An MILP-Based Framework for Multi-Modal Transportation with Air Taxis

The integration of air taxis into multimodal transportation systems presents a transformative opportunity to enhance urban mobility by reducing congestion, improving service reliability, and minimizing environmental impact. This paper develops a Mixed Integer Linear Programming (MILP)-based optimization framework that seamlessly integrates ground and air taxi services, addressing the complexities of multimodal ride-matching and routing. The proposed framework leverages Yen’s algorithm to efficiently compute k-shortest paths for ground transportation while simultaneously optimizing vehicle assignments and routes across both ground and air modes. Extensive numerical experiments, including large-scale case studies in Chicago, demonstrate that the multimodal system significantly reduces vehicle usage, operational costs, and carbon emissions while maintaining high service quality. Results show that integrating air taxis improves service rates by up to 5% in high-demand scenarios and reduces carbon emissions by over 60% compared to a ground-only mobility system. Additionally, computational efficiency is enhanced, with the multimodal system requiring 30-50% less processing time than traditional optimization approaches. These findings highlight the feasibility and benefits of a fully integrated urban mobility system that leverages air taxis, providing valuable insights for policymakers and transportation planners aiming to develop sustainable and scalable mobility solutions.