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CLC number: TP393; U491.13

On-line Access: 2017-02-10

Received: 2015-11-03

Revision Accepted: 2016-02-26

Crosschecked: 2016-12-23

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Dong-wei Xu

http://orcid.org/0000-0003-2693-922X

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.2 P.287-302

10.1631/FITEE.1500381


Real-time road traffic state prediction based on ARIMA and Kalman filter


Author(s):  Dong-wei Xu, Yong-dong Wang, Li-min Jia, Yong Qin, Hong-hui Dong

Affiliation(s):  College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; more

Corresponding email(s):   dongweixu@zjut.edu.cn

Key Words:  Autoregressive integrated moving average (ARIMA) model, Kalman filter, Road traffic state, Real-time, Prediction


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Dong-wei Xu, Yong-dong Wang, Li-min Jia, Yong Qin, Hong-hui Dong. Real-time road traffic state prediction based on ARIMA and Kalman filter[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 287-302.

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Abstract: 
The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average (ARIMA) and the kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the kalman filter is feasible and can achieve high accuracy.

This article describes how the author have implemented an ARIMA state space representation and used Kalman filtering for traffic condition predictions. This is an interesting idea.

基于ARIMA和Kalman滤波的道路交通状态实时预测

概要:道路交通流预测不仅可以为出行者提供实时有效的信息,而且可以帮助他们选择最佳路径,减少出行时间,实现道路交通路径诱导,缓解交通拥堵。本文提出了一种基于ARIMA模型和Kalman滤波算法的道路交通流预测方法。首先,基于道路交通历史数据建立时间序列的ARIMA模型。其次,结合ARIMA模型和Kalman滤波法构建道路交通预测算法,获取Kalman滤波的测量方程和更新方程。然后,基于历史道路交通数据进行算法的参数设定。最后,以北京的四条路段作为案例,对所提出的方法进行了分析。实验结果表明,基于ARIMA模型和Kalman滤波的实时道路交通状态预测方法是可行的,并且可以获得很高的精度。

关键词:ARIMA模型;Kalman滤波;建模;训练;预测

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Reference

[1]Brockwell, P.J., Davis, R.A., 2006. ARMA models. In: Casella, G., Fienberg, S., Olkin, I. (Eds.), Introduction to Time Series and Forecasting. Springer Science & Business Media, Berlin, Germany, p.83-100.

[2]Chang, T.H., Chueh, C.H., Yang, L.K., 2011. Dynamic traffic prediction for insufficient data roadways via automatic control theories. Contr. Eng. Pract., 19(12):1479-1489.

[3]Chen, B.K., Xie, Y.B., Tong, W., et al., 2012. A comprehensive study of advanced information feedbacks in real-time intelligent traffic systems. Phys. A, 91(8):2730-2739.

[4]Chen, C.Y., Hu, J.M., Meng, Q., et al., 2011. Short-time traffic flow prediction with ARIMA-GARCH model. IEEE Intelligent Vehicles Symp., p.607-612.

[5]Diebold, F.X., Mariano, R.S., 1995. Comparing predictive accuracy. J. Bus. Econ. Stat., 13(3):134-144.

[6]Dong, C.F., Ma, X., Wang, G.W., et al., 2009. Prediction feedback in intelligent traffic systems. Phys., 388(21):4651-4657.

[7]Dong, C.F., Ma, X., Wang, B.H., 2010. Weighted congestion coefficient feedback in intelligent transportation systems. Phys. Lett. A, 374(11):1326-1331.

[8]Durbin, J., Koopman, S.J., 2012. Time Series Analysis by State Space Methods. Oxford University Press, London, UK.

[9]Guo, J.H., Huang, W., Williams, B.M., 2014. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp. Res. Part C, 43:50-64.

[10]Hoong, P.K., Tan, I.K.T., Chien, O.K., et al., 2012. Road traffic prediction using Bayesian networks. IET Int. Conf. on Wireless Communications and Applications, p.1-5.

[11]Kirchgässner, G., Wolters, J., Hassler, U., 2012. Introduction to Modern Time Series Analysis. Springer Science & Business Media, Berlin, Germany.

[12]Kumar, K., Parida, M., Katiyar, V.K., 2013. Short term traffic flow prediction for a non urban highway using artificial neural network. Proc.-Soc. Behav. Sci., 104:755-764.

[13]Lin, L., Li, Y., Sadek, A., 2013. A k nearest neighbor based local linear wavelet neural network model for online short-term traffic volume prediction. Proc.-Soc. Behav. Sci., 96:2066-2077.

[14]Liu, H., Tian, H.Q., Li, Y.F., 2012. Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Appl. Energy, 98:415-424.

[15]Liu, J.Y., Wang, W.D., Gong, X.Y., et al., 2012. A hybrid model based on Kalman filter and neutral network for traffic prediction. IEEE 2nd Int. Conf. on Cloud Computing and Intelligent Systems, p.533-536.

[16]Liu, X.L., Jia, P., Wu, S.H., et al., 2011. Short-term traffic flow forecasting based on multi-dimensional parameters. J. Transp. Syst. Eng. Inform. Technol., 11(4):140-146 (in Chinese).

[17]Lv, L., Chen, M., Liu, Y., et al., 2015. A plane moving average algorithm for short-term traffic flow prediction. In: Cau, T., Lim, E.P., Zhou, Z.H., et al. (Eds.), Advances in Knowledge Discovery and Data Mining. Springer Int. Publishing, Cham, Switzerland, p.357-369.

[18]Ma, T., Zhou, Z., Abdulhai, B., 2015. Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction. Transp. Res. Part B, 76:27-47.

[19]Ma, X.L., Tao, Z.M., Wang, Y.H., et al., 2015. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C, 54:187-197.

[20]Min, W., Wynter, L., 2011. Real-time road traffic prediction with spatio-temporal correlations. Transp. Res. Part C, 19 (4):606-616.

[21]Moretti, F., Pizzuti, S., Panzieri, S., et al., 2015. Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. Neurocomputing, 167:3-7.

[22]Ojeda, L.L., Kibangou, A.Y., de Wit, C.C., 2013. Adaptive Kalman filtering for multi-step ahead traffic flow prediction. IEEE American Control Conf., p.4724-4729.

[23]Pan, T.L., Sumalee, A., Zhong, R.X., et al., 2013. Short-term traffic state prediction based on temporal–spatial correlation. IEEE Trans. Intell. Transp. Syst., 14(3):1242-1254.

[24]Park, J., Li, D., Murphey, Y.L., et al., 2011. Real time vehicle speed prediction using a neural network traffic model. IEEE Int. Joint Conf. on. Neural Networks, p.2991-2996.

[25]Qi, Y., Ishak, S., 2014. A hidden Markov model for short term prediction of traffic conditions on freeways. Transp. Res. Part C, 43:95-111.

[26]Smith, B.L., Williams, B.M., Oswald, R.K., 2002. Comparison of parametric and nonparametric models for traffic flow forecasting. Transp. Res. Part C, 10(4):303-321.

[27]Sommer, M., Tomforde, S., Haehner, J., 2015. A systematic study on forecasting of traffic flows with artificial neural networks. Proc. 28th Int. Conf. on. Architecture of Computing Systems, p.1-8.

[28]Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C., 2005. Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp. Res. Part C, 13(3):211-234.

[29]Wang, J., Shi, Q.X., 2013. Short-term traffic speed forecasting hybrid model based on chaos–wavelet analysis-support vector machine theory. Transp. Res. Part C, 27:219-232.

[30]Zhang, L., Ma, J., Sun, J., 2012. Examples of validating an adaptive Kalman filter model for short-term traffic flow prediction. 12th Int. Conf. of Transportation Professionals, p.912-922.

[31]Zhang, L., Liu, Q.C., Yang, W.C., et al., 2013. An improved k-nearest neighbor model for short-term traffic flow prediction. Proc.-Soc. Behav. Sci., 96:653-662.

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