It is important to find ride matches for individuals who participate in ridesharing quickly, and it is equally important to minimize the number of drivers to serve all individuals and minimize the total travel distance of the vehicles. This paper considers the following ridesharing problem: given a set of trips, each trip consists of an individual, a vehicle of the individual and some requirements, select a subset of trips and use the vehicles of selected trips to deliver all individuals to their destinations while satisfying the requirements and achieving some optimization goal. Requirements of trips are specified by parameters including source, destination, vehicle capacity, preferred paths, detour distance and number of stops a driver is willing to make, and time constraints. We consider two optimization problems: minimizing the number of selected vehicles and minimizing total travel distance of the vehicles. We prove that it is NP-hard to approximate both minimization problems within a constant factor if any one of the requirements related to the detour distance, preferred paths, number of stops and time constraints is not satisfied. We give $\frac{K+2}{2}$-approximation algorithms for minimizing the number of selected vehicles when the requirement related to the number of stops is not satisfied, where K is the largest capacity of all vehicles.

找到快速参与拼车的个人的比赛很重要，并且最小化为所有个人服务的驾驶员人数并最小化车辆的总行驶距离也同样重要。本文考虑以下共享乘车问题：给定一组行程，每个行程由一个人，一个人的车辆和一些要求组成，选择一个行程子集，并使用选定行程的车辆将所有人员运送到目的地满足要求并实现一些优化目标。行程的要求由参数指定，包括来源，目的地，车辆容量，首选路线、,回距离和驾驶员愿意停车的次数以及时间限制。我们考虑两个优化问题：最小化所选车辆的数量并最小化车辆的总行驶距离。我们证明，如果不满足与the回距离，首选路径，停靠点数和时间限制相关的任何一项要求，则很难在一个恒定因子内近似两个最小化问题。我们给予$\frac{ķ+\mathrm{2个}}{\mathrm{2个}}$-近似算法，用于在不满足与停车次数相关的要求的情况下最小化所选车辆的数量，其中K是所有车辆的最大容量。