一种基于双周期时间序列的短时交通流预测算法

According to the characteristics of urban road short-term traffic flow,one kind of short-term traffic flow forecast model named DSARIMA（double seasonal autoregressive integrated moving average model) was proposed,which can meet the requirements of predicting daily pattern and weekly pattern traffic flow in urban road based on the ARIMA （autoregressive integrated moving average model) model and SARIMA （seasonal autoregressive integrated moving average model) model.According to the characteristics of the traffic flow in weekdays and weekends in urban road,a forecast algorithm was given which use two methods to forecast traffic flow by ARIMA model,one forecast usingN₁time quantum before the given time,the other forecast using the same time quantum ofN₂days before the given time,and use improved BAYESIAN model to decide the powers of the two methods by calculating the difference of the forecast results and the real results.The final forecast results were the sum of the respective results of the two methods multiplied by its power.The results showed that the DSARIMA model has better stability and are more accurate than the ARIMA model or SARIMA model when applied to the short-term traffic flow forecast.