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

arXiv: https://arxiv.org/abs/2407.14541 · HTML: https://mlsanity.com/paper/2407.14541/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 46 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Builds on (most cited first, 2 of 2)

- [Demographic Inference and Representative Population Estimates from Multilingual Social Media Data](https://mlsanity.com/paper/1905.05961/) (2019)
- [Extracting Trips from Multi-Sourced Data for Mobility Pattern Analysis: An App-Based Data Example](https://mlsanity.com/paper/1912.01835/) (2019)

## Abstract (arXiv)

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.