Li, Z. and Xu, T. (2022) Understanding Factors Influencing Willingness to Ridesharing Using Big Trip Data and Interpretable Machine Learning. In: GISRUK 2022, Liverpool, UK, 05-08 Apr 2022, (doi: 10.5281/zenodo.6411504)
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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.
| Item Type: | Conference Proceedings |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Li, Dr Ziqi |
| Authors: | Li, Z., and Xu, T. |
| College/School: | College of Science and Engineering > School of Geographical and Earth Sciences |
| Copyright Holders: | Copyright © 2022 Zenodo |
| First Published: | First published in 30th GISRUK Conference 2022 |
| Publisher Policy: | Reproduced under a Creative Commons licence |
| Related URLs: |
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