The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach
Abstract
Transportation systems might be heavily affected by factors such as accidents and weather. Specifically, inclement weather conditions may have a drastic impact on travel time and traffic flow. This study has two objectives: first, to investigate a correlation between weather parameters and traffic flow and, second, to improve traffic flow prediction by proposing a novel holistic architecture. It incorporates deep belief networks for traffic and weather prediction and decision-level data fusion scheme to enhance prediction accuracy using weather conditions. The experimental results, using traffic and weather data originated from the San Francisco Bay Area of California, corroborate the effectiveness of the proposed approach compared with the state of the art.
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Cited by
Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295
Deep learning for edge computing applications: A State-of-the-Art survey
Wang, F., Zhang, M., Wang, X., Ma, X., & Liu, J. (2020) · IEEE Access · doi:10.1109/access.2020.2982411 · Cited by 259
Survey of deep learning for autonomous surface vehicles in marine environments
Qiao, Y., Yin, J., Wang, W., Duarte, F., Yang, J., & Ratti, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3235911 · Cited by 192
Geospatial data to images: a deep-learning framework for traffic forecasting
Jiang, W., & Zhang, L. (2019) · Tsinghua Science and Technology · doi:10.26599/tst.2018.9010033 · Cited by 176
Machine learning for next-generation intelligent transportation systems: A survey
Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4427 · Cited by 179
Metadata
- Authors
- Koesdwiady, A., Soua, R., & Karray, F.
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2016-12
- Volume
- 65
- Issue
- 12
- Pages
- 9508-9517
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W2460404912
- Cited by
- 367
- References
- 26
- FWCI
- 37.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- Koesdwiady, A., Soua, R., & Karray, F. (2016). Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/tvt.2016.2585575
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
