Chapter
Apr 26, 2012
A New Fitting Method for Mending Defective Urban Traffic Flow Data
Publication: International Conference on Transportation Engineering 2009
Abstract
Defective data to collect urban traffic flow message is always occurred due to the sensor failure. To mend the defective data, a new approach named SARBF neural network fitting is presented. It combines analysis based on spatial autocorrelation and RBF neural network fitting method. The complete data is determined to mend the defective data according to the spatial autocorrelation of traffic grid. Not only the mending precision is improved and also the limitation of regression analysis is avoided by using RBF neural network. Finally, the experiment to mend the defective traffic flow data in Hangzhou City is shown that the method is practicable.
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Information
Published In
International Conference on Transportation Engineering 2009
Pages: 1661 - 1666
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 collection
- Engineering fundamentals
- Infrastructure
- Methodology (by type)
- Network analysis
- Neural networks
- Regression analysis
- Research methods (by type)
- Spatial analysis
- Spatial data
- Statistical analysis (by type)
- Traffic engineering
- Traffic flow
- Traffic management
- Transportation engineering
- Urban and regional development
- Urban areas
Authors
Affiliations
School of Mechanical and Automotive Engineering, Zhejiang University of Science and Technology, Hangzhou City, Zhejiang Province. E-mail: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.