With the recent rapid growth of technology-enabled mobility services, ride-sourcing platforms, such as Uber and DiDi, have launched commercial on-demand ride-pooling programs that allow drivers to serve more than one passenger request in each ride. Without requiring the prearrangement of trip schedules, these programs match on-demand passenger requests with vehicles that have vacant seats. Ride-pooling programs are expected to offer benefits for both individual passengers in the form of cost savings and for society in the form of traffic alleviation and emission reduction. In addition to some exogenous variables and environments for ride-sourcing market, such as city size and population density, three key decisions govern a platform's efficiency for ride-pooling services: trip fare, vehicle fleet size, and allowable detour time. An appropriate discounted fare attracts an adequate number of passengers for ride-pooling, and thus increases the successful pairing rate, while an appropriate allowable detour time prevents passengers from giving up ride-pooling service. This paper develops a mathematical model to elucidate the complex relationships between the variables and decisions involved in a ride-pooling market. We find that the monopoly optimum, social optimum and second-best solutions in both ride-pooling and non-pooling markets are always in a normal regime rather than the wild goose chase (WGC) regime—an inefficient equilibrium in which drivers spend substantial time on picking up passengers. Besides, in general, a unit decrease in trip fare in a ride-pooling market attracts more passengers than would a non-pooling market, because it not only directly increases passenger demand due to the negative price elasticity, but also reduces actual detour time, which in turn indirectly increases ride-pooling passenger demand. As a result, we prove that monopoly optimum, social optimum and second-best solution trip fares in a ride-pooling market are lower than that in a non-pooling market under certain conditions. These theoretical findings are further verified by a set of numerical studies.

随着基于技术的出行服务的快速增长，Uber和DiDi等出行平台已经启动了商业按需出行拼车计划，该程序允许驾驶员在每次出行中满足多个乘客的要求。这些程序无需预先安排行程表，即可将按需乘客请求与座位空缺的车辆相匹配。骑乘联乘计划有望以节省成本的形式为个人乘客带来好处，并以交通减少和减少排放的形式为社会带来好处。除了用于乘车来源市场的一些外在变量和环境（例如城市规模和人口密度）以外，三个关键决策还决定着平台对乘车服务的效率：出行票价，车队规模和允许的绕行时间。适当的折扣票价吸引了足够数量的乘客搭车，从而增加了成功的配对率，而适当的允许的tour回时间则阻止了乘客放弃搭车服务。本文开发了一个数学模型来阐明乘车市场中变量与决策之间的复杂关系。我们发现，在拼车和非拼车市场中，垄断最优，社会最优和次优解决方案始终处于正常状态，而不是 本文开发了一个数学模型来阐明乘车市场中变量与决策之间的复杂关系。我们发现，在拼车和非拼车市场中，垄断最优，社会最优和次优解决方案始终处于正常状态，而不是 本文开发了一个数学模型来阐明乘车市场中变量与决策之间的复杂关系。我们发现，在拼车和非拼车市场中，垄断最优，社会最优和次优解决方案始终处于正常状态，而不是大雁追捕（WGC）制度-一种效率低下的平衡机制，驾驶员需要花费大量时间来接送乘客。此外，总的来说，乘车拼车市场单价下跌比非拼车市场吸引更多的乘客，因为这不仅会由于负价格弹性而直接增加乘客需求，而且会减少实际的reduces回时间，这反过来间接增加了拼车乘客的需求。结果，我们证明了在某些情况下，拼车市场中的垄断最优，社会最优和次佳解决方案票价要低于非拼车市场中的最优票价。这些理论发现通过一组数值研究得到进一步证实。