基于能力区域的交通状态预测方法

交通状态预测是交通流诱导和交通信息发布系统的重要依据.本文提出了一种基于能力区域的城市快速路交通状态预测方法,该方法通过构建神经网络分类器的能力区域,根据样本数据与交通状态类簇之间的空间距离,预测道路交通状态等级.神经网络分类器的能力区域能够有效融合时间、空间等多种特征,并且不需要考虑各特征之间的相关性,具有很强的适应性.实验结果表明,与经典的预测方法相比,其预测误差明显降低,均等系数增大,基于能力区域的方法预测交通状态具有较高的准确性.

Traffic state prediction based on competence region

Traffic state prediction is a key basis of traffic flow guidance system and traffic information publishing system. This paper presents a new method of forecasting the traffic state of unban expressway based on competence region. This method can predict the traffic state grade of road based on the distance between the sample data and the traffic state cluster center by creating a competence region of neural network classifier. And this method can effectively integrate the temporal and spatial features together without considering the correlation between the different features, and thus it has a strong adaptability. The experimental results show that this traffic state prediction method can reduce the prediction error and improve the equality coefficients compared with the classical algorithms. The prediction method used in this paper is effective and accurate for forecasting traffic state based on the competence region.
Keywords：traffic statepredictiontraffic parametercluster