Application of model predictive control to traffic systems with hierarchical distributed control framework

Abstract

Traffic congestion is one of the chronic problems that degrades an urban traffic sustainable development especially the transport system. It seriously affects the quality of life of many people and becomes a catalyst of an increment of air pollution. If we can improve public infrastructure, logistics, and public transport system, they will reduce traffic congestion problem. This thesis aims to apply model predictive control (MPC) to traffic systems in a framework on hierarchical distributed control. The control structure composes of three layers which are network layer, area layer, and intersection layer. On the network layer and area layer, the control design considers the optimal traffic flow of the network and area, respectively. The optimal traffic flow is distributed as the reference signal for each intersection to calculate the optimal traffic signal. Finally, we test the designed control on a case study by simulation of a Sathorn’s traffic zone with ten intersections. Traffic polices rely on the traffic volume information to control the traffic lights. We compare the performance of MPC to that of existing traffic control system. The simulation result shows that MPC improves the the total traffic flow by almost 2%.