The DOI matches its Crossref record; 187 of the 187 references it cites were found in a registry or an index.
Enhancing transportation systems via deep learning: A survey
Abstract
Abstract Machine learning (ML) plays the core function to intellectualize the transportation systems. Recent years have witnessed the advent and prevalence of deep learning which has provoked a storm in ITS (Intelligent Transportation Systems). Consequently, traditional ML models in many applications have been replaced by the new learning techniques and the landscape of ITS is being reshaped. Under such perspective, we provide a comprehensive survey that focuses on the utilization of deep learning models to enhance the intelligence level of transportation systems. By organizing multiple dozens of relevant works that were originally scattered here and there, this survey attempts to provide a clear picture of how various deep learning models have been applied in multiple transportation applications.
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Cites
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Gambella, C., Ghaddar, B., & Naoum-Sawaya, J. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.08.045 · Cited by 365
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Metadata
- Authors
- Wang, Y., Zhang, D., Liu, Y., Dai, B., & Lee, L. H.
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 99
- Pages
- 144-163
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W2906257585
- Cited by
- 312
- References
- 187
- FWCI
- 16.69
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, Y., Zhang, D., Liu, Y., Dai, B., & Lee, L. H. (2019). Enhancing transportation systems via deep learning: A survey. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2018.12.004
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