Hu Songhua, Xiong Chenfeng, Liu Zhanqin, Zhang Lei
Maryland Transportation Institute (MTI), Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, 20742, United States of America.
Shock Trauma and Anesthesiology Research (STAR) Center, School of Medicine, University of Maryland, Baltimore, MD 21201, United States of America.
J Transp Geogr. 2021 Feb;91:102997. doi: 10.1016/j.jtrangeo.2021.102997. Epub 2021 Feb 19.
The COVID-19 pandemic has led to a globally unprecedented change in human mobility. Leveraging two-year bike-sharing trips from the largest bike-sharing program in Chicago, this study examines the spatiotemporal evolution of bike-sharing usage across the pandemic and compares it with other modes of transport. A set of generalized additive (mixed) models are fitted to identify relationships and delineate nonlinear temporal interactions between station-level daily bike-sharing usage and various independent variables including socio-demographics, land use, transportation features, station characteristics, and COVID-19 infections. Results show: 1) the proportion of commuting trips is substantially lower during the pandemic; 2) the trend of bike-sharing usage follows an "increase-decrease-rebound" pattern; 3) bike-sharing presents as a more resilient option compared with transit, driving, and walking; 4) regions with more white, Asian, and fewer African-American residents are found to become less dependent on bike-sharing; 5) open space and residential areas exhibit less decrease and earlier start-to-recover time; 6) stations near the city center, with more docks, or located in high-income areas go from more increase before the pandemic to more decrease during the pandemic. Findings provide a timely understanding of bike-sharing usage changes and offer suggestions on how different stakeholders should respond to this unprecedented crisis.
新冠疫情导致全球人类出行发生了前所未有的变化。本研究利用芝加哥最大的共享单车项目的两年骑行数据，考察了疫情期间共享单车使用情况的时空演变，并将其与其他交通方式进行了比较。通过拟合一组广义相加（混合）模型，以确定站点级每日共享单车使用量与包括社会人口统计学、土地利用、交通特征、站点特征以及新冠感染病例等各种自变量之间的关系，并描绘非线性时间交互作用。结果表明：1）疫情期间通勤出行的比例大幅降低；2）共享单车使用量的趋势呈“增加-减少-反弹”模式；3）与公交、驾车和步行相比，共享单车是一种更具韧性的出行选择；4）发现白人、亚裔居民较多而非洲裔居民较少的地区对共享单车的依赖程度降低；5）开放空间和居民区的使用量下降较少且恢复时间较早；6）市中心附近、有更多停车桩或位于高收入地区的站点，从疫情前使用量增加较多转变为疫情期间下降较多。研究结果及时揭示了共享单车使用情况的变化，并就不同利益相关者应如何应对这一前所未有的危机提供了建议。
