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arXiv · 2502.08893

Uncovering Disparities in Rideshare Drivers Earning and Work Patterns: A Case Study of Chicago

Hy Dang·Yuwen Lu·Jason Spicer·Tamara Kay·Di Yang·Yang Yang·Jay Brockman·Meng Jiang·Toby Jia-Jun Li
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Abstract

Ride-sharing services are revolutionizing urban mobility while simultaneously raising significant concerns regarding fairness and driver equity. This study employs Chicago Trip Network Provider dataset to investigate disparities in ride-sharing earnings between 2018 and 2023. Our analysis reveals marked temporal shifts, including an earnings surge in early 2021 followed by fluctuations and a decline in inflation-adjusted income, as well as pronounced spatial disparities, with drivers in Central and airport regions earning substantially more than those in peripheral areas. Recognizing the limitations of trip-level data, we introduce a novel trip-driver assignment algorithm to reconstruct plausible daily work patterns, uncovering distinct driver clusters with varied earning profiles. Notably, drivers operating during late-evening and overnight hours secure higher per-trip and hourly rates, while emerging groups in low-demand regions face significant earnings deficits. Our findings call for more transparent pricing models and a re-examination of platform design to promote equitable driver outcomes.

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Hy Dang, Yuwen Lu, Jason Spicer, Tamara Kay, Di Yang, Yang Yang, Jay Brockman, Meng Jiang, Toby Jia-Jun Li. 2025-02-13. Uncovering Disparities in Rideshare Drivers Earning and Work Patterns: A Case Study of Chicago. https://arxiv.org/abs/2502.08893

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