Uncovering Disparities in Rideshare Drivers Earning and Work Patterns: A Case Study of Chicago
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917920804700160 |
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| author | Dang, Hy Lu, Yuwen Spicer, Jason Kay, Tamara Yang, Di Yang, Yang Brockman, Jay Jiang, Meng Li, Toby Jia-Jun |
| author_facet | Dang, Hy Lu, Yuwen Spicer, Jason Kay, Tamara Yang, Di Yang, Yang Brockman, Jay Jiang, Meng Li, Toby Jia-Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08893 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncovering Disparities in Rideshare Drivers Earning and Work Patterns: A Case Study of Chicago Dang, Hy Lu, Yuwen Spicer, Jason Kay, Tamara Yang, Di Yang, Yang Brockman, Jay Jiang, Meng Li, Toby Jia-Jun Human-Computer Interaction 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. |
| title | Uncovering Disparities in Rideshare Drivers Earning and Work Patterns: A Case Study of Chicago |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.08893 |