Overview of nonlinear programming methods suitable for calibration of traffic flow models

The calibration of a macroscopic traffic flow model aims at enabling the model to reproduce, as accurately as possible, the real traffic conditions on a motorway network. Essentially, this procedure targets the best value for the parameter vector of the model and this can be achieved using appropriate optimization algorithms. The parameter calibration problem is formulated as a nonlinear, non-convex, least-squares optimization problem, which is known to attain multiple local minima; for this reason gradient-based solution algorithms are not considered to be an option. The methodologies that are more appropriate for application to this problem are mainly some meta-heuristic algorithms which use direct search approaches that allow them to avoid bad local minima. This paper presents an overview of the most suitable nonlinear programming methods for the calibration procedure of macroscopic traffic flow models. Furthermore, an application example, where two well-known macroscopic traffic flow models are evaluated through the calibration procedure, is presented.