Mitigating biases in big mobility data: a case study of monitoring large-scale transit systems

ABSTRACT

Big mobility data (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.

KEYWORDS:

• Big mobility data
• data bias
• mitigation
• public transit
• nonlinear association
• COVID-19

Acknowledgments

The authors acknowledge the efforts of a former student Ce Wang at University of Washington and data from collaborators at King County Metro and Sound Transit. This work was supported in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2022A1515110924.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Big data and codes used in the study are available at https://github.com/activeconclusion/covid19_mobility. and https://github.com/feilongwang92/mitigate_data_bias., respectively.

Additional information

Funding

The work was supported by the Basic and Applied Basic Research Foundation of Guangdong Province [2022A1515110924].