Parking pressure has been steadily increasing in cities as well as in university and corporate campuses. To relieve this pressure, this paper studies a car-pooling platform that would match riders and drivers, while guaranteeing a ride back and exploiting spatial and temporal locality. In particular, the paper formalizes the Commute Trip Sharing Problem (CTSP) to find a routing plan that maximizes ride sharing for a set of commute trips. The CTSP is a generalization of the vehicle routing problem with routes that satisfy time window, capacity, pairing, precedence, ride duration, and driver constraints. The paper introduces two exact algorithms for the CTPS: A route-enumeration algorithm and a branch-and-price algorithm. Experimental results show that, on a high-fidelity, real-world dataset of commute trips from a mid-size city, both algorithms optimally solve small and medium-sized problems and produce high-quality solutions for larger problem instances. The results show that car pooling, if widely adopted, has the potential to reduce vehicle usage by up to 57% and decrease vehicle miles traveled by up to 46% while only incurring a 22% increase in average ride time per commuter for the trips considered.

中文翻译：

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通勤出行共享问题

在城市以及大学和企业园区中，停车压力一直在稳定增长。为了缓解这种压力，本文研究了一种可与骑手和驾驶员相匹配的拼车平台，同时保证了回程并利用了时空局部性。特别是，本文对通勤出行共享问题（CTSP）进行了形式化，以找到一种路线计划，该方案可以使一组通勤出行的乘车共享最大化。CTSP是车辆路线问题的一般化，其路线满足时间窗口，容量，配对，优先级，行驶时间和驾驶员约束。本文介绍了CTPS的两种精确算法：路由枚举算法和分支价格算法。实验结果表明，在来自中型城市的高保真，真实世界的通勤旅行数据集上，两种算法都可以最佳地解决中小型问题，并针对较大的问题实例提供高质量的解决方案。结果表明，如果集中使用车池，则有可能将车辆使用量减少多达57％，将行驶里程减少多达46％，而每个通勤者的平均乘车时间仅增加22％ 。