The Economic Recovery from Traffic Restriction Policies during the COVID-19 through the Perspective of Regional Differences and Sustainable Development: Based on Human Mobility Data in China

In the post-epidemic era, balancing epidemic prevention and control with sustainable economic development has become a serious challenge for all countries around the world. In China, a range of interventions include detection policies, clinical treatment policies, and most notably, traffic policies have been carried out for epidemic prevention and control. It has been widely confirmed that massive traffic restriction policies effectively brought the spread of the pandemic under control. However, restrictions on the use of transportation infrastructure undermine the smooth functioning of the economy. Particularly, China has a vast territory, with provinces differing in economic development, leading industries and transportation infrastructure;economic shock varies from region to region. In this case, targeted policies are the key to sustainable development. This paper sets forth advice for the Chinese government on its measures to boost the economy by analyzing regional differences in the impact of massive traffic restriction policies, based on large-scale human mobility data. After applying the Data Envelopment Analysis model, we classify Chinese provinces into different regions from the perspective of economic gradient, degree of internationalization and level of traffic convenience, respectively. Classification results are matched with the indicators of New Venues Created and the weekly Volumes of Visits to Venues from Baidu Maps. We find that the regional differences in the recovery of investment and consumption levels are striking. Based on the findings, we suggest that the government should adjust the intensity of traffic restrictions and economic stimulus policies dynamically according to regional differences to achieve sustainable economic development.

Understanding the Impact of the COVID-19 Pandemic on Transportation-related Behaviors with Human Mobility Data (preprint)

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, a widely-used Web mapping service in China, to look into the detail reaction of the people there during the pandemic. To be specific, we conduct data-driven analysis on transportation-related behaviors during the pandemic from the perspectives of 1) means of transportation, 2) type of visited venues, 3) check-in time of venues, 4) preference on "origin-destination" distance, and 5) "origin-transportation-destination" patterns. For each topic, we also give our specific insights and policy-making suggestions. Given that the COVID-19 pandemic is still spreading in more than 200 countries and territories worldwide, infecting millions of people, the insights and suggestions provided here may help fight COVID-19.