Linear Parameter Varying Identification of Freeway Traffic Models

Abstract

This paper deals with linear parameter varying (LPV) modeling and identification of a generic, second-order freeway traffic flow model. A non-conventional technique is proposed to transform the nonlinear freeway traffic flow model into a parameter-dependent form. The resulting exact LPV model is equivalent to the original nonlinear dynamics. Simplification of the nonlinear model gives rise to the introduction of an approximate LPV description. The application of parameter varying identification approaches are made possible by the transformation. Closed-loop predictor-based subspace identification for LPV systems (PBSID LPV) is applied to estimate the affine parameter matrices of the LPV freeway models developed. If the model structure of the original plant is assumed to be known, this paper shows a solution how to estimate LPV model parameters based on the identified model. Parameter-dependent models are identified and validated using real detector measurement data in order to emphasize the applicability of the kernel PBSID LPV methodology. Comparison with traditional nonlinear parametric identification, generally used in traffic identification, is also provided.