Mitigating biases in big mobility data: a case study of monitoring large-scale transit systems
It addresses biases in mobility data for transportation researchers and practitioners to prevent flawed decisions, though it is incremental in applying existing methods to new data.
This study evaluated biases in big mobility datasets (BMD) by comparing Google and Apple data with governmental benchmarks, revealing spatio-temporal discrepancies that could misguide policymaking, and proposed a mitigation method that improved insights into transit system recovery across 100+ US counties.
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.