  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.
