Abstract
In recent years, traffic flow prediction has become a crucial technique in ITS (intelligent transportation system), which is helpful for alleviating the congestion in many metropolises and improving the efficiency of public traffic service. On the other hand, with the development of traffic sensors, traffic data are collected with a fantastic scale. It leads ITS into a data-driven application fashion. With this observation, it is a challenge to accurately and promptly forecast the traffic flow by effectively utilizing the big traffic data. In view of this challenge, in this paper, we propose an evolutionary method for short-term traffic flow forecasting service. Concretely, in our method, traffic flow is firstly specified by a model of time series. Then, the model is decomposed into seasonal component and the residual component. The seasonal component reflects the history average condition, while we treat the residual component as the output of a linear filter. The proposed method is evaluated with real bus transaction dataset. The experimental results show the effectiveness of our method.
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References
Bolshinsky, E., Freidman, R.: Traffic flow forecast survey, Technion–Israel Institute of Technology–2012–Technical Report–15p (2012)
Kong, Q.-J., Xu, Y., Lin, S., Wen, D., Zhu, F., Liu, Y.: UTN-model-based traffic flow prediction for parallel-transportation management systems. IEEE Trans. Intell. Transp. Syst. 14(3), 1541–1547 (2013)
Chen, C.P., Zhang, C.-Y.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275, 314–347 (2014)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Lippi, M., Bertini, M., Frasconi, P.: Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Trans. Intell. Transp. Syst. 14(2), 871–882 (2013)
Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)
Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp. 323–336. ACM (2008)
Hou, Y., Edara, P., Sun, C.: Traffic flow forecasting for urban work zones. IEEE Trans. Intell. Transp. Syst. 16(4), 1761–1770 (2015)
Abadi, A., Rajabioun, T., Ioannou, P., et al.: Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transp. Syst. 16(2), 653–662 (2015)
Jun, M., Ying, M.: Research of traffic flow forecasting based on neural network. In: Second International Symposium on Intelligent Information Technology Application, IITA 2008, vol. 2. pp. 104–108. IEEE (2008)
Jiang, X., Adeli, H.: Dynamic wavelet neural network model for traffic flow forecasting. J. Transp. Eng. 131(10), 771–779 (2005)
Tchrakian, T.T., Basu, B., O’Mahony, M.: Real-time traffic flow forecasting using spectral analysis. IEEE Trans. Intell. Transp. Syst. 13(2), 519–526 (2012)
Jeong, Y.-S., Byon, Y.-J., Mendonca Castro-Neto, M., Easa, S.M.: Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 14(4), 1700–1707 (2013)
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Fei, F., Li, S., Dou, W., Yu, S. (2017). An Evolutionary Approach for Short-Term Traffic Flow Forecasting Service in Intelligent Transportation System. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2016. Lecture Notes in Computer Science(), vol 10135. Springer, Cham. https://doi.org/10.1007/978-3-319-52015-5_49
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DOI: https://doi.org/10.1007/978-3-319-52015-5_49
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