The Commute Trip Sharing Problem (CTSP) was introduced to remove parking pressure on cities, as well as corporate and university campuses. Its goal is to reduce the number of vehicles being used for daily commuting activities. Given a set of inbound and outbound requests, which consists of origin and destination pairs and their departure and return times, the CTSP assigns riders and a driver, as well as inbound and outbound routes, to each vehicle in order to satisfy time-window, capacity, and ride-duration constraints. The CTSP guarantees a ride back for each rider, which is a critical aspect of such a ride-sharing system. This paper generalizes the CTSP to account for uncertainties about the return trip. Each rider is assumed to have a return time specified by a distribution (learned from historical data) and, each day, a percentage of riders will want to preprone or postpone their return trip to accommodate some schedule changes. The paper proposes two generalizations of the CTSP: the Flexible CTSP (FCTSP) and the Real-Time CTSP (RT-CTSP). In the FCTSP, riders must confirm their final return times by a fixed deadline. In the RT-CTSP, riders confirm their new return times in real time with some prior notice. The paper proposes a two-step approach to address the FCTSP and the RT-CTSP. The first step uses a scenario-based stochastic program to choose the drivers and the morning routes in order to maximize the robustness of the driver assignment. The second step reoptimizes the plan at the fixed deadline or in real time once the return times are confirmed. Experiments on a real-world dataset of commute trips demonstrate the effectiveness of the algorithm in generating robust plans and reveal a trade-off between vehicle reduction and plan robustness as the robust plans tend to be conservative. A method is then proposed to evaluate this trade-off using the per-unit price ratio of vehicle increase to uncovered riders.

引入了通勤旅行共享问题（CTSP），以消除对城市以及公司和大学校园的停车压力。其目标是减少用于日常通勤活动的车辆数量。给定一组入站和出站请求，其中包括出发地和目的地对以及它们的出发和返回时间，CTSP会为每辆车分配乘员和驾驶员以及进出路线，以满足时间窗口的要求，容量和行驶时间限制。CTSP保证了每个骑手的回程，这是这种骑行共享系统的关键方面。本文对CTSP进行了概括，以解决有关回程的不确定性。假定每个骑手都有一个由分布（从历史数据中获悉）指定的返回时间，并且每天，一定比例的车手希望提前或推迟回程，以适应某些日程安排的变化。本文提出了CTSP的两种概括：柔性CTSP（FCTSP）和实时CTSP（RT-CTSP）。在FCTSP中，车手必须在固定的截止日期之前确认最终的返回时间。在RT-CTSP中，车手可以事先通知并实时确认新的返回时间。本文提出了解决FCTSP和RT-CTSP的两步方法。第一步使用基于场景的随机程序选择驾驶员和早晨路线，以最大化驾驶员分配的稳定性。一旦确定了返回时间，第二步将在固定的期限或实时重新优化计划。在现实世界的通勤旅行数据集上进行的实验证明了该算法在生成稳健计划中的有效性，并揭示了在稳健计划趋于保守的情况下，车辆减少量与稳健性之间的权衡。然后提出一种方法，使用增加的车辆与未发现的骑手的单价比率来评估这种折衷。