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Abstract-In this paper, an advanced control strategy, i.e. Model Predictive Control (MPC), is applied to control and coordinate urban traffic networks. However, due to the nonlinearity of the prediction model, the optimization of MPC is a nonlinear non-convex optimization problem. In this case, the online computational complexity becomes a big challenge for the MPC controller, if it is implemented in real-life traffic network. To overcome this problem, the on-line optimization problem is reformulated into a Mixed-Integer Linear Programming (MILP) optimization problem, so as to increase the real-time feasibility of the MPC control strategy. The new optimization problem can be solved very efficiently by existing MILP solvers, and the global optimum of the problem is guaranteed. Moreover, we propose an approach to reduce the complexity of the MILP optimization problem even further. The simulation results show that the MILPbased MPC controllers can reach the same performance, but the time taken to solve the optimization becomes only a few seconds, which is a significant reduction compared with the time required by the original MPC controller.Index Terms-Urban traffic network control, Model predictive control, Urban traffic modeling.