摘要
分析了交通流由线性和非线性部分组成,使用ARIMA模型、指数平滑模型和灰色模型对线性部分进行预测,并且以预测误差平方和最小为准则确定这3个模型预测结果的最优加权系数,得到这3个模型的最优组合,最后对非线性残差部分使用支持向量机模型进行预测.通过实例分析发现,组合预测模型相比单个预测方法具有更高的精度,能够较准确地对交通流进行预测.
The linear and non-linear components of traffic flow time series are analyzed. ARIMA model, exponential smoothing model and the gray model are used to predict the linear component of traffic flow time series, The optimal weighting coefficients of the three model predictions are determined by the criterion of the minimum square sum of the forecasting, then the optimal combination of the three models are generated. Finally, the SVM model is applied to the nonlinear residual component prediction. An ex- ample is given to show that the combination model can produce more accurate predictions of traffic flow than that of single model.
出处
《暨南大学学报（自然科学与医学版）》
CAS
CSCD
北大核心
2010年第5期457-461,共5页
Journal of Jinan University(Natural Science & Medicine Edition)
基金
广东省科技计划项目(2009B011400046)
广东省自然科学基金项目(9151001003000005)
