The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Short-term traffic flow prediction with Conv-LSTM
Abstract
The accurate short-term traffic flow prediction can provide timely and accurate traffic condition information which can help one to make travel decision and mitigate the traffic jam. Deep learning (DL) provides a new paradigm for the analysis of big data generated by the urban daily traffic. In this paper, we propose a novel end-to-end deep learning architecture which consists of two modules. We combine convolution and LSTM to form a Conv-LSTM module which can extract the spatial-temporal information of the traffic flow information. Furthermore, a Bi-directional LSTM module is also adopted to analyze historical traffic flow data of the prediction point to get the traffic flow periodicity feature. The experimental results on the real dataset show that the proposed approach can achieve a better prediction accuracy compared with the existing approaches.
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Cites
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Li, P., Abdel-Aty, M., & Yuan, J. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.105371 · Cited by 394
Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295
Boukerche, A., Tao, Y., & Sun, P. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107484 · Cited by 215
Afandizadeh, S., Abdolahi, S., & Mirzahossein, H. (2024) · Journal of Advanced Transportation · doi:10.1155/2024/9981657 · Cited by 64
Metadata
- Authors
- Liu, Y., Zheng, H., Feng, X., & Chen, Z.
- Venue
- 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1-6
- OpenAlex
- W2775717462
- Cited by
- 328
- References
- 20
- FWCI
- 27.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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