Identification of freeway traffic dynamics using fluid and black-box nonlinear models

The goal of this paper is that of reporting the results obtained in the identification of a macroscopic dynamic model that can describe the freeway traffic using the information available from a wireless cellular network. To this end, we need to assume that the distribution of mobile terminals aboard cars be uniform along the freeway and to deal with the cells of the cellular network as the sections in which a freeway stretch is divided. The information on the mobile terminals concerns their positions and speed. Two different approaches are investigated. The former is an extension to quite a standard freeway macroscopic model, while the latter is a black-box approach that consists in using a neural network to represent the traffic dynamics. The parameters of the former and the neural weights of the latter are identified off line by a least-squares approach. Numerical results obtained after identification and validation are reported using the data generated by a microscopic simulator.