Scalable Multimodal Ridesharing for Route and Emission Optimization

This study presents a mixed-integer linear programming (MILP) framework for optimizing a multimodal transportation system integrating ground mobility-on-demand (GMoD) and air mobility-on-demand (AirMoD) services. The proposed model aims to minimize user waiting time and CO2 emissions while maximizing service rates and vehicle utilization. The multimodal system is evaluated against a GMoD-only baseline across various scales and system phases, demonstrating the effectiveness of incorporating air taxis into urban transportation networks. Results indicate that the multimodal system significantly improves service rates and reduces waiting times compared to the GMoD-only baseline, particularly in medium- and large-scale networks. The integration of Yen’s algorithm within the MILP-GMoD model enhances computational efficiency of ground taxi routing. Analysis of different k values suggests that k = 2 provides a balanced trade-off between routing flexibility, cost efficiency, and computational feasibility. The findings highlight the potential of air taxis to complement traditional ground transportation, offering a scalable and sustainable solution for urban mobility.