摘要
针对短时交通流量的预测问题,提出了一种结合卡尔曼滤波与支持向量机的预测模型。该模型采用预测误差平方和与相关系数极大化准则智能选取恰当的预测方式,综合利用了支持向量机的稳定性与卡尔曼滤波的实时性,发挥了两种模型各自的优势。实验结果表明,该模型误差指标均低于单项预测模型。特别地,该模型在高峰时段的预测性能最佳,平均相对误差保持在8%以内,是短时交通流预测的一种有效可行的方法。
Aiming at the issue about short-term traffic flow forecasting, a prediction model combining with Kalman filte-ring and support vector machine was proposed. The model adopts appropriate forecast method intelligently in each pre-diction period by the standards of error sum of squares and vector cosine of the angle,utilizes the stability of SVM and the real-time nature of Kalman filter comprehensively,and takes respective advantages of the two models. Experiments show that the model＇s error indicators are lower than single forecast model. In particular, the model in the peak hours, which average relative error is maintained at less than 8 %, is a feasible and effective method of short-term traffic flow forecasting.
出处
《计算机科学》
CSCD
北大核心
2013年第10期248-251,278,共5页
Computer Science
基金
科技部国家科技支撑计划重点项目(2011BAH25B041)资助
关键词
交通流
组合预测
支持向量机
卡尔曼滤波
Traffic flow, Combining forecasting, Support vector machine, Kalman filter
