Traffic Flow Forecasting Using Attention Enabled Bi-LSTM and GRU Hybrid Model
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-1648-1_42
Reference21 articles.
1. Wang, P., Hao, W., Jin, Y.: Fine-grained traffic flow prediction of various vehicle types via fusion of multisource data and deep learning approaches. IEEE Trans. Intell. Transp. Syst. 22(11), 6921–6930 (2020)
2. Tedjopurnomo, D.A., et al.: A survey on modern deep neural network for traffic prediction: trends, methods and challenges. IEEE Trans. Knowl. Data Eng. 34, 1544–1561 (2020)
3. Zheng, H., et al.: A hybrid deep learning model with attention-based Conv-LSTM networks for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 22(11), 6910–6920 (2020)
4. Zhaowei, Q., et al.: Short-term traffic flow forecasting method with MB-LSTM hybrid network. IEEE Trans. Intell. Transp. Syst. 23(1), 225–235 (2020)
5. Zheng, G., et al.: A joint temporal-spatial ensemble model for short-term traffic prediction. Neurocomputing 457, 26–39 (2021)
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