Cited by 66Traffic Prediction and Management TechniquesTraffic control and managementForecasting Techniques and Applications

A learning-based multimodel integrated framework for dynamic traffic flow forecasting

Zhou, Teng, Han, Guoqiang, Xu, Xuemiao, Han, Chu, Huang, Yuchang, Qin, Jing·2019·Neural Processing Letters

Integrity signals

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Crossref · 10.1007/s11063-018-9804-x

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60 of 60 references verified0 citation errors · 0 not checkable

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Authors
Zhou, T., Han, G., Xu, X., Han, C., Huang, Y., & Qin, J.
Affiliations
  • Shantou University (CN)
  • South China University of Technology (CN)
  • Chinese University of Hong Kong (HK)
  • South China Agricultural University (CN)
  • Hong Kong Polytechnic University (HK)
Corresponding author
Xu, Xuemiao
Venue
Neural Processing Letters
Published
2018-03-26
Volume
49
Issue
1
Pages
407-430
ISSN
  • 1370-4621 (ISSN-L)
  • 1573-773X
OpenAlex
W2795012238
Cited by
66
References
60
Citations by year
2026: 3 · 2025: 1 · 2024: 9 · 2023: 12 · 2022: 9 · 2021: 12 · 2020: 16 · 2019: 4
FWCI
6.41
Type
journal article
Language
en
Open access
closed
Locations
Neural Processing Letters (publishedVersion)
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords
traffic flow prediction, stacked autoencoder, dynamic model selection, representation learning, traffic flow uncertainty, intelligent transportation systems

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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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