中文摘要：

针对移动交通流检测信息的特点，在分析概率神经网络与Global K-means聚类算法的基础上，提出了一种基于移动交通流检测信息的城市路况概率神经网络判别方法。通过分析路况的相关因素，同时考虑信号控制交叉口红灯对车辆行程时间延误的影响，利用Global K-means算法改进的概率神经网络对探测车采集的实时交通信息进行处理，进而得出城市的道路状况。应用结果表明该方法能够有效地判别和跟踪道路状况的变化，比不考虑交叉口红灯的影响时能够更准确地反映城市道路的路况信息。

英文摘要：

A traffic condition recognition method based on floating car data is proposed by analyzing Probability Neural Network （PNN） and Global K-means algorithm.The related factors of traffic condition and the collection method of floating car data are presented.Considering the influence of traffic control intersection delay to travel time,a probability neural network classifier is designed using Global K-means algorithm and applied to the recognition of traffic condition with floating car data.The experiment results show that the method can recognize traffic condition well,which can reflect traffic condition better than that without considering traffic control intersection delay.