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《China Civil Engineering Journal》 2008-01
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A study on urban short-term traffic flow forecasting based on a nonlinear time series model

Sun Xianghai1 Liu Tanqiu2(1.Changsha University of Science & Technology,Changsha 410076,China; 2.Post-Doctor Work Station of Mathematics,Central South University,Changsha 410075,China)  
The data of urban short-term traffic flow is characterized by complicated nonlinearity,thus a nonlinear time series model is proposed and employed to explore how traffic flow varies with time so that accurate predictions may be obtained.Traffic flow conditions on urban roads are categorized into two states,namely the congested traffic state and the free-flow traffic state,and traffic flow varies in different ways under different states,which is consistent with the structure of a two-regime self-exciting threshold autoregressive model(SETAR).The estimates obtained from a case study show the SETAR model can not only offer accurate simulations,but also very well explain the nonlinear characteristics of traffic flow.The out-of-sample prediction performance of the proposed model is compared with the autoregressive integrated moving average model(ARIMA),and the results show the former does better than the latter.
【Fund】: 国家自然科学基金(50608010)
【CateGory Index】: U491.14
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Chinese Journal Full-text Database 2 Hits
1 LIU Tanqiu1,SUN Xianghai2,ZHONG Xiang3(1.School of Economics and Management,Changsha University of Science & Technology,Changsha Hunan 410114,China;2.School of Traffic and Transportation Engineering,Changsha University of Science & Technology,Changsha Hunan 410114,China;3.College of Mechanics and Aerospace,Hunan University,Changsha Hunan 410082,China);Short-term Traffic Flow Forecasting Based on a Three-regime SETAR Model[J];Journal of Highway and Transportation Research and Development;2010-10
2 LIU Ning,CHEN Yu-ting, YU Hui-qun,FAN Gui-sheng(Department of Computer Science and Engineering,East China University of Science and Technology,Shanghai 200237,China);Traffic Flow Forecasting Method Based on Elman Neural Network[J];Journal of East China University of Science and Technology(Natural Science Edition);2011-02
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2 YANG Yong-qin, LIU Xiao-ming, YU Quan, CHU Shi-xin (Transportation Research Center, Beijing University of Technology, Beijing 100022, China; Qingdao Municipal Engineering Design Institute, Qingdao 266071, China);Research on Three Traffic Flow Parameters[J];Journal of Beijing University of Technology;2006-01
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【Secondary References】
Chinese Journal Full-text Database 1 Hits
1 LI Chang-jin,TAN Man-chun(Department of Mathematics,Jinan University,Guangzhou 510632,China);Improvement of traffic flow combination prediction base on the optimal weighting method[J];Journal of Jinan University(Natural Science & Medicine Edition);2010-05
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