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《Systems Engineering-Theory & Practice》 2011-03
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Short-term traffic volume intelligent hybrid forecasting model and its application

SHEN Guo-jiang~1,WANG Xiao-hu~2,KONG Xiang-jie~3 (1.State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China; 2.Shaoxing Branch,Zhejiang Communications Services Co Ltd,Shaoxing 312000,China; 3.School of Software,Dalian University of Technology,Dalian 116620,China)  
A novel intelligent hybrid(IH) model for short-term traffic volume forecasting was presented. The IH model had three sub-models:kalman filter(KF) model,artificial neural network(ANN) model and fuzzy combination(FC) model.By means of the good static linear stabilization character of the kalman filter method,the KF model forecasted the traffic volume by the linear iteration method.Otherwise, the ANN model can estimate the dynamic traffic volume in a very precise and satisfactory sense due to the strong dynamic nonlinear mapping ability of artificial neural network.The FC model mixed the two individual forecast results by fuzzy logic and its output was regarded as the final forecasting of the traffic volume.Practical application results show that the IH model,which takes advantage of the unique strength of the KF model and the ANN model,can produce more precise forecasting than that of two individual models.Thus,this IH model can be an efficient method to the short-term traffic volume forecasting.
【Fund】: 国家高技术研究发展计划(863计划)(2007AA11Z216);; 国家自然科学基金(50708094);; 浙江省自然科学基金(Y1090208)
【CateGory Index】: U491.112
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