The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Using LSTM and GRU neural network methods for traffic flow prediction
Abstract
Accurate and real-time traffic flow prediction is important in Intelligent Transportation System (ITS), especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear models and cannot describe the stochastic and nonlinear nature of traffic flow. In recent years, deep-learning-based methods have been applied as novel alternatives for traffic flow prediction. However, which kind of deep neural networks is the most appropriate model for traffic flow prediction remains unsolved. In this paper, we use Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) neural network (NN) methods to predict short-term traffic flow, and experiments demonstrate that Recurrent Neural Network (RNN) based deep learning methods such as LSTM and GRU perform better than auto regressive integrated moving average (ARIMA) model. To the best of our knowledge, this is the first time that GRU is applied to traffic flow prediction.
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Cites
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Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Cited by
Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach
Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991401 · Cited by 712
Traffic Flow Prediction via Spatial Temporal Graph Neural Network
Wang, X., Ma, Y., Wang, Y., Jin, W., Wang, X., Tang, J., Jia, C., & Yu, J. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380186 · Cited by 644
Short-term traffic flow prediction with Conv-LSTM
Liu, Y., Zheng, H., Feng, X., & Chen, Z. (2017) · 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2017.8171119 · Cited by 328
Survey on traffic prediction in smart cities
Nagy, A. M., & Simon, V. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.07.004 · Cited by 312
Enhancing transportation systems via deep learning: A survey
Wang, Y., Zhang, D., Liu, Y., Dai, B., & Lee, L. H. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.12.004 · Cited by 312
Metadata
- Authors
- Fu, R., Zhang, Z., & Li, L.
- Venue
- 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC)
- Publisher
- IEEE
- Published
- 2016-11
- Pages
- 324-328
- OpenAlex
- W2572939427
- Cited by
- 1,434
- References
- 3
- FWCI
- 88.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Fu, R., Zhang, Z., & Li, L. (2016). Using LSTM and GRU neural network methods for traffic flow prediction. 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). https://doi.org/10.1109/yac.2016.7804912
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