A Hybrid Deep Convolutional Neural Network Approach for Predicting the Traffic Congestion Index
Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network.
Ko JH. Seoul’s Transportation Demand Management Policy. Seoul, Korea: The Seoul Institute; 2015.
Cookson G, Pishue B. INRIX Global Traffic Scorecard. Kirkland, Washington: INRIX research; 2018.
Yasin Çodur M, Tortum A. An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey. Promet – Traffic&Transportation. 2015;27(3): 217-25.
Linchao L, Fratrović T, Jian Z, Bin R. Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm. Promet – Traffic&Transportation. 2017;29(4): 433-441.
Tseng F, Hsueh J, Tseng C, Yang Y, Chao H, Chou L. Congestion Prediction with Big Data for Real-Time Highway Traffic. IEEE Access. 2018;6: 57311-57323. DOI: 10.1109/ACCESS.2018.2873569
Zhao H, Xia J, Li F, Li Z, Li Q. A Peak Traffic Congestion Prediction Method Based on Bus Driving Time. Entropy. 2019;21(709). DOI: 10.3390/e21070709
Transportation Research Board. The Highway Capacity Manual 2010 (HCM2010). The National Academies of Science, United States; 2010.
Xing Y, Ban XJ, Liu X, Shen Q. Large-Scale Traffic Congestion Prediction Based on the Symmetric Extreme Learning Machine Cluster Fast Learning Method. Symmetry. 2019;11(6): 730. DOI: 10.3390/sym11060730
Huang D, Deng Z, Wan S, Mi B, Liu Y. Identification and Prediction of Urban Traffic Congestion via Cyber-Physical Link Optimization. IEEE Access. 2018;6: 63268-63278.
Feifei H, Yan XD, Liu Y, Ma LA. Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index. Procedia Engineering. 2016;137: 425-433. DOI: 10.1016/j.proeng.2016.01.277
Nguyen DB, Dow CR, Hwang SF. An Efficient Traffic Congestion Monitoring System on Internet of Vehicles. Wireless Communications and Mobile Computing. 2018. DOI: 10.1155/2018/9136813
Zhang H, Shi B, Zhuge C, Wang W. Detecting Taxi Travel Patterns using GPS Trajectory Data: A Case Study of Beijing. KSCE Journal of Civil Engineering. 2019;23: 1797-1805. DOI: 10.1007/s12205-019-0580-6
Dawei N, Zhichao M, Hai W, Weibo Y, Wendong Z, Xiao G. A cross traffic estimate method for high speed networks. Proceedings of IEEE 14th International Conference on Communication Technology, 2012, Chengdu; 2012.
Liu Y, Feng X, Wang Q, Zhang H, Wang X. Prediction of Urban Road Congestion Using a Bayesian Network Approach. Procedia - Social and Behavioral Sciences. 2014;138: 671-678. DOI: 10.1016/j.sbspro.2014.07.259
Lee J, Hong B, Lee K, Jang YJ. A Prediction Model of Traffic Congestion Using Weather Data. Proceedings of IEEE International Conference on Data Science and Data Intensive Systems, 2015, Sydney; 2015.
Liu Y, Wu H. Prediction of Road Traffic Congestion Based on Random Forest. Proceedings of 10th International Symposium on Computational Intelligence and Design (ISCID), 2017, Hangzhou; 2017.
Mondal MA, Rehena A. Intelligent Traffic Congestion Classification System using Artificial Neural Network. Proceedings of Second International Conference on Advanced Computational and Communication Paradigms (ICACCP-2019); 2019.
Elleuch W, Wali A, Alimi AM. 2017. Intelligent Traffic Congestion Prediction System Based on ANN and Decision Tree Using Big GPS Traces. Advances in Intelligent Systems and Computing. 2017; 557. DOI: 10.1007/978-3-319-53480-0_47
Liu Z, Li Z, Wu K, Li M. Urban Traffic Prediction from Mobility Data Using Deep Learning. IEEE Network. 2018;32(4): 40-46. DOI: 10.1109/MNET.2018.1700411
Zhang S, Yao Y, Hu J, Zhao Y, Li S, Hu J. Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks. Sensors (Basel). 2019;19(10): 2229. DOI: 10.3390/s19102229
Zhang T, Liu Z, Cui Z, Leng J, Xie WH, Zhang L. Short-Term Traffic Congestion Forecasting Using Attention-Based Long Short-Term Memory Recurrent Neural Network. Proceedings of International Conference on Computational Science; 2019.
Zhou X, Dong P, Xing P, Sun P. Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network. Future Internet. 2019;11: 247. DOI: 10.3390/fi11120247
Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks. 1994;5(2): 157-166. DOI: 10.1109/72.279181
Cho KH, Merrienboer BV, Bahdanau D, Bengio Y. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, October, 2014, Doha, Qatar; 2014.
Graves A, Mohamed A, Hinton G. Speech recognition with deep recurrent neural networks. Proceedings of ICASSP 2013, May, 2013, Vancouver, Canada; 2013.
Sutskever I, Vinyals O, Quoc VL. Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems. 2014;4: 3104-3112.
Sun S, Chen J, Sun J. Traffic congestion prediction based on GPS trajectory data. International Journal of Distributed Sensor Networks. 2019;15(5). DOI: 10.1177/1550147719847440
Chen M, Yu G, Chen P, Wang YP. Traffic Congestion Prediction Based on Long-Short Term Memory Neural Network Models. Proceedings of 17th COTA International Conference of Transportation Professionals; 2017.
Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. 2017;39(12): 2481-2495. DOI: 10.1109/TPAMI.2016.2644615
Liu X, Deng Z, Yang Y. Recent progress in semantic image segmentation. Artif. Intell. Rev. 2019;52(2): 1089-1106. DOI: 10.1007/s10462-018-9641-3
Kurniawan J, Syahra SG, Dewa CK. Traffic Congestion Detection: Learning from CCTV Monitoring Images using Convolutional Neural Network. Procedia Computer Science. 2018;144: 291-297. DOI: 10.1016/j.procs.2018.10.530
Ma X, Dai Z, He Z, Ma J, Wang Y, Wang Y. Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors. 2017;17(4): 818. DOI: 10.3390/s17040818
Zahid M, Chen Y, Jamal A, Memon MQ. Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers. Sensors. 2020;20(3): 685. DOI: 10.3390/s20030685
Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 1962;60(1): 106-154.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems. 2012;25(2). DOI: 10.1145/3065386
Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review. 1958;65(6): 386-408.
Ruder S. An overview of gradient descent optimization algorithms. arXiv. 2016; arXiv:1609.04747.
Matthew DZ. AdaDelta: An adaptive learning rate method. arXiv. 2012; arXiv:1212.5701v1.
Kingma DP, Ba JL. Adam: A method for stochastic optimization. arXiv. 2014; arXiv:1412.6980v9.
Rumelhart D, Hinton G, Williams R. Learning representations by back-propagating errors. Nature. 1986;323: 533-536. DOI: 10.1038/323533a0
James LM, Rumelhart DE. Schemata and Sequential Thought Processes in PDP Models. Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Psychological and Biological Models. 1987; 7-57.
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