Chapter
Apr 26, 2012
Road Traffic Incident Detection Model Based on SMO
Authors: Haozhe Cong [email protected], Shouen Fang, and Jing GuoAuthor Affiliations
Publication: International Conference on Transportation Engineering 2009
Abstract
Road traffic automatic incident detection system is the important composition of road traffic monitoring system. On the basis of support vector machines (SVM) theory and sequential minimal optimization (SMO) algorithm, the road traffic incident detection model based on SMO was put forward. With Visual C++ and Matlab, a simulation experiment was applied to the model. The shortcomings of quadratic programming (QP) algorithm were analyzed. The effects of three different kernel functions on sample training SVM classifier and on detection performance were compared using Chunking algorithm and SMO algorithm. The results show that Gauss kernel function is better than other two kernel functions for the performance of training and incident detection using SMO.
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© 2009 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Automation and robotics
- Computer programming
- Computing in civil engineering
- Education
- Engineering fundamentals
- Infrastructure
- Mathematics
- Models (by type)
- Optimization models
- Practice and Profession
- Simulation models
- Systems engineering
- Traffic accidents
- Traffic engineering
- Traffic management
- Traffic models
- Training
- Transportation engineering
Authors
Affiliations
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai, China. E-mail: [email protected]
Shouen Fang
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai, China
Jing Guo
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, School of Transportation Engineering, Tongji University, Shanghai, China
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