基于樸素貝葉斯分類的高速公路交通事件檢測
發(fā)布時間：2018-11-26 06:53
【摘要】：提出一種基于樸素貝葉斯分類的高速公路非重現交通事件檢測算法.將交通事件的檢測看作是0-1分類問題,采用交通波動理論建立交通事件的特征屬性概念模型,并利用分段離散化的方法將連續(xù)特征變量轉換為離散特征變量,設計基于樸素貝葉斯算法的交通事件分類器.以典型高速公路的一條路段進行VISSIM仿真試驗.結果表明:該算法的檢測率高,且在高強度狀況下,算法魯棒性良好,適用于高速公路交通事件檢測系統(tǒng).
[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.
【作者單位】： 同濟大學道路與交通工程教育部重點實驗室;
【基金】：國家自然科學基金(50408034) 上海市創(chuàng)新基金(11ZZ27)
【分類號】：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.
【作者單位】： 同濟大學道路與交通工程教育部重點實驗室;
【基金】：國家自然科學基金(50408034) 上海市創(chuàng)新基金(11ZZ27)
【分類號】：U495
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