基于小波分析和ARIMA模型的交通流预测方法

Study of Traffic Flow Prediction Based on Wavelet Analysis and Autoregressive Integrated Moving Average Model

摘要:

针对实际交通系统时变复杂的特征和交通流变化的不确定性,应用小波分析理论,对原始交通数据进行了消噪处理,使消噪后的数据更能反映交通流的本质及变化规律；采用综合自回归移动平均(ARIMA)时间序列模型对交通流进行预测；并对实测交通数据进行验证分析.结果表明,该方法具有较高的预测精度,可用于交通流的实时动态预测.

Abstract:

Based on the analysis of the fundamental characteristics of the field traffic system and traffic flow,this paper presents a new method of traffic flow prediction.The theory of wavelet analysis is applied to eliminating the noise from the realtime traffic data to better reflect a real traffic flow.Based on the processed traffic data,the timeseries model,autoregressive integrated moving average model(ARIMA)is used to predict traffic flows in different periods.Finally,the paper presents numerical examples on the field data to testify the the proposed model.