Padmakumar Athul, Patil Gopal R
Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India.
Cities. 2022 Jul;126:103697. doi: 10.1016/j.cities.2022.103697. Epub 2022 Apr 11.
The outbreak of the COVID-19 pandemic disrupted all walks of life, including the transportation sector. Fear of the contagion coupled with government regulations to restrict mobility altered the travel behavior of the public. This study proposes integrating freely accessible aggregate mobility datasets published by tech giants Apple and Google, which opens a broader avenue for mobility research in the light of difficult data collection circumstances. A comparative analysis of the changes in usage of different mobility modes during the national lockdown and unlock policy periods across 6 Indian cities (Bangalore, Chennai, Delhi, Hyderabad, Mumbai, and Pune) explain the spatio-temporal differences in mode usages. The study shows a preference for individual travel modes (walking and driving) over public transit. Comparisons with pre-pandemic mode shares present evidence of inertia in the choice of travel modes. Association investigations through generalized linear mixed-effects models identify income, vehicle registrations, and employment rates at the city level to significantly impact the community mobility trends. The methods and interpretations from this study benefit government, planners, and researchers to boost informed policymaking and implementation during a future emergency demanding mobility regulations in the high-density urban conglomerations.
新冠疫情的爆发扰乱了各行各业，包括交通运输业。对传染的恐惧以及政府限制出行的规定改变了公众的出行行为。本研究建议整合科技巨头苹果和谷歌发布的可免费获取的总体出行数据集，鉴于数据收集困难的情况，这为出行研究开辟了更广阔的途径。对印度6个城市（班加罗尔、金奈、德里、海得拉巴、孟买和浦那）在全国封锁和解封政策期间不同出行方式使用情况的变化进行的比较分析，解释了出行方式使用的时空差异。研究表明，人们更倾向于选择个体出行方式（步行和驾车）而非公共交通。与疫情前出行方式份额的比较表明出行方式选择存在惯性。通过广义线性混合效应模型进行的关联调查发现，城市层面的收入、车辆登记和就业率对社区出行趋势有显著影响。本研究的方法和解读有助于政府、规划者和研究人员在未来需要对高密度城市集聚区实施出行规定的紧急情况下，推动明智的政策制定和实施。
