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城市道路交通状态识别是现代智能交通系统的重要组成部分，是交通智能控制、诱导和协同系统的基础.基于支持向量机建立车流量、平均速度和占有率的三维反映空间，以堵塞流、拥挤流、平稳流和顺畅流为标签对道路交通状态进行分类；并在MATLAB平台下利用LiBSVM工具包进行实验分析，对SVM各种核函数的分类效果进行比较研究，实现了支持向量机技术的交通状态模式识别.结果表明：选择的指标能很好地反映交通状态的特征，SVM核函数可以以较高的分类精度区分开交通流的状态识别，数据的归一化对分类的结果具有重要的影响.

Urban Road Traffic Condition Pattern Recognition Based on Support Vector Machine

As an important part of the modern intelligent transportation system, urban transport condition recognition is the base of intelligent control, guidance and synergy system. This paper establishes a three-dimensional space with traffic volume, average speed and occupation ratio. It then classifies transportation condition patterns in terms of blocking flow, crowded flow, steady flow and unhindered flow based on wide literature review. Furthermore, this paper presents the algorithm with the MATALB LiBSVM toolbox. To process the data, this paper compares the classification result of different SVM kernel functions and thus realizes the transport condition pattern recognition via the support vector machine (SVM). The results reveal that the selected indexes effectively reflect the characteristics of the traffic conditions. The SVM kernel function can separate different patterns from traffic flows with high classification accuracy, and the data normalization has a significant influence on the result of classification.