Traffic Density Estimation and Congestion Identification of Urban Freeway Based on Proportional-Integral Observer

In order to timely and accurately estimate traffic densities of the whole road network and to identify congestion in an urban freeway network when traffic sensors do not cover the freeway network completely and only local measurement data can be utilized. A proportional-integral (PI) observer approach was proposed based on a macroscopic traffic flow model. Firstly, the urban freeway network was modeled by embedding the cell transmission model (CTM) into the dynamic graph hybrid automata (DGHA), and then a piecewise affine linear system (PWALS) model was deduced. Moreover, the PI observer was designed on the basis of this model, and the proportional gain and integral gain were computed via using the Linear Matrix Inequality (LMI) method. Especially, the traffic congestion was identified by checking whether the estimated densities were greater than the critical ones. Finally, as examples, the Jingtong freeway and East Third Ring freeway were given to demonstrate the feasibility of the proposed approach.