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《Journal of Highway and Transportation Research and Development》 2013-11
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An Improved Nonparametric Regression Algorithm for Short-term Expressway Traffic Flow Forecasting

SUN Di-hua;LI Chao;LIAO Xiao-yong;School of Automation,Chongqing University;Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education,Chongqing University;  
Aiming at the state vector selection in nonparametric regression for short-term traffic flow forecasting,an improved nonparametric regression algorithm for traffic flow forecasting is proposed based on the characteristic of obvious spatial evolution trend of expressway traffic flow. Taking the travel time of traffic from upstream sections to current section as the basis of dynamic state vector selection,the calculation method of similar mechanism is adjusted according to the effect of each upstream section. The model is tested and verified with the microwave detectors data of Chongqing-Wusheng expressway. The result indicates that the improved nonparametric regression algorithm has overcome the shortcoming that the fixed state vector cannot meet different traffic conditions at the same section,and has a better adaptability to various traffic states and a higher precision of prediction.
【Fund】: 中国工程院重点咨询项目(2012-xz-22);; 教育部博士点基金项目(20120191110047);; 重庆市科技攻关项目(cstc2012gg yyjsB30001)
【CateGory Index】: U491.112
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