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Kalman Filter-Based CNN-BiLSTM-ATT Model for Traffic Flow Prediction

Hong Zhang1,2,*, Gang Yang1, Hailiang Yu1, Zan Zheng1

1 Institute of Transportation, Inner Mongolia University, Hohhot, 010020, China
2 Inner Mongolia Engineering Research Center for Urban Transportation Data Science and Applications, Hohhot, 010020, China

* Corresponding Author: Hong Zhang. Email:

Computers, Materials & Continua 2023, 76(1), 1047-1063.


To accurately predict traffic flow on the highways, this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism (CNN-BiLSTM-Attention) traffic flow prediction model based on Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, which can reduce the instability of short-term traffic flow prediction due to unexpected accidents. Then the local spatial features of the traffic data during different periods are extracted, dimensionality is reduced through a one-dimensional CNN, and the BiLSTM network is used to analyze the time series information. Finally, the Attention Mechanism assigns feature weights and performs Softmax regression. The experimental results show that the data processed by Kalman filter is more accurate in predicting the results on the CNN-BiLSTM-Attention model. Compared with the CNN-BiLSTM model, the Root Mean Square Error (RMSE) of the Kal-CNN-BiLSTM-Attention model is reduced by 17.58 and Mean Absolute Error (MAE) by 12.38, and the accuracy of the improved model is almost free from non-working days. To further verify the model’s applicability, the experiments were re-run using two other sets of fluctuating data, and the experimental results again demonstrated the stability of the model. Therefore, the Kal-CNN-BiLSTM-Attention traffic flow prediction model proposed in this paper is more applicable to a broader range of data and has higher accuracy.


Cite This Article

H. Zhang, G. Yang, H. Yu and Z. Zheng, "Kalman filter-based cnn-bilstm-att model for traffic flow prediction," Computers, Materials & Continua, vol. 76, no.1, pp. 1047–1063, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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