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

TL;DR

This paper evaluates biases in big mobility data from Google and Apple, compares them with official benchmarks, and proposes mitigation methods to improve their reliability for transportation analysis.

Contribution

It identifies biases in BMD, compares them with official data, and develops mitigation strategies to enhance data accuracy for large-scale transit system monitoring.

Findings

Biases exist between BMD and official data.

Mitigation improves data reliability.

Reveals regional transit recovery disparities.

Abstract

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.