Ridesourcing platforms, such as Uber, Lyft and Didi, are now launching commercial on-demand ride-pooling programs that enable their affiliated drivers to serve two or more passengers in one ride. It is generally expected that successful designs of ride-pooling programs can reduce the required vehicle fleet size, and achieve various societally beneficial objectives, such as alleviating traffic congestion. The reduction in traffic congestion can in turn save travel time for both ridesourcing passengers and normal private car users. However, it is still unclear to what extent the implementation of ride-pooling affects traffic congestion and riders’ travel time. To this end, this paper establishes a model to describe the ridesourcing markets with congestion effects, which are explicitly characterized by a macroscopic fundamental diagram. We compare the time cost (sum of travel time and waiting time) of ridesourcing passengers and normal private car users (background traffic) in the ridesourcing markets without ride-pooling (each vehicle serves one passenger) and with ride-pooling (each vehicle serves one or more passengers). It is found that, a win-win situation can be achieved under some scenarios such that the implementation of on-demand ride-pooling reduces the time cost for both ridesourcing passengers and private car users. Furthermore, we find that the matching window is a key decision variable the platform leverages to affect the stationary equilibrium state. As the matching window increases, passengers are expected to wait for longer time, but the pool-matching probability (the proportion of passengers who are pool-matched) increases, which further alleviates traffic congestion and in turn reduces passengers’ travel time. It is interesting to find that, there is a globally optimal matching window for achieving the minimum time cost for ridesourcing passengers in the normal flow regime.

优步（Uber），Lyft和滴滴（Didi）等Ridesourcing平台现在正在启动商业按需乘车拼车计划，使他们的下属驾驶员能够在一次乘车中为两名或以上乘客提供服务。通常预期，成功的拼车计划设计可以减少所需的车队规模，并实现各种对社会有益的目标，例如减轻交通拥堵。交通拥堵的减少又可以节省乘车出行旅客和普通私家车使用者的出行时间。但是，目前尚不清楚骑行骑行的实施在多大程度上影响交通拥堵和骑行者的出行时间。为此，本文建立了一个模型来描述具有拥挤效应的拼车市场，并通过宏观基本图明确地对其进行了描述。我们比较了在没有拼车（每辆车为一名乘客服务）和拼车（每辆车为服务）的情况下，拼车市场上的拼车乘客和普通私家车用户（背景交通）的时间成本（旅行时间和等待时间之和）一名或多名乘客）。可以发现，在某些情况下可以实现双赢，因此按需拼车的实施可以减少拼车乘客和私家车用户的时间成本。此外，我们发现匹配窗口是平台用来影响平稳平衡状态的关键决策变量。随着匹配窗口的增加，预计乘客将等待更长的时间，但是池匹配的可能性（池匹配的乘客比例）增加，这进一步减轻了交通拥堵，从而减少了乘客的出行时间。有趣的是，存在一个全局最佳匹配窗口，可在正常流量状态下为骑乘乘客实现最小时间成本。