基于支持向量机回归的短时交通流预测模型

Short-Term Traffic Flow Forecasting Model Based on Support Vector Machine Regression

将交通流预测的理论和方法引入交通控制系统,可提高交通控制系统对交通流变化的自适应能力.为此,文中通过引入核函数把短时交通流预测问题转化为高维空间中的线性回归问题,提出了基于支持向量机回归的短时交通流预测模型,并利用广州市交通流检测系统的数据进行实验.结果表明,文中模型的预测结果与实际数据相吻合,预测误差小于基于卡尔曼滤波的预测方法,从而验证了该模型的可行性和有效性.

As the short-term traffic flow forecasting theories and approaches help to improve the ability of traffic control systems to automatically adapt to traffic flow changes,this paper proposes a short-term traffic flow forecasting model based on the support vector machine regression by using a kernel function to transform the issues into a linear regression problem in Hilbert Space.Then,the corresponding experiments are conducted based on the data from the traffic flow detection systems in Guangzhou.It is found that the forecasted results accord well with the actual data,and that the forecasting error of the proposed model is less than those of the prediction methods based on Kalman filtering.Thus,the feasibility and effectiveness of the proposed model are verified.