MINLP Model for Optimal Assignment and Scheduling of a Multimodal Transportation Network

Air taxi services have the potential to revolutionize metropolitan transportation by integrating with ground transportation systems to reduce the number of vehicles required, reduce greenhouse gas emissions, and improve the economic viability of transportation network companies. However, most studies address vehicle assignment and scheduling problems separately, with limited focus on jointly optimizing these in multimodal systems. Additionally, challenges specific to air taxi ridesharing, such as energy optimization across flight phases and integration with ground transportation, remain underexplored. This work proposes a mixed integer nonlinear programming (MINLP) model to optimize vehicle assignments and schedules, accounting for energy consumption and service quality. The model integrates ground and air transportation within a unified framework, using batch optimization with rolling horizons to enhance scalability. Results show that optimal solutions are found in under 2 minutes for systems with up to 200 air taxis, 500 service requests, and 6 skyports, demonstrating practical applicability. Furthermore, the model evaluates greenhouse gas emissions under multimodal scenarios, revealing a 40.47% reduction in [Formula: see text] emissions in the 30%GroundTaxi–70%AirTaxi scenario compared to the 100%GroundTaxi scenario. These findings highlight the potential of the model to enhance the sustainability and efficiency of urban transportation systems.