基于季节ARIMA模型的公路交通量预测

Highway Traffic Volume Forecasting Based on Seasonal ARIMA Model

为了提高公路交通量季节性预测的精度,在介绍一般ARIMA模型的基础上,推导出一种具有周期的季节ARI-MA模型的一般表达式,以及使用这种模型进行建模和预报的一般过程。在实证分析中,先用傅立叶周期分析法检验时间序列的周期性并求出周期长度,然后用Eviews软件对时间序列作平稳性检验以及实现模型的识别、建立、选择与预测过程。与三个常用季节预测模型:分组回归模型、可变季节指数预测模型和季节周期回归模型相比,季节ARIMA模型的预测精度最高。研究结果对于更为科学准确地预测公路交通量具有一定意义。

In order to improve the accuracy of seasonal highway traffic volume forecasting, a general expression of seasonal ARIMA model with periodicity was presented based on the normal ARIMA model, and then the procedures of modeling and forecasting via seasonal ARIMA model were provided. In the feasibility-study experiment, the seasonal length was calculated by Fourier period analysis method, the test of the stationarity of the time series, and identifying, establishing, choosing and forecasting of the model were done by Eviews software. Compared with three normal seasonal forecasting models （group regression model, variable seasonal index forecasting model and seasonal regression model）, the seasonal ARIMA model can obtain the highest accuracy in forecasting. The research result is significant to forecast highway traffic volume more accurately.