The COVID-19 has led to significant changes in urban travel behaviors, with commuting being one of the most affected travel modes. Commuting cycling by bike-sharing systems (BSS) is regarded as a new transportation mode that is low-carbon and low-cost. However, its dynamic changes and spatiotemporal characteristics in different periods of COVID-19 still lack exploration. Therefore, this study adopts machine learning methods to identify commuter bike-sharing activities and develops a combined analysis method to analyze commuting cycling data via temporal, spatial, and spatiotemporal aggregation. Finally, we select the bike-sharing data in New York City from periods before, during, and after COVID-19 to conduct experiments. It has been found that commuting cycling experienced a “decrease-rebound” trend at the macroscopic level under the pandemic impact. However, at the micro level, urban mobility driven by this travel mode failed to fully recover, as evidenced by significant changes in spatial and temporal mobility patterns. The findings shall not only help traffic operators and managers discover the BSS commuting patterns but also reveal the pandemic impact on the travel behavior of urban residents, promoting the development of intelligent services for urban emergency management and traffic management.

中文翻译：

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COVID-19 前后通过共享单车实现通勤活动的时空动态和恢复：以纽约为例


COVID-19 导致城市出行行为发生重大变化，通勤是受影响最严重的出行方式之一。通过共享单车系统 （BSS） 进行通勤骑行被认为是一种低碳、低成本的新型交通方式。然而，其在 COVID-19 不同时期的动态变化和时空特征仍缺乏探索。因此，本研究采用机器学习方法来识别通勤自行车共享活动，并开发了一种组合分析方法，通过时间、空间和时空聚合来分析通勤自行车数据。最后，我们从 COVID-19 之前、期间和之后的纽约市共享单车数据中进行选择以进行实验。研究发现，在大流行的影响下，通勤骑行在宏观层面上经历了 “减少-反弹 ”的趋势。然而，在微观层面上，由这种出行模式驱动的城市交通未能完全恢复，空间和时间交通模式的显著变化证明了这一点。研究结果不仅有助于交通运营者和管理者发现 BSS 通勤模式，还可以揭示疫情对城市居民出行行为的影响，促进城市应急管理和交通管理智能化服务的发展。