Data-Driven Analysis of Matching Probability, Routing Distance and Detour Distance In On-Demand Ride-Pooling Services

By serving two or more passenger requests in each ride in ride-sourcing markets, ride-splitting 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-splitting services and the impacts of ride-splitting services on transit ridership and traffic congestions. In these studies, there are three key measures that distinguish ride-splitting service analysis from the normal non-ride-splitting 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 drivers' routing distance to pick-up and drop-off all passengers with different origins and destinations in one specific ride. These measures are determined by passenger demand for ride-splitting 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, drivers' average 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 relationship. Our findings are insightful and useful to theoretical modeling and applications in ride-resourcing markets, such as evaluation of the impacts of ride-splitting on transit usage and traffic congestions