Faculty of Engineering, Department of Industrial Engineering, Chulalongkorn University, Bangkok, Thailand.
Institute of Transportation Studies, University of California, Davis, California, United States of America.
PLoS One. 2022 Aug 4;17(8):e0272537. doi: 10.1371/journal.pone.0272537. eCollection 2022.
The COVID-19 pandemic is found to be one of the external stimuli that greatly affects mobility of people, leading to a shift of transportation modes towards private individual ones. To properly explain the change in people's transport behavior, especially in pre- and post- pandemic periods, a tensor-based framework is herein proposed and applied to Pun Pun-the only public bicycle-sharing system in Bangkok, Thailand-where multidimensional trip data of Pun Pun are decomposed into four different modes related to their spatial and temporal dimensions by a non-negative Tucker decomposition approach. According to our computational results, the first pandemic wave has a sizable influence not only on Pun Pun but also on other modes of transportation. Nonetheless, Pun Pun is relatively more resilient, as it recovers more quickly than other public transportation modes. In terms of trip patterns, we find that, prior to the pandemic, trips made during weekdays are dominated by business trips with two peak periods (morning and evening peaks), while those made during weekends are more related to leisure activities as they involve stations nearby a public park. However, after the first pandemic wave ends, the patterns of weekday trips have been drastically changed, as the number of business trips sharply drops, while that of educational trips connecting metro/subway stations with a major educational institute in the region significantly rises. These findings may be regarded as a reflection of the ever-changing transport behavior of people seeking a sustainable mode of private transport, with a more positive outlook on the use of bicycle-sharing system in Bangkok, Thailand.
新冠疫情被发现是极大影响人们流动性的外部刺激因素之一，导致交通模式向私人个体交通转移。为了正确解释人们交通行为的变化，特别是在疫情前后时期，本文提出了一种基于张量的框架，并将其应用于泰国曼谷唯一的公共自行车共享系统——邦邦，通过非负 Tucker 分解方法，将邦邦的多维出行数据分解为与空间和时间维度相关的四种不同模式。根据我们的计算结果，第一波疫情不仅对邦邦，而且对其他交通模式都有相当大的影响。然而，邦邦相对更具弹性，因为它比其他公共交通模式更快地恢复。就出行模式而言，我们发现，在疫情之前，工作日的出行主要是商务出行，有两个高峰期（早晚高峰），而周末的出行则更多地与休闲活动有关，因为它们涉及到一个公共公园附近的车站。然而，第一波疫情结束后，工作日的出行模式发生了巨大变化，商务出行的数量急剧下降，而连接地铁站和该地区主要教育机构的教育出行的数量显著上升。这些发现可以被视为人们不断变化的交通行为的反映，他们寻求一种可持续的私人交通模式，对泰国曼谷自行车共享系统的使用有更积极的展望。
