[1] Liu Qingchao, Lu Jian, Chen Shuyan,. Design and analysis of traffic incident detectionbased on random forest [J]. Journal of Southeast University (English Edition), 2014, 30 (1): 88-95. [doi:10.3969/j.issn.1003-7985.2014.01.017]

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Design and analysis of traffic incident detectionbased on random forest()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

30

Issue:

2014 1

Page:

88-95

Research Field:

Traffic and Transportation Engineering

Publishing date:

2014-03-31

Info

Title:

Design and analysis of traffic incident detectionbased on random forest

Author(s):

Liu Qingchao;  Lu Jian;  Chen Shuyan

Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China

Keywords:

intelligent transportation system;  random forest;  traffic incident detection;  traffic model

PACS:

U491

DOI:

10.3969/j.issn.1003-7985.2014.01.017

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 I-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).

References:

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Memo

Memo:

Biographies: Liu Qingchao(1987—), male, graduate; Lu Jian(corresponding author), male, doctor, professor, lujian_-1972@seu.edu.cn.
Foundation items: The National High Technology Research and Development Program of China(863 Program)(No.2012AA112304), the Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXZZ13_-0119).
Citation: 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, 30(1):88-95.[doi:10.3969/j.issn.1003-7985.2014.01.017]

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