Chapter
Apr 26, 2012
Short-Term Traffic Flow Prediction Based on Lagrange Support Vector Regression
Authors: Yanzhong Liu [email protected], Xuhong Li [email protected], and Xiaojian Shao [email protected]Author Affiliations
Publication: International Conference on Transportation Engineering 2007
Abstract
The real-time, accurate and credible prediction information of traffic flow is the promise and the basic of realizing dynamic route guidance system, but the traditional prediction methods and neutral network don't work well with their inherent defects. Support Vector Machine (SVM) is a new learning machine with preferable prediction for the small samples. In this paper, a new prediction model of short-term traffic flow based on Lagrange Support Vector Regression (LSVR) was presented. Experimental results showed that compared to other forecasting method, LSVR could make better effects.
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Copyright
© 2007 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
- Engineering fundamentals
- Flow (fluid dynamics)
- Fluid dynamics
- Fluid mechanics
- Hydrologic engineering
- Infrastructure
- Intelligent transportation systems
- Lagrangian functions
- Mathematical functions
- Mathematics
- Models (by type)
- Regression analysis
- Statistical analysis (by type)
- Traffic engineering
- Traffic flow
- Traffic management
- Traffic models
- Transportation engineering
- Transportation management
- Vector analysis
- Water and water resources
Authors
Affiliations
School of Transportation, Southeast University, P.O. Box 210096, Nanjing. E-mail: [email protected]
School of Transportation, Southeast University, P.O. Box 210096, Nanjing. E-mail: [email protected]
College of Science, China Agricultural University, P.O. Box 100083, Beijing. E-mail: [email protected]
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