Short-term traffic flow prediction method based on SVM

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作者
College of Transportation, Jilin University, Changchun 130022, China [1 ]
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来源
Jilin Daxue Xuebao (Gongxueban) | 2006年 / 6卷 / 881-884期
关键词
Computer simulation - Convergence of numerical methods - Forecasting - Neural networks - Optimization - Statistics;
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摘要
Summarizing the existing prediction models and considering the characteristics of the traffic itself such as nonlinearity, complexity and uncertainty, a short-term traffic flow prediction model based on support vector machine (SVM) was proposed. The real traffic data validation and the comparison with the traffic flow prediction model based on the BP neural network resulted in a conclusion that this model is superior to the BP neural network model on the aspects of prediction accuracy, convergence time, generalization ability, optimization possibility and so on.
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