Sun Feiyang, Chen Peng, Jiao Junfeng
Department of Urban Design and Planning, University of Washington, Seattle, WA, USA.
College of Transportation Engineering, Tongji University, Shanghai, China.
Transp Res D Transp Environ. 2018 Aug;63:533-547. doi: 10.1016/j.trd.2018.06.021. Epub 2018 Jun 28.
In 2014, Seattle implemented its own bike-sharing system, Pronto. However, the system ultimately ceased operation three years later on March 17th, 2017. To learn from this failure, this paper seeks to understand factors that encourage, or discourage, bike-sharing trip generation and attraction at the station level. This paper investigates the effects of land use, roadway design, elevation, bus trips, weather, and temporal factors on three-hour long bike pickups and returns at each docking station. To address temporal autocorrelations and the nonlinear seasonality, the paper implements a generalized additive mixed model (GAMM) that incorporates the joint effects of a time metric and time-varying variables. The paper estimates models on total counts of pickups and returns, as well as pickups categorized by user types and by location. The results clarify that effects of hilly terrain and the rainy weather, two commonly perceived contributors to the failure. Additionally, results suggest that users in the University District, presumably mostly university students, tend to use shared bikes in neighborhoods with a higher household density and a higher percentage of residential land use, and make bike-sharing trips regardless workdays or non-workdays. The paper also contributes to the discussion on the relationship between public transportation service and bike-sharing. In general, users tend to use bike-sharing more at stations that have more scheduled bus trips nearby. However, some bike-sharing users may shift to bus services during peak hours and rainy weather. Several strategies are proposed accordingly to increase bike ridership in the future.
2014年，西雅图推出了自己的共享单车系统——Pronto。然而，该系统最终在三年后的2017年3月17日停止运营。为了从这次失败中吸取教训，本文旨在了解在站点层面上鼓励或阻碍共享单车出行生成及吸引的因素。本文研究了土地利用、道路设计、海拔、公交出行、天气和时间因素对每个停靠站三小时时长的自行车取车和还车情况的影响。为了解决时间自相关和非线性季节性问题，本文实施了一个广义相加混合模型（GAMM），该模型纳入了时间度量和随时间变化变量的联合效应。本文对取车和还车的总数以及按用户类型和位置分类的取车情况进行了模型估计。结果表明，丘陵地形和阴雨天气的影响，这两个因素通常被认为是导致失败的原因。此外，结果表明，大学区的用户，大概大多是大学生，倾向于在家庭密度较高且住宅用地比例较高的社区使用共享单车，并且无论工作日还是非工作日都会进行共享单车出行。本文还为关于公共交通服务与共享单车之间关系的讨论做出了贡献。总体而言，用户倾向于在附近有更多定时公交出行的站点更多地使用共享单车。然而，一些共享单车用户可能会在高峰时段和阴雨天气转向公交服务。相应地提出了几种策略，以在未来增加自行车出行量。
