Data-driven analysis on matching probability, routing distance and detour distance in ride-pooling services

Abstract

By serving two or more passenger requests in each ride in ride-sourcing markets, ride-pooling service is now becoming an important component of shared smart mobility. It is generally expected to improve vehicle utilization rate, and therefore alleviate traffic congestion and reduce carbon dioxide emissions. A few recent theoretical studies are conducted, mainly focusing on the equilibrium analysis of the ride-sourcing markets with ride-pooling services and the impacts of ride-pooling services on transit ridership and traffic congestion. In these studies, there are three key measures that distinguish ride-pooling service analysis from the non-pooling ride-sourcing market analysis. The first is the proportion of passengers who are pool-matched(referred to as pool-matching probability), the second is passengers’ average detour distance, and the third is average vehicle routing distance to pick up and drop off all passengers with different origins and destinations in one specific ride. These three measures are determined by passenger demand for ride-pooling and matching strategies. However, due to the complex nature of ride-resourcing market, it is difficult to analytically determine the relationships between these measures and passenger demand. To fill this research gap, this paper attempts to empirically ascertain these relationships through extensive experiments based on the actual on-demand mobility data obtained from Chengdu, Haikou, and Manhattan. We are surprised to find that the relationships between the three measures (pool-matching probability, passengers’ average detour distance, average vehicle routing distance) and number of passengers in the matching pool (which reflects passenger demand) can be fitted by some simple curves (with fairly high goodness-of-fit) or there exist elegant empirical laws on these relationships. Our findings are insightful and useful to theoretical modeling and applications in ride-resourcing markets, such as evaluation of the impacts of ride-pooling on transit usage and traffic congestion.