A learning-based multimodel integrated framework for dynamic traffic flow forecasting
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ML has been applied to traffic forecasting using ANN, DT, and time series models.
“ML has been applied to traffic forecasting using ANN, DT, and time series models [171,172].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [172]
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Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
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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
- Licence
- www.springer.com/tdm (tdm)
- 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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