基于朴素贝叶斯分类的高速公路交通事件检测
发布时间：2018-11-26 06:53
【摘要】：提出一种基于朴素贝叶斯分类的高速公路非重现交通事件检测算法.将交通事件的检测看作是0-1分类问题,采用交通波动理论建立交通事件的特征属性概念模型,并利用分段离散化的方法将连续特征变量转换为离散特征变量,设计基于朴素贝叶斯算法的交通事件分类器.以典型高速公路的一条路段进行VISSIM仿真试验.结果表明:该算法的检测率高,且在高强度状况下,算法鲁棒性良好,适用于高速公路交通事件检测系统.
[Abstract]:Based on naive Bayes classification, an algorithm for detecting non-recurrence traffic events in freeway is proposed. The traffic event detection is regarded as a 0-1 classification problem. The traffic fluctuation theory is used to establish the concept model of the characteristic attribute of the traffic event, and the continuous characteristic variable is transformed into the discrete characteristic variable by the method of piecewise discretization. A traffic event classifier based on naive Bayes algorithm is designed. The VISSIM simulation test was carried out on a section of a typical expressway. The results show that the algorithm has high detection rate and good robustness under the condition of high intensity. It is suitable for highway traffic incident detection system.
【作者单位】： 同济大学道路与交通工程教育部重点实验室;
【基金】：国家自然科学基金(50408034) 上海市创新基金(11ZZ27)
【分类号】：U495
[Abstract]:Based on naive Bayes classification, an algorithm for detecting non-recurrence traffic events in freeway is proposed. The traffic event detection is regarded as a 0-1 classification problem. The traffic fluctuation theory is used to establish the concept model of the characteristic attribute of the traffic event, and the continuous characteristic variable is transformed into the discrete characteristic variable by the method of piecewise discretization. A traffic event classifier based on naive Bayes algorithm is designed. The VISSIM simulation test was carried out on a section of a typical expressway. The results show that the algorithm has high detection rate and good robustness under the condition of high intensity. It is suitable for highway traffic incident detection system.
【作者单位】： 同济大学道路与交通工程教育部重点实验室;
【基金】：国家自然科学基金(50408034) 上海市创新基金(11ZZ27)
【分类号】：U495
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