摘要
对城市道路短时交通流进行准确预测是实现城市交通控制与交通诱导的关键。针对目前单一预测方法预测精度不高的问题,提出了小波与支持向量机(SVM)融合的预测新方法;同时为了避免SVM知识学习过程陷入局部最优的问题,采用粒子群算法(PSO)来优化SVM的关键参数,以提高对短时交通流量的预测精度。通过对武汉市道路交通流数据的实验分析,结果表明所提出的方法能够准确提取实验数据关键特征,显著提高SVM的预测精度,且结果比单一使用方法提高了近9%。
Accurate and reliable short time traffic flow forecasting of urban road is one of the most important issues in the traffic information management. Due to the nonlinear and stochastic of the data, it is often difficult to predict the traffic flow precisely via a certain method. Hence, a new hybrid intelligent forecasting approach based on the integration of wavelet transform （WT）, particle swarm optimization （PSO） and support vector machine （SVM） is proposed for the short time traffic flow prediction in this paper. The advantage of the proposed method is that the combination of wavelet transform and SVM can deal with the nonlinear and stochastic characteristics of the original data well. The forecasting rate may be enhanced by using this new technique. Furthermore, 360 samples of the practical traffic flow data are applied to the validation of the proposed prediction model. The analysis results show that the proposed method can extract the underlying rules of the testing data and improve the prediction accuracy by 9% or better when compared with SVM approach.
出处
《交通信息与安全》
2011年第4期58-61,共4页
Journal of Transport Information and Safety
