基于ARIMA模型的短时交通流实时自适应预测

实时、准确的短时交通流量预测是智能交通系统(ITS)中的一个关键问题。基于采用ARIMA(P，d，0)模型结构的时间序列分析方法，提出一种短时交通流实时自适应预测算法。在该算法中采用带遗忘因子的递推最小二乘方法进行参数估计，采用基于线性最小方差预报原理的Astrom预报算法进行预报。针对大量实测数据进行仿真实验，结果表明：减小遗忘因子可以提高一步预测的性能。此外，将该算法分别应用于工作日和双休日的数据时，仿真实验都取得了较好的预测效果，说明该算法对不同交通流状况具有较好的适应性。

A Real-time Short-term Traffic Flow Adaptive Forecasting Method Based on ARIMA Model

Real-time and accurate short-term traffic flow forecasting has become a critical problem in intelligent transportation systems (ITS). Based on time series analysis method adopting ARIMA(p,d,0) model, a kind of real-time adaptive forecasting method for short-term traffic flow was presented . In this method the recursive forgetting factor least square method (RFFLS) was adopted for parameter estimation. The Astrom forecasting algorithm was used for forecasting, which is based on linear minimum square error of prediction. A lot of real observation data are used for simulation tests and results show that when forgetting factor is decreased, the one-step forecasting performance can be improved. In addition, when this method is respectively applied to the data at the weekday and the weekend, both simulation tests have good forecasting performance, which demonstrates that this method has good adaptability in different traffic flow circumstances.