Iterative Learning Identification Method for the Macroscopic Traffic Flow Model
宏观交通流模型参数的迭代学习辨识方法

By transforming the macroscopic traffic flow model into a more general discrete-time nonlinear system model,an iterative learning identification method is developed to estimate the parameters of the more general discrete- time nonlinear system,so the macroscopic traffic flow parameters as well,based on the repeatability of the macroscopic traffic flow behavior in a freeway.With rigorous analysis,it is shown that the proposed learning identification scheme can guarantee the convergence and robustness.A number of simulation results are provided to demonstrate the efficacy of the proposed approach.