Cited by 252Transportation and Mobility InnovationsTraffic Prediction and Management TechniquesTransportation Planning and Optimization

Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach

Chen, Xiqun (Michael), Zahiri, Majid, Zhang, Shuaichao·2017·Transportation Research Part C: Emerging Technologies

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Crossref · 10.1016/j.trc.2016.12.018

Cites

44 of 44 references verified0 citation errors · 0 not checkable

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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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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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