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

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