A constrained filtering algorithm for freeway traffic state estimation

A real-time traffic state estimation algorithm is developed and applied to a freeway. The evolution of the traffic is defined by a second order macroscopic model which computes, for each section of the freeway, the density and the mean speed according to several non linear equations. Different extensions of the Kalman method were already applied to this model, though none of them considers the natural constraints in the state variables. In this work, a new method that incorporates those natural constraints, is applied to the macroscopic model obtaining better results. To validate the proposed method, a simulation over a freeway section was made using two different tools: the macroscopic simulator called METANET and the microscopic simulator called SUMO. Promising results were obtained using both approaches