Understanding the Impact of the COVID-19 Pandemic on Transportation-related Behaviors with Human Mobility Data
Jizhou Huang, Haifeng Wang, Miao Fan, An Zhuo, Yibo Sun, Ying Li

TL;DR
This study analyzes how COVID-19 containment measures in China significantly altered transportation behaviors using Baidu Maps mobility data, providing insights for targeted epidemic control strategies.
Contribution
It offers a data-driven analysis of transportation-related behavioral changes during COVID-19, with specific insights and policy suggestions based on large-scale human mobility data.
Findings
Transport mode usage changed during the pandemic
Venue visitation patterns shifted significantly
People's origin-destination preferences were affected
Abstract
The constrained outbreak of COVID-19 in Mainland China has recently been regarded as a successful example of fighting this highly contagious virus. Both the short period (in about three months) of transmission and the sub-exponential increase of confirmed cases in Mainland China have proved that the Chinese authorities took effective epidemic prevention measures, such as case isolation, travel restrictions, closing recreational venues, and banning public gatherings. These measures can, of course, effectively control the spread of the COVID-19 pandemic. Meanwhile, they may dramatically change the human mobility patterns, such as the daily transportation-related behaviors of the public. To better understand the impact of COVID-19 on transportation-related behaviors and to provide more targeted anti-epidemic measures, we use the huge amount of human mobility data collected from Baidu Maps,…
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