A Bidirectional Context-Aware and Multi-Scale Fusion Hybrid Network for Short-Term Traffic Flow Prediction
Short-term traffic flow prediction is to automatically predict the traffic flow changes in a period of future time based on the extraction of the spatiotemporal features in the road network. For governments, timely and accurate traffic flow prediction is crucial to plan road manage-ment and improve traffic efficiency. Recent advances in deep learning have shown their dominance on short-term traffic flow prediction. However, previous methods based on deep learning are mainly limited to temporal features and have so far failed to predict the bidirectional con-textual spatiotemporal relationship correctly. Besides, the precision and the practicality are limited by the road network scale and the single time scale. To remedy these issues, a Bidirectional Context-aware and Multi-scale fusion hybrid Network (BCM-Net) is proposed, which is a novel short-term traffic flow prediction framework to predict timely and accurate traffic flow changes. In BCM-Net, the Bidirectional Context-aware (BCM) block is added to the feature extraction structure to effective-ly integrate spatiotemporal features. The Interpolation Back Propagation sub-network is used to merge multi-scale information, which further improves the robustness of the model. Experiment results on diverse datasets demonstrated that the proposed method outperformed the state-of-the-art methods.
Agachai S, Wai HH. Smarter and more connected: Future intelligent transportation system. IATSS Research. 2018;42: 67-71. doi: 10.1016/j.iatssr.2018.05.005.
Liu Z, et al. Effect of time intervals on K-nearest neighbors model for short-term traffic flow prediction. Promet – Traffic&Transportation. 2019;31(2): 129-139. doi: 10.7307/ptt.v31i2.2811.
Liu Z, et al. A hybrid short-term traffic flow forecasting method based on neural networks combined with K-nearest neighbor. Promet – Traffic&Transportation. 2018;30(4): 445-456. doi: 10.7307/ptt.v30i4.2651.
Hubel DH, Wiesel TN. Receptive fields of single neurones in the cat's striate cortex. The Journal of Physiology. 1959;148(3): 574. doi: 10.1113/jphysiol.1959.sp006308.
Mou L, Zhao P, Xie H, Chen Y. T-LSTM: A long short-term memory neural network enhanced by temporal information for traffic flow prediction. IEEE Access. 2019;7: 98053-98060. doi: 10.1109/ACCESS.2019.2929692.
Doan E. Short-term traffic flow prediction using artificial intelligence with periodic clustering and elected set. Promet – Traffic & Transportation. 2020;32(1): 65-78. doi: 10.7307/ptt.v32i1.3154.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation. 1997;9(8): 1735-1780. doi: 10.1162/neco.19188.8.131.525.
Wei W, Wu H, Ma H. An AutoEncoder and LSTM-based traffic flow prediction method. Sensors. 2019;19(13): 2946. doi: 10.3390/s19132946.
Zheng H, Lin F, Feng X, Chen Y. A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems. 2021;22(11): 6910-6920. doi: 10.1109/TITS.2020.2997352.
Qiao Y, Wang Y, Ma C, Yang J. Short-term traffic flow prediction based on 1DCNN-LSTM neural network structure. Modern Physics Letters B. 2020;35(2): 2150042. doi: 10.1142/S0217984921500421.
Li Z, et al. A hybrid deep learning approach with GCN and LSTM for traffic flow prediction. IEEE International Conference on Intelligent Transportation Systems (ITSC). 2019. doi: 10.1109/ITSC.2019.8916778.
Zhang Y, Yang SM, Xin DR. Short-term traffic flow forecast based on improved wavelet packet and long short-term memory combination model. Transportation Systems Engineering and Information. 2020;20(2): 208-214. doi: 10.1109/CSAE.2011.5953161.
Shan G, Ye Z, Guo QC. Study on exhaust emission test of diesel vehicles based on PEMS. Procedia Computer Science. 2020;166: 428-433. doi: 10.1016/j.procs.2020.02.070.
Zheng L, et al. Dynamic spatial-temporal feature optimization with ERI big data for short-term traffic flow prediction. Neural Computation. 2020;412: 339-350. doi: 10.1016/j.neucom.2020.05.038.
Doan E. LSTM training set analysis and clustering model development for short-term traffic flow prediction. Neural Computing and Applications. 2021;4: 1-14. doi: 10.1007/s00521-020-05564-5.
Kang DQ, Lv YS, Chen YY. Short-term traffic flow prediction with LSTM recurrent neural network. IEEE International Conference on Intelligent Transportation Systems (ITSC). 2018. doi:10.1109/ITSC.2017.8317872.
Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. JMLR.org. 2015.
Zhang H, et al. Power control based on deep reinforcement learning for spectrum sharing. IEEE Transactions on Wireless Communications. 2020;19(6): 4209-4219. doi: 10.1109/TWC.2020.2981320.
Pan X, et al. Identifying patients with atrioventricular septal defect in down syndrome populations by using self-normalizing neural networks and feature selection. Genes. 2018;9(4): 208. doi: 10.3390/genes9040208.
Fu R. Using LSTM and GRU neural network methods for traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems. 2016. doi:10.1109/YAC.2016.7804912.
Oh YR, Park K, Jeon HB, Park JG. Automatic proficiency assessment of Korean speech read aloud by non‐natives using bidirectional LSTM‐based speech recognition. ETRI Journal. 2020;42(10). doi: 10.4218/etrij.2019-0400.
Zeyer A, et al. A comprehensive study of deep bidirectional LSTM RNNs for acoustic modeling in speech recognition. IEEE ICASSP. 2017. doi: 10.1109/ICASSP.2017.7952599.
Hu G, Feng ZZ, Cao J, Huang H. Nonlinear calibration optimization based on the Levenberg-Marquardt algorithm. IET Image Processing. 2020;14(7). doi: 10.1049/iet-ipr.2019.1489.
Dai X, et al. Deeptrend 2.0: A light-weighted multi-scale traffic prediction model using detrending. Transportation Research Part C Emerging Technologies. 2019;103(1): 142-157. doi: 10.1016/j.trc.2019.03.022.
Huang H, et al. Effect of multi-scale decomposition on performance of neural networks in short-term traffic flow prediction. IEEE Access. 2021;9: 50994-51004. doi: 10.1109/ACCESS.2021.3068652.
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