options offers a synergistic advantage.
• Congestion-aware policies are essential for efficient AMoD integration.

Conclusions:

• Intermodal AMoD systems, when strategically integrated, offer a sustainable path for urban mobility.
• Policy interventions focusing on mode integration are vital to mitigate congestion.
• The proposed framework provides a robust tool for planning and managing future mobility systems.