拼车出行的规模效应：以芝加哥市为例
Scale Effects in Ridesplitting: A Case Study of the City of Chicago
AI总结：
本研究以芝加哥市2019年1-9月的TNC行程数据为依据，验证了拼车出行的规模经济与规模报酬递增效应，即用户量增长会提升服务质量、降低成本并提高用户拼车意愿。
摘要：
拼车出行（ridesplitting）是网约车（ride-hailing）的一种，乘客可与其他乘客共享车辆，目前已成为部分大城市的常见出行方式。这类共享出行选项目前由Uber、Lyft、Via等交通网络公司（TNCs）提供，吸引了越来越多的用户，在新冠疫情之前尤其如此。以往研究表明，拼车可降低出行成本，甚至能通过减少载客所需的车辆数量缓解交通拥堵。近期研究还提出，拼车出行应存在正反馈机制，即服务质量会随用户数量增加而提升，具体而言，这类系统应能受益于规模经济和规模报酬递增。本文利用2019年1月至9月交通网络公司（TNCs）上报给芝加哥市的行程数据，证明了上述效应的存在。研究具体显示，在给定时间段内，请求或授权共享行程的乘客数量增加，与更短的行程绕行距离、更高的乘客配对成功率、相对非共享行程更低的成本，以及乘客更高的拼车意愿相关。
英文摘要：
Ridesplitting -- a type of ride-hailing in which riders share vehicles with other riders -- has become a common travel mode in some major cities. This type of shared ride option is currently provided by transportation network companies (TNCs) such as Uber, Lyft, and Via and has attracted increasing numbers of users, particularly before the COVID-19 pandemic. Previous findings have suggested ridesplitting can lower travel costs and even lessen congestion by reducing the number of vehicles needed to move people. Recent studies have also posited that ridesplitting should experience positive feedback mechanisms in which the quality of the service would improve with the number of users. Specifically, these systems should benefit from economies of scale and increasing returns to scale. This paper demonstrates evidence of their existence using trip data reported by TNCs to the City of Chicago between January and September 2019. Specifically, it shows that increases in the number of riders requesting or authorizing shared trips during a given time period is associated with shorter trip detours, higher rates of riders being matched together, lower costs relative to non-shared trips, and higher willingness for riders to share trips.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
