基于支持向量机方法的短时交通流量预测方法

在总结已有多种预测模型的基础上,充分考虑了交通本身所存在的非线性、复杂性和不确定性,提出了一种基于支持向量机的短时交通流量预测模型。实例数据验证结果和基于BP神经网络的预测模型的对比结果表明,该模型在精度、收敛时间、泛化能力、最优性等方面均优于基于BP神经网络的预测模型。

Short-term traffic flow prediction method based on SVM

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.