Order Dispatching in Ride-Pooling with Walking Points Search

Abstract

Ride-pooling has significantly enhanced the system efficiency of current on-demand ride-sharing services. However, as the numbers of on-board passengers increase, more detours inevitably occur since ride-pooling provides door-to-door service for everyone. To solve this problem, we focus on rider-participating dispatch by searching walking points, equivalent to alternative pick-up points from origins and alternative drop-off points from destinations. Based on the existing framework for large-scale ride-pooling, we develop our walking point search algorithm, which finds cost-minimizing alternatives. In addition, our approach enables the model to reflect the sensitivity of riders to given walking points by introducing the probability of riders’ acceptance. We conduct a simulation with the Yellow Cap Taxi data set in New York City to validate and compare with the base model, which does not include walking. The results show an increase from 69.56% to 77.84% in the service rate, an improvement of 18.2% in delay time, and 8.6% in in-vehicle time. With the increased service rate, the average travel times of vehicles are reduced by 1.5%, allowing drivers to spend more time rebalancing. Furthermore, we show that the effect of walking is maximized in high-demand areas during peak hours. This study demonstrates that walking can substantially enhance operational efficiency, mitigating the supply-demand imbalance with limited fleets. The proposed model can also be used in optimizing the meeting points for various high-capacity vehicles, such as on-demand shuttles.