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.
This paper aims to develop a traffic control approach that provides an optimal real-time solution for large-scale highway networks. The traffic flow model’s nonlinearity is the main reason for the complex optimization problems that consequently require high computational effort. Feedback linearization is a well-known approach in nonlinear systems control. It can provide an exact linear representation of the original nonlinear system. Thus, it facilitates further steps in the controller design. This research combines the feedback linearization method and linear MPC to simultaneously guarantee optimal performance and real-time feasibility. While developing feedback linearization for METANET model, we discovered a pattern that describes the expansion of the control signals (ramp metering and variable speed limits) and disturbances effects through the highway networks. Utilizing this pattern, we design a generic feedback linearization control law for METANET model. The control law provides the key connection between the linearized and original model. Finally, we complete the methodology by employing a linear MPC to regulate the linearized model. The performance of the designed method is evaluated by conducting comprehensive simulation studies, including a large-scale network. The simulation results are promising. Eventually, comparing the developed methodology with an equivalent nonlinear MPC verifies a substantial improvement in computational costs.
This paper aims to develop a traffic control approach that provides an optimal real-time solution for large-scale highway networks. The traffic flow model’s nonlinearity is the main reason for the complex optimization problems that consequently require high computational effort. Feedback linearization is a well-known approach in nonlinear systems control. It can provide an exact linear representation of the original nonlinear system. Thus, it facilitates further steps in the controller design. This research combines the feedback linearization method and linear MPC to simultaneously guarantee optimal performance and real-time feasibility. While developing feedback linearization for METANET model, we discovered a pattern that describes the expansion of the control signals (ramp metering and variable speed limits) and disturbances effects through the highway networks. Utilizing this pattern, we design a generic feedback linearization control law for METANET model. The control law provides the key connection between the linearized and original model. Finally, we complete the methodology by employing a linear MPC to regulate the linearized model. The performance of the designed method is evaluated by conducting comprehensive simulation studies, including a large-scale network. The simulation results are promising. Eventually, comparing the developed methodology with an equivalent nonlinear MPC verifies a substantial improvement in computational costs.
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