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A novel abnormal traffic incident detection method based on improved support vector machine

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成果类型:
期刊论文
作者:
Sun, Yan;Hou, Zhixiang
通讯作者:
Sun, Yan(736688480@qq.com)
作者机构:
[Sun, Yan] Xi'an Fanyi University, Xi'an, Shaanxi, 710105, China
[Hou, Zhixiang] College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, 410114, China
通讯机构:
Xi'an Fanyi University, Xi'an, Shaanxi, China
语种:
英文
关键词:
Particle swarm optimization (PSO);Reactive power;Automatic Detection;Classification performance;Data preprocessing;Detection accuracy;Detection performance;Intelligent transportation;Penalty parameters;Traffic incident detections;Support vector machines
期刊:
Journal of Applied Science and Engineering
ISSN:
2708-9967
年:
2018
卷:
21
期:
1
页码:
45-50
机构署名:
本校为其他机构
院系归属:
汽车与机械工程学院
摘要:
In recent years, the traffic incident automatic detection technology has become a central issue in intelligent transportation field. In order to detect traffic incidents accurately, highway traffic incident detection model is set up based on the characteristics of highway traffic flow and the basic principles of traffic incident detection. This model includes data preprocessing module, construction of SVM and decision output module. Improved particle swarm optimization is adopted to optimize parameters of SVM model. By adjusting the penalty par...

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