基于小波分析的交通参数组合预测方法

为了更加准确地预测交通参数的变化趋势,结合小波理论处理时变信息的优势,设计了一种在小波分析的基础上利用BP神经网络进行预测的组合预测方法。该方法采用小波理论与神经网络结合的策略,具有普适性,且比传统的基于小波分析的组合预测过程简单,为大运算量的实时应用提供了可能。利用实际数据对该方法进行了验证,并与对比算法进行了效果对比。结果表明,本文提出的方法预测效果较好,具有一定的实用性。

Combined method for traffic parameter prediction based on wavelet analysis

Considering the superiority of the wavelet theory in dealing with the time-varying informations, a combined method was proposed based on the wavelet analysis and the BP neural network to predict the trend of the traffic parameters more accurately. The new method is characterized by general adaptability because of the strategy combining the wavelet theory and the neural network. It is simpler than the traditional wavelet-based combined method, providing the possibility of application in the real-time large amount computation. The method was verified by the practical traffic data and compared with the traditional equivalence. The results show that the proposed method leads to better prediction, is applicable for the real condition.