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.

通过在乘车外包市场的每次乘车中满足两个或更多乘客的需求，乘车拼车服务现已成为共享智能出行的重要组成部分。通常期望提高车辆利用率，从而减轻交通拥堵并减少二氧化碳排放。最近进行了一些理论研究，主要侧重于对具有拼车服务的拼车市场的均衡分析，以及拼车服务对过境乘车和交通拥堵的影响。在这些研究中，有三项关键措施将拼车服务分析与非拼车来源市场分析区分开来。第一个是池匹配的乘客比例（称为池匹配概率），第二个是乘客的平均de回距离，第三个是平均车辆路线距离，该距离是指在一次特定乘车中上落所有具有不同出发地和目的地的乘客的平均路线。这三种措施取决于乘客对拼车和匹配策略的需求。但是，由于乘车资源市场的复杂性，很难分析确定这些措施与乘客需求之间的关系。为了填补这一研究空白，本文尝试根据从成都，海口和曼哈顿获得的实际按需出行数据，通过广泛的实验以经验方式确定这些关系。我们惊讶地发现这三个量度之间的关系（池匹配概率，乘客的平均tour回距离，平均车辆路线距离）和匹配池中的乘客数量（反映乘客需求）可以通过一些简单的曲线（拟合优度很高）进行拟合，或者在这些关系上存在完善的经验定律。我们的发现很有见地，对于乘车资源市场中的理论模型和应用（例如评估乘车对交通使用和交通拥堵的影响）很有用。