LAUSR

In modern intelligent transportation systems, short-term prediction of freeway entrance flow plays an important role in providing travelers timely traffic conditions. Short-term prediction approaches are expected to be computation-efficient, operation-convenient, and able to forecast with high accuracy. Nevertheless, few existing prediction approaches satisfy all the requirements at the same time. For addressing this issue, we propose an autoregressive (AR) model parameterized by iterative learning control (ILC) (called ARPILC) to make short-term predictions of freeway entrance flows. Different from traditional AR models which have constant parameters, the proposed AR method has time-dependent parameters which are optimized by the ILC method with historical dataset. In this manner, the ARPILC model can describe time-dependent characteristics, especially nonlinear characteristics, of freeway entrance flows much better. The ARPILC model predicts short-term entrance flows as weighted sums of the outputs of several AR models with time-dependent parameters. We validate the ARPILC model with the toll collection data during July 2012 in Jiangsu province, China, and compare it with several existing prediction approaches. The results show that the ARPILC model can predict freeway entrance flows with a high accuracy and has better prediction performance than existing approaches.