A Bidirectional Context-Aware and Multi-Scale Fusion Hybrid Network for Short-Term Traffic Flow Prediction
Abstract
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.
References
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.1997.9.8.1735.
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.
Copyright (c) 2022 Zhixing CHEN, Guizhou ZHENG
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
