Bustamante Xavier, Federo Ryan, Fernández-I-Marin Xavier
Universitat Politècnica De Catalunya, Carrer Jordi Girona 31, 08034 Barcelona, Spain.
Universitat de les Illes Balears, Campus Cra. Valldemossa. Km 7, 07122, Palma de Mallorca, Spain.
Sustain Cities Soc. 2022 Aug;83:103929. doi: 10.1016/j.scs.2022.103929. Epub 2022 May 4.
To simultaneously promote health, economic, and environmental sustainability, a number of cities worldwide have established bike-sharing systems (BSS) that complement the conventional public transport systems. As the rapid spread of COVID-19 becoming a global pandemic disrupted urban mobility due to government-imposed lockdowns and the heightened fear of infection in crowded spaces, populations were increasingly less likely to use public transportation and instead shifted toward alternative transport systems, including BSS. In this study, we use probabilistic machine learning in a quasi-experimental research design to identify how the relevance of a comprehensive set of factors to predict the use of Bicing (the BSS in Barcelona) may have changed as COVID-19 unfolded. We unpack the key factors in predicting the use of Bicing, uncovering evidence of increasing bike-related built infrastructure (e.g., tactical urbanism), trip distance, and the income levels of neighborhoods as the most relevant predictors. Moreover, we find that the relevance of the factors in predicting Bicing usage has generally decreased during the global pandemic, suggesting altered societal behavior. Our study enhances the understanding of BSS and societal behavior, which can have important implications for developing resilient programs for cities to adopt sustainable practices through transport policy, infrastructure planning, and urban development.
为了同时促进健康、经济和环境的可持续性，全球许多城市都建立了共享单车系统（BSS），作为传统公共交通系统的补充。由于新冠疫情迅速蔓延成为全球大流行，政府实施封锁以及人们对拥挤空间感染风险的高度恐惧扰乱了城市交通，人们越来越不太可能使用公共交通，转而选择包括共享单车系统在内的其他交通方式。在本研究中，我们在准实验研究设计中使用概率机器学习，以确定随着新冠疫情的发展，预测巴塞罗那共享单车系统（Bicing）使用情况的一系列综合因素的相关性可能发生了怎样的变化。我们剖析了预测Bicing使用情况的关键因素，发现与自行车相关的建成基础设施（如战术城市主义）、出行距离以及社区收入水平是最相关的预测因素。此外，我们发现，在全球大流行期间，这些因素在预测Bicing使用情况方面的相关性总体上有所下降，这表明社会行为发生了变化。我们的研究增进了对共享单车系统和社会行为的理解，这对于制定城市弹性计划以通过交通政策、基础设施规划和城市发展采用可持续做法具有重要意义。
