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.
Short-Term Traffic Flow Prediction Based on Lagrange Support Vector Regression
First International Conference on Transportation Engineering ; 2007 ; Southwest Jiaotong University, Chengdu, China
2007-07-09
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Short-Term Traffic Flow Prediction Based on Lagrange Support Vector Regression
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