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《Journal of Sichuan University of Science & Engineering(Natural Science Edition)》 2015-06
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Research on Short-term Freeway Traffic Flow Prediction Based on Improved Wavelet Neural Network

CAO Li;TANG Ling;WU Hao;GAO Xiang;YUE Yinggao;School of Automation and Electronic Information,Sichuan University of Science & Engineering;School of Mechanical Engineering,Sichuan University of Science & Engineering;School of Instrument Science and Engineering,Southeast University;  
In view of the complexity and nonlinear characteristics of urban short-term traffic flow,the short-term traffic flow prediction analytic model base on artificial bee colony algorithm( ABC) optimizing wavelet neurotic network algorithm was proposed. With the wavelet neural network( WNN) as the foundation,and the past urban traffic flow as the predicted sample,the structure,weight and threshold of WNN are optimized by artificial bee colony algorithm,finally the urban shortterm traffic flow prediction mathematical model is established. Through the comparison of experimental simulation,the proposed algorithm is more effective than the WNN algorithm and the particle swarm optimization BP neural network algorithm only,which is an effective and reliable method for traffic flow prediction.
【Fund】: 企业信息化与物联网测控技术四川省高校重点实验室项目(2013WYJ03;2013WYY05;2013WZY01;2014WYJ04);; 酿酒生物技术四川省重点实验室项目(NJ2013-11);; 四川省智慧旅游研究基地项目(ZHZ13-02);; 四川理工学院科研基金项目(2014KY03)
【CateGory Index】: U491;TP183
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