广西师范大学学报（自然科学版）2012，Vol.30Issue(4)：13-17,5.
基于SVM短时交通流量预测
Short-term Traffic Flow Prediction Based on SVM
摘要
Abstract
Traffic flow prediction is a very important area in intelligent transportation systems. Traditional prediction methods have a very wide range of applications in the traffic prediction. But traditional prediction methods does not work very well in short-term traffic flow prediction because of the complexity of the influencing factors. With the development of machine learning and data mining,traffic flow prediction with a combination of machine learning and data mining has become more and more important as a research area. In this paper,SVM (Support Vector Machine) is used to build a short-term traffic flow prediction model,and Genetic Algorithm (GA) is used to optimize the SVM penalty factor C and kernel parameter a as well. The results of different kernel functions of SVM are compared,including polynomial kernel and RBF kernel. RBF SVM plays better than polynomial SVM with less training time and higher accuracy and SVM is very suitable for short-term traffic flow prediction.关键词
SVM/交通流量/短时预测/遗传算法Key words
SVM/traffic flow/short-term prediction/genetic algorithm分类
交通工程引用本文复制引用
蒋晓峰,许伦辉,朱悦..基于SVM短时交通流量预测[J].广西师范大学学报（自然科学版）,2012,30(4):13-17,5.基金项目
青年科学基金资助项目(51108191) （51108191）
