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

Ziqi Li

No chy4p, OSF Preprints from Center for Open Science

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.

Date: 2022-04-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-pay, nep-tre and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:chy4p

DOI: 10.31219/osf.io/chy4p

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