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《Journal of South China University of Technology(Natural Science Edition)》 2016-04
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Short-Term Traffic Flow Prediction Based on Phase Space Reconstruction and RELM

SHANG Qiang;YANG Zhao-sheng;LI Zhi-lin;LI Lin;QU Xin;College of Transportation,Jilin University;State Key Laboratory of Automobile Simulation and Control,Jilin University;Jilin Province Key Laboratory of Road Traffic,Jilin University;  
In order to increase the accuracy of short-time traffic flow prediction,a flow prediction model based on the phase space reconstruction and the regularized extreme learning machine is put forward. In this method,the CC method is used to calculate the best time delay and embedding dimension of traffic flow time series for phase space reconstruction,and the G-P algorithm is used to calculate the correlative dimension of the seriesthat is an important judgment index ofthe chaotic characteristics of traffic flow series. Then,the reconstructed phase point data are taken as the inputs and outputsto trainthe regularized extreme learning machine model,and the main parameters of the model are determined by means of grid searching. Finally,a comparative analysis is carried out based on the actual measured traffic flow data. The results show that the proposed model possesses high performance and is effective in improving the accuracy of short-time traffic flow prediction.
【Fund】: 国家科技支撑计划项目(2014BAG03B03);; 国家自然科学基金资助项目(51308249 51308248 51408257);; 山东省省管企业科技创新项目(20122150251-5)~~
【CateGory Index】: U491.14
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