城市道路交通流短时预测及可靠性分析

为了捕捉交通流随机波动导致的交通流短时预测的不确定性,利用反映预测波动的异方差对可靠性进行量化预测;基于时间序列及其异方差理论,构建了以单整自回归滑动平均ARIMA(0,1,1)模型为均值方程的城市道路交通流短时预测的广义自回归条件异方差GARCH(1,1)模型. 通过ARCH LM检验证实,GARCH(1,1)模型能够有效捕捉并消除ARIMA(0,1,1)模型的异方差性.结果表明:基于GARCH(1,1)模型的城市快速路流量预测的MAPE值不高于10%,城市快速路及主干道速度预测的MAPE值为7.86%~10.24%;与ARIMA(0,1,1)模型预测的固定置信区间相比,在自由流交通状况下,GARCH(1,1)模型在有效预测前提下的预测置信区间更窄;在交通拥挤状况下,GARCH(1,1)模型能够通过放大预测置信区间宽度减少无效预测. 

Short-Term Traffic Flow Forecasting and Reliability Analysis of Urban Road

In order to capture the uncertainty of short-term traffic forecasting caused by the random fluctuation of traffic flow, the heteroscedasticity which can reflect the fluctuation is used to quantify the reliability of traffic forecasting. On the basis of time series and its heteroscedastic theory, a generalized autoregressive conditional heteroscedasticity (GARCH(1,1)) model was developed, in which an autoregressive integrated moving average (ARIMA(0,1,1)) model was used as the mean equation. The ARCH LM test results show that the heteroscedasticity of the ARIMA(0,1,1) model can be effectively captured and eliminated by the proposed GARCH(1,1) model. Performance evaluation illustrates that based on the GARCH(1,1) model, the traffic volume forecasting of urban expressway has a mean absolute percentage error (MAPE) of less than 10%, and the speed forecasting of urban expressway and arterial roads has a MAPE between 7.86% and 10.24%. Compared with the fixed confidence intervals predicted by ARIMA(0,1,1) model, the GARCH(1,1) model can produce narrower forecasting confidence intervals on the premise of effective prediction of free flow traffic conditions; while in congested traffic conditions, the GARCH(1,1) model can produce wider forecasting confidence intervals to improve the forecasting reliability by reducing the invalid prediction.