Abstract
Nowadays many large cities often suffer from road congestion. Then, it is necessary to improve the working efficiency of urban road network with the help of computational intelligence technologies, within the Internet of vehicles (IoV) environment. We are committed to designing traffic flow prediction methods through the use of machine learning models. In this paper, we predict the traffic flow and traffic situation of the selected road segment. Specifically, we propose a deep reinforcement learning (DRL)-based long short-term memory (LSTM) model to predict the traffic flow. On the basis of it, we characterize the traffic situation with fuzzy comprehensive evaluation (FCE)-based model. The experimental results show that the constructed DRL-based LSTM model can accurately predict the traffic flow data. Meanwhile, the traffic situation characterization model can represent the road traffic situation well.
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References
Feng, X., Ling, X., Zheng, H., Chen, Z., Xu, Y.: Adaptive multi-kernel SVM with spatial-temporal correlation for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 20(6), 2001–2013 (2019)
Chen, X., Lu, J., Zhao, J., Qu, Z., Yang, Y., Xian, J.: Traffic flow prediction at varied time scales via ensemble empirical mode decomposition and artificial neural network. Sustainability 12(9), 3678 (2020)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Okutani, I., Stephanedes, Y.J.: Dynamic prediction of traffic volume through Kalman filtering theory. Transp. Res. Part B-Methodol. 18(1), 1–11 (1984)
Papageorgiou, M.: Concise Encyclopedia of Traffic & Transportation Systems. Pergamon Press, Oxford (1991)
Wang, F., Xu, J., Liu, C., Zhou, R., Zhao, P.: On prediction of traffic flows in smart cities: A multitask deep learning based approach. World Wide Web 24, 805–823 (2021). https://doi.org/10.1007/s11280-021-00877-4
Kang, C., Zhang, Z.: Application of LSTM in short-term traffic flow prediction. In: IEEE 5th International Conference on Intelligent Transportation Engineering, pp. 98–101. IEEE, Piscataway (2020)
Wei, W., Wu, H., Ma, H.: An AutoEncoder and LSTM-based traffic flow prediction method. Sensors 19(13), 2946 (2019)
Liu, Q., Wang, B., Zhu, Y.: Short-term traffic speed forecasting based on attention convolutional neural network for arterials. Comput.-Aided Civ. Infrastruct. Eng. 33(11), 999–1016 (2018)
Ma, Y., Zhang, Z., Ihler, A.: Multi-lane short-term traffic forecasting with convolutional LSTM network. IEEE Access 8, 34629–34643 (2020)
Wu, Y., Tan, H., Qin, L., Ran, B., Jiang, Z.: A hybrid deep learning based traffic flow prediction method and its understanding. Transp. Res. Part C-Emerg. Technol. 90, 166–180 (2018)
Hong, W., Dong, Y., Chen, L., Wei, S.: Seasonal support vector regression with chaotic genetic algorithm in electric load forecasting. In: Sixth International Conference on Genetic and Evolutionary Computing, pp. 124–127. IEEE, Piscataway (2012)
Luo, R., Zhong, X., Zhong, L., Li, L.: Research on the intelligent judgment of traffic congestion in intelligent traffic based on pattern recognition technology. Clust. Comput. 22(5), 12581–12588 (2019). https://doi.org/10.1007/s10586-017-1684-8
Khan, S.M., Dey, K.C., Chowdhury, M.: Real-time traffic state estimation with connected vehicles. IEEE Trans. Intell. Transp. Syst. 18(7), 1687–1699 (2017)
Bouyahia, Z., Haddad, H., Derrode, S., Pieczynski, W.: Toward a cost-effective motorway traffic state estimation from sparse speed and GPS data. IEEE Access 9, 44631–44646 (2021)
Islam, M.R., Ibn Shahid, N., ul Karim, D.T., Al Mamun, A.: An efficient algorithm for detecting traffic congestion and a framework for smart traffic control system. In: 18th International Conference on Advanced Communication Technology, pp. 802–807. IEEE, Piscataway (2016)
Wang, Y., Papageorgiou, M.: Real-time freeway traffic state estimation based on extended Kalman filter: a general approach. IEEE Trans. Transp. Res. Part B-Methodol. 39(2), 141–167 (2005)
Zhang, Y., Ning, Y., Wang, R., Malekian, R.: A method for traffic congestion clustering judgment based on grey relational analysis. ISPRS Int. Geo-Inf. 5(5), 71 (2016)
Li, L., Lin, H., Wan, J., Ma, Z., Wang, H.: MF-TCPV: A machine learning and fuzzy comprehensive evaluation-based framework for traffic congestion prediction and visualization. IEEE Access 8, 227113–227125 (2020)
Wang, P., Yu, H., Chowdhury, M., Xiao, L., Wang, L.: Online traffic condition evaluation method for connected vehicles based on multisource data fusion. J. Sens. 18(7), 1687–1699 (2017)
Yang, X., Luo, S., Gao, K., Qiao, T., Chen, X.: Application of data science technologies in intelligent prediction of traffic congestion. J. Adv. Transp. 2019, 1–14 (2019)
Microscopic vehicular mobility trace of Europarc roundabout, Creteil, France. http://vehicular-mobility-trace.github.io
Acknowledgments
This work was supported in part by the Beijing Natural Science Foundation under Grant 19L2029, in part by the National Natural Science Foundation of China under Grants U1836106 and 81961138010, in part by the Beijing Natural Science Foundation under Grant M21032, in part by the Beijing Intelligent Logistics System Collaborative Innovation Center under Grant BILSCIC-2019KF-08, in part by the Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB, under Grants BK20BF010 and BK19BF006, and in part by the Fundamental Research Funds for the University of Science and Technology Beijing under Grant FRF-BD-19-012A.
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Chen, Z., Luo, X., Wang, T., Wang, W., Zhao, W. (2022). Deep Reinforcement Learning-Based LSTM Model for Traffic Flow Forecasting in Internet of Vehicles. In: Deng, Z. (eds) Proceedings of 2021 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-16-6372-7_57
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DOI: https://doi.org/10.1007/978-981-16-6372-7_57
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