电动滑板车是否填补了COVID-19之前和期间的流动性差距并促进了公平？利用开放大数据进行时空分析
Do e-scooters fill mobility gaps and promote equity before and during COVID-19? A spatiotemporal analysis using open big data
摘要：
电动滑板车的日益普及及其在城市街道上的快速扩张引起了广泛关注。一个主要的政策问题是，电动滑板车是替代现有的移动选项，还是填补它们留下的服务空白。本研究通过分析华盛顿特区电动滑板车服务可用性和使用的时空模式来解决这个问题，重点关注它们与公共交通和自行车共享的空间关系。对三个开放大数据集的分析结果表明，电动滑板车对公共交通和自行车共享服务既有竞争作用，也有互补作用。电动滑板车的供应与公共交通和自行车共享的服务领域明显重叠，我们将大多数电动滑板车旅行归类为公共交通和自行车共享用途的替代品。基于旅行时间的分析进一步表明，当选择电动滑板车而不是交通工具时，旅行者支付了价格溢价并节省了一些旅行时间。在COVID-19大流行期间，价格溢价更大，但相关的旅行时间节省较小。这意味着公共卫生考虑而不是时间成本权衡是许多人在COVID期间选择电动滑板车而不是交通的主要驱动力。此外，我们发现电动滑板车通过为服务不足的社区提供服务来补充自行车共享和交通。相当大一部分（约10%）的电动滑板车旅行是与铁路服务连接的。未来的研究可能会将本文提出的基于大数据的分析与传统方法相结合，以进一步揭示电动滑板车服务，自行车共享和公共交通之间的相互作用。
英文摘要：
The growing popularity of e-scooters and their rapid expansion across urban streets has attracted widespread attention. A major policy question is whether e-scooters substitute existing mobility options or fill the service gaps left by them. This study addresses this question by analyzing the spatiotemporal patterns of e-scooter service availability and use in Washington DC, focusing on their spatial relationships with public transit and bikesharing. Results from an analysis of three open big datasets suggest that e-scooters have both competing and complementary effects on transit and bikesharing services. The supply of e-scooters significantly overlaps with the service areas of transit and bikesharing, and we classify a majority of e-scooter trips as substitutes to transit and bikesharing uses. A travel-time-based analysis further reveals that when choosing e-scooters over transit, travelers pay a price premium and save some travel time. The price premium is greater during the COVID-19 pandemic but the associated travel-time savings are smaller. This implies that public health considerations rather than time-cost tradeoffs are the main driver for many to choose e-scooters over transit during COVID. In addition, we find that e-scooters complement bikesharing and transit by providing services to underserved neighborhoods. A sizeable proportion (about 10 percent) of e-scooter trips are taken to connect with the rail services. Future research may combine the big-data-based analysis presented here with traditional methods to further shed light on the interactions between e-scooter services, bikesharing, and public transit.
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。
