  Big mobility datasets (BMD) have shown many advantages in studying human
mobility and evaluating the performance of transportation systems. However, the
quality of BMD remains poorly understood. This study evaluates biases in BMD
and develops mitigation methods. Using Google and Apple mobility data as
examples, this study compares them with benchmark data from governmental
agencies. Spatio-temporal discrepancies between BMD and benchmark are observed
and their impacts on transportation applications are investigated, emphasizing
the urgent need to address these biases to prevent misguided policymaking. This
study further proposes and tests a bias mitigation method. It is shown that the
mitigated BMD could generate valuable insights into large-scale public transit
systems across 100+ US counties, revealing regional disparities of the recovery
of transit systems from the COVID-19. This study underscores the importance of
caution when using BMD in transportation research and presents effective
mitigation strategies that would benefit practitioners.
