A simplified macroscopic urban traffic network model for model-based predictive control

A model predictive control (MPC) approach offers several advantages for controlling and coordinating urban traffic networks. To apply MPC in large urban traffic networks, a fast model that has a low on-line computational complexity is needed. In this paper, a simplified macroscopic urban traffic network model is proposed and tested. Compared with a previous model, the model reduces the computing time by enlarging its updating time intervals, and preserves the computational accuracy as much as possible. Simulation results show that the simplified model reduces the computing time significantly, compared with the previous model that provided a good trade-off between accuracy and computational complexity. We also illustrate that the simplifications introduced in the simplified model have a limited impact on the accuracy of the simulation results. As a consequence, the simplified model can be used as prediction model for MPC for larger urban traffic network.