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《Transport Standardization》 2010-11
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Prediction Model for Road Traffic Accident Based on Support Vector Regression

WANG Li-juan,ZHA Wei-xiong (Institute of Transportation and Economics,East China Jiaotong University,Nanchang 330013,China)  
In view of road accident prediction has such characteristics as great stochastic fluctuation,little information and nonlinear data series prediction,the Support Vector Regression(SVR) is presented and prediction model for road traffic accidents based on SVR is established.The practical instance proves that the road traffic accident prediction model based on SVR has such advantages as nonlinear data,less information,simple modeling and quick computation.Compared with RBF neural network prediction model,SVR prediction model has higher precision and better generalization,therefore it is much well applied to road traffic accident prediction.
【CateGory Index】: U491.31
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【Citations】
Chinese Journal Full-text Database 4 Hits
1 ZHANG Jie LIU Xiao-ming HE Yu-long CHEN Yong-sheng (Beijing Key Lab of Traffic Engineering,Beijing University of Technology,Beijing 100022,China);Application of ARIMA Model in Forecasting Traffic Accidents[J];Journal of Beijing University of Technology;2007-12
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【Co-citations】
Chinese Journal Full-text Database 10 Hits
1 YU Hui-jie , ZHANG Xiang-dong(Institute of ITS,Xidian University.Xian 710071,China);The application of dynamic equal dimension new information model in prediction of the traffic accidents[J];Electronic Instrumentation Customer;2008-02
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3 TANG Shi-xing1,KE Feng-qin2(1.Department of Mathematic and Physics,Chengde Petroleum College,Chengde 067000,China;2.Department of Automotive Engineering,Chengde Petroleum College,Chengde 067000,China);The ARIMA Model of Overseas Tourism Source in HeBei and its Application[J];Journal of Guangxi University for Nationalities(Natural Science Edition);2009-02
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【Secondary Citations】
Chinese Journal Full-text Database 6 Hits
1 SUN Yan-feng, LIANG Yan-chun, JIANG Jing-qing, WU Chun-guo(College of Computer Science and Technology, Jilin University, Changchun 130012,China);Neural network methods in financial time series forcasting[J];Journal of Changchun Post and Telecommunication Institute;2004-01
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