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Understanding Factors Influencing Willingness to Ridesharing Using Big Trip Data and Interpretable Machine Learning

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  • Li, Ziqi

Abstract

Ridesharing, compared to traditional solo ride-hailing, can reduce traffic congestion, cut per-passenger carbon emissions, reduce parking infrastructure, and provide a more cost-effective way to travel. Despite these benefits, ridesharing only occupies a small percentage of the total ride-hailing trips. This study provides a reproducible and replicable framework that integrates big trip data, machine learning models, and explainable artificial intelligence (XAI) to better understand the factors that influence people's decisions to take or not to take a shared ride.

Suggested Citation

  • Li, Ziqi, 2022. "Understanding Factors Influencing Willingness to Ridesharing Using Big Trip Data and Interpretable Machine Learning," OSF Preprints chy4p, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:chy4p
    DOI: 10.31219/osf.io/chy4p
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    References listed on IDEAS

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    1. Dean, Matthew D. & Kockelman, Kara M., 2021. "Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago," Journal of Transport Geography, Elsevier, vol. 91(C).
    2. Susan Shaheen & Adam Cohen, 2019. "Shared ride services in North America: definitions, impacts, and the future of pooling," Transport Reviews, Taylor & Francis Journals, vol. 39(4), pages 427-442, July.
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