基于小波包和最小二乘支持向量机的短时交通流组合预测方法研究

Research on Short-Term Traffic Flow Combined Forecasting Based on Wavelet Package and Least Square Support Vector Machines

基于小波在处理非线性、非平稳随机信号的优势以及支持向量机在解决小样本、非线性及高维模式识别问题中的优势。笔者探讨结合小波包和最小二乘支持向量机的组合预测方法在交通流短时预测中的应用。首先介绍小波包和最小二乘支持向量机的基本原理,然后提出基于小波包和最小二乘支持向量机的交通流短时组合预测方法,并以北京市快速路的实测交通流量来验证效果,结果表明其可行性和有效性。

Because wavelet is suitable for processing nonlinear, random signals and support vector machines excel at solving less--data, nonlinear, multi-dimension problems, the paper proposes combining of wavelet package with least squares support vector machines for short-term traffic flow forecasting. First, theories of wavelet package and least squares support vector machines are introduced,and then a short-term traffic flow forecasting method based on wavelet package and least squares support vector machines is proposed. Second, the effect of the method is tested by the real-time traffic flows collected in Beijing City. The result shows the feasibility and validity of the proposed method.