关键词: 智能交通系统（ITS）, 支持向量机（SVM）, 回归预测, 分类预测

Abstract:

Support vector machine (SVM) is a supervised machine learning algorithm based on statistical learning theory. Due to low requirements for data and excellent generalization ability in regression and classification modeling, SVM is used widely in data analysis and mining modeling of intelligent transportation system. This paper first introduces the basic principles and open source tools of SVM. Next, this paper summarizes the applications of SVM in regression prediction of passenger flow, traffic congestion, traffic accident and traffic carbon emission. After that, this paper summarizes the applications of SVM in classified prediction of traffic status estimation, traffic sign recognition and traffic incident detection. This paper compares SVM with other widely used algorithms in intelligent transportation system. The research status of the optimization algorithms and derivative algorithms based on SVM are analyzed. Finally, this paper prospects the optimization and application trend of SVM in the future intelligent transportation system.

Key words: intelligent transportation system (ITS), support vector machine (SVM), regression prediction, classification prediction