Traffic Flow Prediction Based on VMD-ARO-BiLSTM
DOI:
https://doi.org/10.62051/ijcsit.v3n3.04Keywords:
Traffic flow prediction, ARO, BiLSTM, VMD-ARO-BiLSTM combined prediction modelAbstract
With the acceleration of urbanization and the continuous growth of transportation demand, traffic flow prediction has become a key research issue in ITS (Intelligent Transportation System). In this paper, a traffic flow prediction model based on VMD-ARO-BiLSTM is proposed, which can make full use of the spatio-temporal characteristics of traffic data to achieve high-precision traffic flow prediction. First, the traffic flow data are input into VMD (Variational Mode Decomposition) for data reconstruction. Then, the optimal parameter values of BiLSTM (Bi-directional Long Short-Term Memory) are solved using ARO (Artificial Rabbits Optimization) to complete the optimization of BiLSTM. Finally, the reconstructed data are input into BiLSTM to realize the accurate prediction of traffic flow. To verify the model performance, we conducted experiments using traffic flow data from different time periods. The experimental results show that on weekdays, the combined VMD-ARO-BiLSTM prediction model decreases the RMSE (Root Mean Square Error) by 47.2% and the MAE (Mean Absolute Error) by 44.6% compared to BiLSTM; on weekends, compared to BiLSTM, the RMSE decreases by 26.1% and MAE decreases by 23.4%, reflecting the strong robustness and applicability of this model, which has certain application value.
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[1] W. B. Zhang, H. Z. Zha, S. Zhang, and L. Ma, “Road section traffic flow prediction method based on the traffic factor state network,” Physica a-Statistical Mechanics and Its Applications, vol. 618, pp. 18, May, 2023.
[2] S. V. Kumar, and L. Vanajakshi, “Short-term traffic flow prediction using seasonal ARIMA model with limited input data,” European Transport Research Review, vol. 7, no. 3, pp. 9, Sep, 2015.
[3] S. V. Kumar, "Traffic Flow Prediction using Kalman Filtering Technique," Procedia Engineering. pp. 582-587, 2017.
[4] Q. X. Lieu, “A deep neural network-assisted metamodel for damage detection of trusses using incomplete time-series acceleration,” Expert Systems with Applications, vol. 233, pp. 120967, 2023.
[5] W. Fitters, A. Cuzzocrea, and M. Hassani, "Enhancing LSTM Prediction of Vehicle Traffic Flow Data via Outlier Correlations," Proceedings International Computer Software and Applications Conference. pp. 210-217, 2021.
[6] W. Y. Wei, H. H. Wu, and H. Ma, “An AutoEncoder and LSTM-Based Traffic Flow Prediction Method,” Sensors, vol. 19, no. 13, pp. 16, Jul, 2019.
[7] Z. Y. Li, H. X. Ge, and R. J. Cheng, “Traffic flow prediction based on BILSTM model and data denoising scheme,” Chinese Physics B, vol. 31, no. 4, pp. 10, Mar, 2022.
[8] C. L. Kang, Z. Y. Zhang, and Ieee, "Application of LSTM in Short-term Traffic Flow Prediction." pp. 98-101, 2020.
[9] L. T. Mou, P. F. Zhao, H. T. Xie, and Y. Y. Chen, “T-LSTM: A Long Short-Term Memory Neural Network Enhanced by Temporal Information for Traffic Flow Prediction,” Ieee Access, vol. 7, pp. 98053-98060, 2019.
[10] C. X. Ma, G. W. Dai, and J. B. A. Zhou, “Short-Term Traffic Flow Prediction for Urban Road Sections Based on Time Series Analysis and LSTM_BILSTM Method,” Ieee Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5615-5624, Jun, 2022.
[11] L. M. Xing, and W. J. Liu, “A Data Fusion Powered Bi-Directional Long Short Term Memory Model for Predicting Multi-Lane Short Term Traffic Flow,” Ieee Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 16810-16819, Sep, 2022.
[12] L. Y. Wang, Q. J. Cao, Z. X. Zhang, S. Mirjalili, and W. G. Zhao, “Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems,” Engineering Applications of Artificial Intelligence, vol. 114, pp. 31, Sep, 2022.
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