Hybrid perimeter control with real-time partitions in heterogeneous urban networks: An integration of deep learning and MPC

Network-wide perimeter control strategies have been shown promise in recent years. These perimeter control strategies are mostly based on networks with fixed boundaries. However, fixed partitions may not exploit the full potential of control performance when traffic condition dynamically changes. This study proposes a hybrid MPC-based perimeter control method to tackle traffic congestion with uneven traffic demand loading by an MPC-based perimeter control framework in which the control boundary between reservoirs is dynamically altered. The framework integrates dynamic partitions into the prediction model to formulate an optimization, in which optimal traffic performance is achieved by iteratively finding the appropriate partitions as well as the control decision variables. Moreover, a Deep Learning (DL) based estimator is incorporated into the framework to address the hefty computational burden caused by the nesting of optimization loops. In the hybrid scheme, the DL-based prediction replaces the model based prediction if a confidence condition can be fulfilled. Comparison analysis is conducted to evaluate the necessity of perimeter control with real-time partitioning. Further results show that the hybrid scheme is effective and computationally efficient. Besides, sensitivity analysis of different confidence thresholds pinpoints the robustness of the hybrid scheme.