Accelerating High-Capacity Ridepooling in Robo-Taxi Systems

Rapid urbanization has increased demand for customized urban mobility, making on-demand services and robo-taxis central to future transportation. The efficiency of these systems hinges on real-time fleet coordination algorithms. This work accelerates the state-of-the-art high-capacity ridepooling framework by identifying its computational bottlenecks and introducing two complementary strategies: (i) a data-driven feasibility predictor that filters low-potential trips, and (ii) a graph-partitioning scheme that enables parallelizable trip generation. Using real-world Manhattan demand data, we show that the acceleration algorithms reduce the optimality gap by up to 27% under real-time constraints and cut empty travel time by up to 5%. These improvements translate into tangible economic and environmental benefits, advancing the scalability of high-capacity robo-taxi operations in dense urban settings.