Distributed and Hybrid Model Predictive Control for freeway traffic networks José Ramón Domínguez Frejo Universidad de Sevilla Nowadays, most of the dynamic traffic control systems operate according to a local control loop and completely centralized control of large networks is viewed by most practitioners as impractical and unrealistic. However, the use of appropriate non-local and multivariable techniques (as Model Predictive Control) can considerably improve the reduction in the total time spent by the drivers and other traffic performance indexes. This presentation outlines some MPC techniques that facilitate the practical implementation of optimal control algorithms to real large traffic networks: Distributed MPC (considering the network as a set of subsystems controlling each subsystem by one independent MPC), Hybrid MPC (splitting the problem in a continuous optimization for the ramp metering signals and in a discrete optimization for speed limits) and Genetic Algorithms (finding the fittest individuals within a generation, applying genetic operators for the recombination of those individuals, and generating a good offspring). Moreover, the application of these MPC techniques to the control of Ramp Metering, Variables Speed Limits (VSL) and Reversible Lanes for a set ofbenchmarks is presented and the results are compared with previously proposed controllers for freeway traffic control.