Short-term traffic flow prediction model based on the optimization of SVM by CS algorithm
In order to improve the accuracy of short-term traffic flow prediction model,a pre-diction model based on CS-SVM is proposed in this study.This model uses cuckoo search(CS)algorithm to optimize support vector machine(SVM).Several groups of typical urban road sections in Qingdao are selected as the research objects.The observed and collected traffic flow data are taken as samples for learning.CS algorithm is used to optimize the main parameters of SVM model.And a short-term traffic flow prediction model based on SVM is established.Finally,CS-SVM model is simulated with several existing models.The results show that CS-SVM model has lower prediction error and better stability than other tradition-al models.Compared with SVM model,the MAE value of CS-SVM model decreased by 6.56%and the RMSE value decreased by 7.36%.Therefore,CS-SVM model can provide effective help to improve urban traffic and enhance the theoretical research on traffic flow.
NETL
NSTL
万方数据
维普
