Nonlinear Model Predictive Control for Coordinated Traffic Flow Management in Highway Systems

Abstract

The growing level of freeway traffic congestion comprises an everyday life issue with social, economic, and environmental implications for modern metropolitan areas. There is evidence that Variable Speed Limits (VSL) and Ramp Metering (RM) are two effective practical approaches to ameliorate traffic congestion. In this work we use the augmented METANET model, which is one of the most widely used macroscopic models for freeway traffic, to demonstrate the positive effects that these approaches can have on traffic flow and congestion. Since the modified METANET is a nonlinear model, nonlinear model predictive control (NLMPC) is a control method pathway for this system. It performs as a recursive on-line finite-horizon optimization of nonlinear problems, subject to the system dynamics and additional constraints, and has the privilege of prediction of future system states. We utilized the NLMPC method for the coordination of VSL and RM in highway networks. We simulate the implementation of the proposed control method on a freeway that contains a typical setting of on-ramps, off-ramps, as well as a lane drop that creates a physical bottleneck. The simulation results demonstrate significant improvement in the traffic flow conditions and provide useful insights about the way that VSL and RM manage to achieve this improvement. Understanding the special characteristics of capacity drop in highways, and how to ameliorate it, is crucial for future large-scale implementations.