Explicit robust model predictive control for urban Traffic network

Traffic cycle times optimization and route control at urban traffic intersections is an important and complex issue in situations where the network is facing uncertainty. In this paper, a new method for obtaining the optimal traffic signal for the urban traffic network is presented which one of the important features of the proposed method is that uncertain traffic demands are taken into account. For this purpose, among all proposed models for the urban traffic network, a model with the uncertainty is selected, and design a robust model predictive control for this. Besides, to overcome the complexity of online computing, multi-parametric programming has been used to solve the model offline, and the designed controller has been analyzed using traffic data. The results show that while meeting many of the network needs, the proposed control scheme may reduce the amount of online computing, which makes it possible to use it in real urban traffic networks.