以交通流率、速度和占有率为输入参数,采用交叉验证法优化模型惩罚参数c和核函数参数γ,建立以径向基为核函数的支持向量机模型,判断道路断面交通流状态；结合设计的道路网综合状态指数,依据自由流、拥挤流和阻塞流状态下占有率划分区间,构建城市快速路网交通流状态判别方法；最后以某一区域路网为例,进行了实证性研究.结果表明:该方法对道路断面交通流状态判别精度可达92.22％；同时能够实现道路网范围内对自由流、拥挤流和阻塞流状态的判别,判别精度可达86.67％.%A support vector machine model based on Radial Basis Function is developed to achieve the end of traffic states identification. The proposed model used traffic flow, speed and occupancy for the input data, and adapted the K-CV method to optimize the parameters c and γ in the model. The method combined with the road network integrated state index, which is designed according to occupancy split interval of free traffic, congested traffic and jam traffic, for identifying traffic states on urban expressway network. The empirical research shows that the proposed method can not only realize the traffic states identification with accuracy 92.22% to the road section, but also achieve the identification to free traffic,congested traffic and jam traffic for the range of road network with the accuracy 86.67%.
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