| 基于随机森林的交通事件检测方法设计与分析(英文) |
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| 引用本文： | 刘擎超,陆建,陈淑燕.基于随机森林的交通事件检测方法设计与分析(英文)[J].东南大学学报,2014(1):88-95. |
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| 作者姓名： | 刘擎超 陆建 陈淑燕 |
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| 作者单位： | [1]东南大学城市智能交通江苏省重点实验室,南京210096 [2]现代城市交通技术江苏高校协同创新中心,南京210096 |
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| 基金项目： | The National High Technology Research and Devel- opment Program of China （863 Program） （No. 2012AA112304）, the Scientific Innovation Research of College Graduates in Jiangsu Province （No. CXZZ13 _ 0119）. |
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| 摘 要： | 为了进一步提高决策树模型的交通事件检测性能,且避免噪音和过拟合现象,提出了基于随机森林的交通事件检测方法.从分类强度和相关性2个角度进行分析,并构建了3组实验:与不同数目决策树的对比、与不同决策树的对比及与神经网络的对比.实验数据采用实测的高速公路交通参数数据库(I-880数据库);实验的评价指标采用检测率、误警率、平均检测时间、分类率和ROC曲线下的面积.实验结果表明,基于随机森林的交通事件检测模型可以提高检测率、减少检测时间、提高分类正确率,和多层前馈神经网络相比具有很好的竞争力.
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| 关 键 词： | 智能交通系统 随机森林 交通事件检测 交通模型 |
Design and analysis of traffic incident detection based on random forest |
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| Liu Qingchao Lu Jian Chen Shuyan.Design and analysis of traffic incident detection based on random forest[J].Journal of Southeast University(English Edition),2014(1):88-95. |
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| Authors: | Liu Qingchao Lu Jian Chen Shuyan |
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| Institution: | Liu Qingchao Lu Jian Chen Shuyan (Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China) (Jiangsu Province Collaborative Innovation Center of Modem Urban Traffic Technologies, Nanjing 210096, China) |
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| Abstract: | In order to avoid the noise and over fitting and further improve the limited classification performance of the real decision tree, a traffic incident detection method based on the random forest algorithm is presented. From the perspective of classification strength and correlation, three experiments are performed to investigate the potential application of random forest to traffic incident detection： comparison with a different number of decision trees; comparison with different decision trees; comparison with the neural network. The real traffic data of the 1-880 database is used in the experiments. The detection performance is evaluated by the common criteria including the detection rate, the false alarm rate, the mean time to detection, the classification rate and the area under the curve of the receiver operating characteristic （ROC）. The experimental results indicate that the model based on random forest can improve the decision rate, reduce the testing time, and obtain a higher classification rate. Meanwhile, it is competitive compared with multi-layer feed forward neural networks （MLF）. |
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| Keywords: | intelligent transportation system random forest traffic incident detection traffic model |
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