基于ARIMA和Kalman滤波的道路交通状态实时预测

徐东伟; 王永东; 贾利民; 秦勇; 董宏辉

doi:10.1631/FITEE.1500381

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基于ARIMA和Kalman滤波的道路交通状态实时预测

常规文章 | Updated：2022-05-19

- 基于ARIMA和Kalman滤波的道路交通状态实时预测
- Real-time road traffic state prediction based on ARIMA and Kalman filter
- 信息与电子工程前沿（英文） 2017年18卷第2期页码：287-302
- Affiliations：
  
  1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
  2United Key Laboratory of Embedded System of Zhejiang Province, Hangzhou 310023, China
  3State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University 100044, China
- Author bio：
  
  E-mail:dongweixu@zjut.edu.cn
- Funds：
  
  Project supported by the National Science & Technology Pillar Program (No. 2014BAG01B02)
- DOI：10.1631/FITEE.1500381
  中图分类号： TP393; U491.13
- 收稿：2015-11-03，
  
  录用：2016-2-26，
  
  纸质出版：2017-02
- Accepted：
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徐东伟, 王永东, 贾利民, 等. 基于ARIMA和Kalman滤波的道路交通状态实时预测[J]. 信息与电子工程前沿（英文）, 2017,18(2):287-302.

Dong-wei XU, Yong-dong WANG, Li-min JIA, et al. Real-time road traffic state prediction based on ARIMA and Kalman filter[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 287-302.
徐东伟, 王永东, 贾利民, 等. 基于ARIMA和Kalman滤波的道路交通状态实时预测[J]. 信息与电子工程前沿（英文）, 2017,18(2):287-302. DOI： 10.1631/FITEE.1500381.

Dong-wei XU, Yong-dong WANG, Li-min JIA, et al. Real-time road traffic state prediction based on ARIMA and Kalman filter[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 287-302. DOI： 10.1631/FITEE.1500381.

摘要

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。

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.

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references

PJ Brockwell , , , RA Davis . . ARMA models. In: Casella, G., Fienberg, S., Olkin, I. (Eds.), Introduction to Time Series and Forecasting . . 2006 . . Springer Science & Business Media, Berlin, Germany . . 83 - - 100 . . . .

TH Chang , , , CH Chueh , , , LK Yang . . Dynamic traffic prediction for insufficient data roadways via automatic control theories . . Contr. Eng. Pract. , , 2011 . . 19 ( ( 12 ): ): 1479 - - 1489 . . DOI: 10.1016/j.conengprac.2011.08.007 http://doi.org/10.1016/j.conengprac.2011.08.007 . .

BK Chen , , , YB Xie , , , W Tong , , , 等 . . A comprehensive study of advanced information feedbacks in real-time intelligent traffic systems . . Phys. A , , 2012 . . 91 ( ( 8 ): ): 2730 - - 2739 . . DOI: 10.1016/j.physa.2011.12.032 http://doi.org/10.1016/j.physa.2011.12.032 . .

CY Chen , , , JM Hu , , , Q Meng , , , 等 . . Short-time traffic flow prediction with ARIMA-GARCH model . . 2011 . . IEEE Intelligent Vehicles Symp. . . 607 - - 612 . . DOI: 10.1109/IVS.2011.5940418 http://doi.org/10.1109/IVS.2011.5940418 . .

FX Diebold , , , RS Mariano . . Comparing predictive accuracy . . J. Bus. Econ. Stat. , , 1995 . . 13 ( ( 3 ): ): 134 - - 144 . . DOI: 10.1198/073500102753410444 http://doi.org/10.1198/073500102753410444 . .

CF Dong , , , X Ma , , , GW Wang , , , 等 . . Prediction feedback in intelligent traffic systems . . Phys. , , 2009 . . 388 ( ( 21 ): ): 4651 - - 4657 . . DOI: 10.1016/j.physa.2009.07.018 http://doi.org/10.1016/j.physa.2009.07.018 . .

CF Dong , , , X Ma , , , BH Wang . . Weighted congestion coefficient feedback in intelligent transportation systems . . Phys. Lett. A , , 2010 . . 374 ( ( 11 ): ): 1326 - - 1331 . . DOI: 10.1016/j.physleta.2010.01.011 http://doi.org/10.1016/j.physleta.2010.01.011 . .

J Durbin , , , SJ Koopman . . Time Series Analysis by State Space Methods . . 2012 . . Oxford University Press, London, UK . . . .

JH Guo , , , W Huang , , , BM Williams . . Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification . . Transp. Res. Part C , , 2014 . . 43 50 - - 64 . . DOI: 10.1016/j.trc.2014.02.006 http://doi.org/10.1016/j.trc.2014.02.006 . .

PK Hoong , , , IKT Tan , , , OK Chien , , , 等 . . Road traffic prediction using Bayesian networks . . 2012 . . IET Int. Conf. on Wireless Communications and Applications . . 1 - - 5 . . DOI: 10.1049/cp.2012.2098 http://doi.org/10.1049/cp.2012.2098 . .

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

K Kumar , , , M Parida , , , VK Katiyar . . Short term traffic flow prediction for a non urban highway using artificial neural network . . Proc.-Soc. Behav. Sci. , , 2013 . . 104 755 - - 764 . . DOI: 10.1016/j.sbspro.2013.11.170 http://doi.org/10.1016/j.sbspro.2013.11.170 . .

L Lin , , , Y Li , , , A Sadek . . A k nearest neighbor based local linear wavelet neural network model for online short-term traffic volume prediction . . Proc.-Soc. Behav. Sci. , , 2013 . . 96 2066 - - 2077 . . DOI: 10.1016/j.sbspro.2013.08.223 http://doi.org/10.1016/j.sbspro.2013.08.223 . .

H Liu , , , HQ Tian , , , YF Li . . Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction . . Appl. Energy , , 2012 . . 98 415 - - 424 . . DOI: 10.1016/j.apenergy.2012.04.001 http://doi.org/10.1016/j.apenergy.2012.04.001 . .

JY Liu , , , WD Wang , , , XY Gong , , , 等 . . A hybrid model based on Kalman filter and neutral network for traffic prediction . . 2012 . . IEEE 2nd Int. Conf. on Cloud Computing and Intelligent Systems . . 533 - - 536 . . DOI: 10.1109/CCIS.2012.6664231 http://doi.org/10.1109/CCIS.2012.6664231 . .

XL Liu , , , P Jia , , , SH Wu , , , 等 . . Short-term traffic flow forecasting based on multi-dimensional parameters . . J. Transp. Syst. Eng. Inform. Technol. , , 2011 . . 11 ( ( 4 ): ): 140 - - 146 . . . .

L Lv , , , M Chen , , , Y Liu , , , 等 . . 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 . . 2015 . . Springer Int. Publishing, Cham, Switzerland . . 357 - - 369 . . DOI: 10.1007/978-3-319-18032-8_28 http://doi.org/10.1007/978-3-319-18032-8_28 . .

T Ma , , , Z Zhou , , , B Abdulhai . . Nonlinear multivariate time-pace threshold vector error correction model for short term traffic state prediction . . Transp. Res. Part B , , 2015 . . 76 27 - - 47 . . DOI: 10.1016/j.trb.2015.02.008 http://doi.org/10.1016/j.trb.2015.02.008 . .

XL Ma , , , ZM Tao , , , YH Wang , , , 等 . . Long short-term memory neural network for traffic speed prediction using remote microwave sensor data . . Transp. Res. Part C , , 2015 . . 54 187 - - 197 . . DOI: 10.1016/j.trc.2015.03.014 http://doi.org/10.1016/j.trc.2015.03.014 . .

W Min , , , L Wynter . . Real-time road traffic prediction with spatio-temporal correlations . . Transp. Res. Part C , , 2011 . . 19 ( ( 4 ): ): 606 - - 616 . . DOI: 10.1016/j.trc.2010.10.002 http://doi.org/10.1016/j.trc.2010.10.002 . .

F Moretti , , , S Pizzuti , , , S Panzieri , , , 等 . . Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling . . Neurocomputing , , 2015 . . 167 3 - - 7 . . DOI: 10.1016/j.neucom.2014.08.100 http://doi.org/10.1016/j.neucom.2014.08.100 . .

LL Ojeda , , , AY Kibangou , , , CC de Wit . . Adaptive Kalman filtering for multi-step ahead traffic flow prediction . . 2013 . . IEEE Trans. Intell. Transp. Syst. . . 1242 - - 1254 . . DOI: 10.1109/ACC.2013.6580568 http://doi.org/10.1109/ACC.2013.6580568 . .

TL Pan , , , A Sumalee , , , RX Zhong , , , 等 . . Short-term traffic state prediction based on temporal–spatial correlation . . IEEE Trans. Intell. Transp. Syst. , , 2013 . . 14 ( ( 3 ): ): 1242 - - 1254 . . DOI: 10.1109/TITS.2013.2258916 http://doi.org/10.1109/TITS.2013.2258916 . .

J Park , , , D Li , , , YL Murphey , , , 等 . . Real time vehicle speed prediction using a neural network traffic model . . 2011 . . IEEE Int. Joint Conf. on. Neural Networks . . 1991 - - 1996 . . DOI: 10.1109/IJCNN.2011.6033614 http://doi.org/10.1109/IJCNN.2011.6033614 . .

Y Qi , , , S Ishak . . A hidden Markov model for short term prediction of traffic conditions on freeways . . Transp. Res. Part C , , 2014 . . 43 95 - - 111 . . DOI: 10.1016/j.trc.2014.02.007 http://doi.org/10.1016/j.trc.2014.02.007 . .

BL Smith , , , BM Williams , , , RK Oswald . . Comparison of parametric and nonparametric models for traffic flow forecasting . . Transp. Res. Part C , , 2002 . . 10 ( ( 4 ): ): 303 - - 321 . . DOI: 10.1016/S0968-090X(02)00009-8 http://doi.org/10.1016/S0968-090X(02)00009-8 . .

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

EI Vlahogianni , , , MG Karlaftis , , , JC Golias . . Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach . . Transp. Res. Part C , , 2005 . . 13 ( ( 3 ): ): 211 - - 234 . . DOI: /10.1016/j.trc.2005.04.007 http://doi.org//10.1016/j.trc.2005.04.007 . .

J Wang , , , QX Shi . . Short-term traffic speed forecasting hybrid model based on chaos-avelet analysis-support vector machine theory . . Transp. Res. Part C , , 2013 . . 27 219 - - 232 . . DOI: 10.1016/j.trc.2012.08.004 http://doi.org/10.1016/j.trc.2012.08.004 . .

L Zhang , , , J Ma , , , J Sun . . Examples of validating an adaptive Kalman filter model for short-term traffic flow prediction . . 2012 . . 12th Int. Conf. of Transportation Professionals . . 912 - - 922 . . DOI: 10.1061/9780784412442.094 http://doi.org/10.1061/9780784412442.094 . .

L Zhang , , , QC Liu , , , WC Yang , , , 等 . . An improved k-nearest neighbor model for short-term traffic flow pre-diction . . Proc.-Soc. Behav. Sci. , , 2013 . . 96 653 - - 662 . . DOI: 10.1016/j.sbspro.2013.08.076 http://doi.org/10.1016/j.sbspro.2013.08.076 . .

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