A Short-Term Traffic Flow Prediction Model Based on EMD and GPSO-SVM

摘要：

　　According to the time and space,randomness and volatility of traffic flow,a short-term traffic flow forecasting model based on empirical mode decomposition(EMD),genetic particle swarm optimization(GPSO)and support vector machine(SVM)is proposed.Firstly,the traffic flow sequence is decomposed into different frequency components by EMD.Then the crossover and mutation factors of the genetic algorithm are introduced into PSO to optimize the parameters of SVM,and the optimal SVM is obtained.Finally,the traffic flow components of different frequencies are input to the optimized SVM to realize the short time traffic flow prediction.A practical example is given based on the measured data of the road network in Changchun City.The results show that,the best effect and the highest prediction accuracy were obtained by the proposed model compared with PSO-SVM and GPSO-SVM.

关键词： traffic flow prediction empirical model decomposition genetic algorithm particle swarm optimization algorithm support vector machine

作者: Mei Duo Yan Qi Gao Lina E Xu

作者单位: College of Information Science and Technology Bohai University Jinzhou,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 2554-2558

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）