支持向量机在交通流量实时预测中的应用

实时、准确的交通流量预测是正在发展的智能交通系统的关键问题之一,对于交通控制和交通流诱导都有着直接的影响。提出一种基于支持向量机的交通流量实时预测模型,通过采用序贯最小优化算法,能够实现对交通流量的有效预测。应用实例表明,支持向量机具有良好的泛化性能,在输入信号混有10%噪声的情况下,支持向量机的鲁棒性更好,预测的平均误差为4.25%,预测结果优于BP神经网络和动态递归神经网络。

Traffic Flow Prediction Using Support Vector Machine Based Method

The accurate prediction of real-time traffic flow is essential in intelligent transportation system(ITS).The prediction results will have direct effect on traffic control and traffic guidance.A traffic flow prediction model using support vector machines(SVMs) based method is proposed.With the sequential minimal optimization(SMO) algorithm,the proposed model can predict traffic flow efficiently.The simulation results show that the SVMs can avoid over-fitting and have better generalization ability compared with BP or recurrent neural networks.The average prediction error is 3.13% if there is no noise in the inputs.When the measurement inputs are disturbed by noise accounting for 10% of the input,the designed SVMs can keep robust and have an average prediction error up to(4.25%.)