吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (03): 646-653.doi: 10.7964/jdxbgxb201303015

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Multi-step traffic flow prediction model based on wavelet and echo state network

YANG Fei1,2,3, FANG Bin-xing1,2, WANG Chun-lu1,2, ZUO Xing-quan1,2, LI Li-xiang1,2, PING Yuan1,2   

  1. 1. School of Computer Science, Beijing University of Post and Telecommunications, Beijing 100876, China;
    2. Key Laboratory of Trustworthy Distributed Computing and Service of the Ministry of Education of China, Beijing University of Post and Telecommunications, Beijing 100876, China;
    3. Nanjing Panda Electronics Company Limited, Nanjing 210002, China
  • Received:2012-01-17 Online:2013-05-01 Published:2013-05-01

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Abstract: In light of the noisy chaotic characteristics of traffic flow, a new multi-step traffic flow prediction model based on wavelet and echo state network was proposed. Utilizing multi-scale decomposition method of wavelet, the proposed model restricts the interference of noisy components to the dynamic behavior of the traffic flow; meanwhile it extracts the chaotic low-frequency component, which possesses most of the energy of traffic flow. In predicting the multi components concurrently, the strong prediction capacity of echo state network for chaotic low-frequency component was utilized effectively to ensure the accuracy of multi-step traffic flow prediction. The results of prediction of the real traffic flow in Xizhimen Bridge of Beijing show that the prediction accuracy is significantly improved by the proposed multi-step model in comparison with the traditional echo state network model. Under the condition of high prediction accuracy, the maximum predictable step is also increased by the proposed model.

Key words: engineering of communications and transportation system, traffic flow prediction, echo state network, chaotic attractor, phase space reconstruction

CLC Number: 

  • U491
[1] Qu L, Li L, Zhang Y, et al. PPCA-based missing data imputation for traffic flow volume: a systematical approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(3):512-521.

[2] 杨兆升, 王媛, 管青. 基于支持向量机方法的短时交通流量预测方法[J]. 吉林大学学报:工学版, 2006,36(6):881-884. Yang Zhao-sheng, Wang Yuan, Guan Qing. Short-term traffic flow prediction method based on SVM[J]. Journal of Jilin University (Engineering and Technology Edition), 2006, 36(6): 881-884.

[3] 李松, 刘力军, 解永乐. 遗传算法优化BP神经网络的短时交通流混沌预测[J]. 控制与决策,2011, 26(10):1581-1585. Li Song, Liu Li-jun, Xie Yong-le. Chaotic prediction for short-term traffic flow of optimized BP neural network based on genetic algorithm[J].Control and Decision, 2011, 26(10):1581-1585.

[4] Tan M C, Wong S C, Xu J M, et al. An aggregation approach to short-term traffic flow prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(1): 60-69.

[5] 董宏辉, 孙晓亮, 贾利民, 等. 多模态的交通流量预测模型[J]. 吉林大学学报:工学版,2011,41(3):645-649. Dong Hong-hui, Sun Xiao-liang, Jia Li-min, et al. Multimode traffic volume prediction model[J]. Journal of Jilin University (Engineering and Technology Edition), 2011, 41(3): 645-649.

[6] 殷礼胜, 鲁照权, 董学平.交通流量小波神经网络多步预测研究[J]. 自动化仪表,2011,32(8):7-10. Yin Li-sheng, Lu Zhao-quan, Dong Xue-ping. Research on the multi-step prediction of wavelet neural network for traffic flow[J].Process Automation Instrumentation, 2011, 32(8): 7-10.

[7] 姜桂艳, 常安德, 牛世峰, 等. 基于BP神经网络的交通数据序列动态可预测性分析方法[J].北京工业大学学报,2011, 37(7):1019-1026. Jiang Gui-yan, Chang An-de, Niu Shi-feng, et al. Dynamic predictability analysis for traffic data serials based on BP neural network[J]. Journal of Beijing University of Technology, 2011, 37(7): 1019-1026.

[8] Smith B L, Demetsky M J. Traffic flow forecasting: comparison of modeling approaches[J]. Journal of Transportation Engineering, 1997, 123(4): 261-266.

[9] Xu M, Gao Z Y. Chaos in a dynamic model of urban transportation network flow based on user equilibrium states[J]. Chaos, Solitons & Fractals,2009, 39(2): 586-598.

[10] 王进, 史其信. 短时交通流预测模型综述[J]. ITS通讯,2005,1(1):10-13. Wang Jin, Shi Qi-xin. A review of the short-term traffic flow prediction methods[J]. Intelligent Transportation Systems, 2005,1(1):10-13.

[11] Jaeger H, Haas H. Harnessing nonlinearity: prediction of chaotic time series with neural networks[J]. Science, 2004, 304(5667):78-80.

[12] Jaeger H, Lukosevicius M, Popovici D, et al. Optimization and applications of echo state networks with leaky-integrator neurons[J]. Neural Networks, 2007, 20: 335-352.

[13] 彭宇, 王建民, 彭喜元. 基于回声状态网络的时间序列预测方法研究[J].电子学报,2010,38(增刊1):148-154. Peng Yu, Wang Jian-min, Peng Xi-yuan. Researches on time series prediction with echo state networks[J]. Acta Electronica Sinica, 2010, 38(Sup.1):148-154.

[14] Lgawa K, Ohashi H. A negative selection algorithm for classification and reduction of the noise effect[J]. Applied Soft Computing, 2009, 9(1): 431-438.
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