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

摘要：

支持向量机（SVM）是一种基于统计学习理论的有监督机器学习算法，具有优秀的泛化和低数据要求的回归与分类建模能力，被广泛应用于智能交通系统的数据分析与挖掘建模中。首先对SVM算法的基本原理和开源工具进行了概述，其次重点综述了SVM算法在客流量、交通拥堵、交通事故和交通碳排放的回归预测应用，同时对交通状态判别、交通标志识别和交通事件检测进行了分类预测应用综述，并对比了其他在智能交通系统中被广泛应用的算法。然后分析总结了SVM算法优化方式和衍生算法的研究现状。最后对SVM算法在未来智能交通系统中的优化与应用趋势进行了展望。

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