Chapter
Apr 26, 2012
The Research of Freeway Incident Detection System Based on Wavelet and Neural Network
Authors: Jianhua Yan [email protected], Rende Yu [email protected], and Qi Zhang [email protected]Author Affiliations
Publication: International Conference on Transportation Engineering 2009
Abstract
The traditional incident detection is based on time axis or site axis to judge the events by vertical or transverse analysis, which losing the relativity of the monitoring points and the time continuity of every detectors. In this paper, wavelet and neural network are used to analyze the testing data by their own features from vertical or transverse. Wavelet Multi_resolution Analysis is used to searching the data mutational sites in time domain, and the monitoring points data at the same moment are dealt with neural network. Based on the relationship between Wavelet transform coefficient and parameter change of traffic stream, and the Pattern Recognition of neural network, the precision of the accident detection is improved greatly.
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Published In
International Conference on Transportation Engineering 2009
Pages: 2937 - 2942
Copyright
© 2009 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
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ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Data analysis
- Engineering fundamentals
- Equipment and machinery
- Geotechnical engineering
- Geotechnical investigation
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Mathematical functions
- Mathematics
- Methodology (by type)
- Network analysis
- Neural networks
- Probe instruments
- Research methods (by type)
- Site investigation
- Traffic accidents
- Traffic engineering
- Traffic management
- Transportation engineering
- Wavelets
Authors
Affiliations
Shandong University of Technology, School of Transport and Vehicle Engineering, Zibo 255049, China;. E-mail: [email protected]
Shandong University of Technology, School of Transport and Vehicle Engineering, Zibo 255049, China;. E-mail: [email protected]
Shandong University of Technology, School of Transport and Vehicle Engineering, Zibo 255049, China;. E-mail: [email protected]
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