This work reconsiders the concept of community-based trip sharing proposed by Hasan et al. (2018) that leverages the structure of commuting patterns and urban communities to optimize trip sharing. It aims at quantifying the benefits of autonomous vehicles for community-based trip sharing, compared to a car-pooling platform where vehicles are driven by their owners. In the considered problem, each rider specifies a desired arrival time for her inbound trip (commuting to work) and a departure time for her outbound trip (commuting back home). In addition, her commute time cannot deviate too much from the duration of a direct trip. Prior work motivated by reducing parking pressure and congestion in the city of Ann Arbor, Michigan, showed that a car-pooling platform for community-based trip sharing could reduce the number of vehicles by close to 60%.

This paper studies the potential benefits of autonomous vehicles in further reducing the number of vehicles needed to serve all these commuting trips. It proposes a column-generation procedure that generates and assembles mini routes to serve inbound and outbound trips, using a lexicographic objective that first minimizes the required vehicle count and then the total travel distance. The optimization algorithm is evaluated on a large-scale, real-world dataset of commute trips from the city of Ann Arbor, Michigan. The results of the optimization show that it can leverage autonomous vehicles to reduce the daily vehicle usage by 92%, improving upon the results of the original Commute Trip Sharing Problem by 34%, while also reducing daily vehicle miles traveled by approximately 30%. These results demonstrate the significant potential of autonomous vehicles for the shared commuting of a community to a common work destination.

这项工作重新考虑了Hasan等人提出的基于社区的旅行共享概念。（2018）利用通勤模式和城市社区的结构来优化出行共享。与将车由其所有者驾驶的拼车平台相比，该工具旨在量化自动驾驶车在社区出行共享中的收益。在所考虑的问题中，每个骑手为入站旅行（通勤上班）指定期望的到达时间，并为出站旅行（回家返程）指定出发时间。此外，她的上下班时间与直接旅行的时间不会有太大差异。在密歇根州安阿伯市，通过减少停车压力和交通拥堵而进行的先前工作表明，基于社区出行共享的拼车平台可以将车辆数量减少近60％。

本文研究了自动驾驶汽车在进一步减少为所有这些通勤旅行服务所需的汽车数量方面的潜在好处。它提出了一种列生成程序，该程序使用词典编目物镜来生成和组装用于进站和出站旅行的微型路线，该程序首先使所需的车辆数量最小，然后使总行驶距离最小。在来自密歇根州安阿伯市的通勤旅行的大规模，真实世界数据集上评估了优化算法。优化的结果表明，它可以利用自动驾驶汽车将每日车辆使用量减少92％，将原始的“通勤出行共享问题”的结果改善了34％，同时还使每日行驶的汽车行驶里程减少了约30％。