The problem of clearing congestion situations in freeway traffic is address
ed for both an N-stage and an infinite-stage control horizon (in the latter
case, a receding-horizon control mechanism is used). Traffic is controlled
by regulating the vehicle access to the freeway and by limiting the vehicl
e speed by means of variable message signs. To describe the traffic behavio
r, a "classical" macroscopic model, first proposed by Payne, is adopted. Ev
en though the problem is stated within a deterministic context, an optimal
control law in feedback form is sought to react to unpredictable events. Th
e resulting functional optimization problem is reduced to a nonlinear progr
amming problem by constraining the control law to take on a tired structure
in which free parameters have to be optimized. For such a structure, a mul
tilayer feedforward neural mapping is chosen. Simulation results show the e
ffectiveness of the proposed method in two different case studies. For the
simulation of the second case study, real traffic data are used, which allo
ws one to very well represent critical traffic conditions on freeways.