Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach
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ML can predict if a customer decides to share a ride with other passengers from an on-demand ride service.
“For example, they can be used to predict if a customer decides to share a ride with other passengers from an ondemand ride service [133].”
From Tackling climate change with machine learning (2023) · cites this paper as [133]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Chen, X. (., Zahiri, M., & Zhang, S.
- Affiliations
- Zhejiang University (CN)
- Corresponding author
- Chen, Xiqun (Michael)
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 76
- Pages
- 51-70
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2570113925
- Cited by
- 252
- References
- 44
- Citations by year
- 2026: 12 · 2025: 26 · 2024: 24 · 2023: 23 · 2022: 27 · 2021: 37 · 2020: 42 · 2019: 39 · 2018: 15 · 2017: 7
- FWCI
- 17.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion)
- Funders
- Zhejiang Provincial Natural Science Foundation of China (LR17E080002)
- National Natural Science Foundation of China (51508505, 51338008)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- on-demand ride services, ride-hailing, boosting ensemble, decision tree, ReliefF feature selection, hitchhiking, travel time reliability, Didi
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