Efficient Model Predictive Control for Variable Speed Limits by Optimizing Parameterized Control Schemes

This paper proposes an efficient model predictive control strategy that is based on the parameterization of a variable speed limit control scheme. Due to the paramiterization, the solution spaces reduces which leads to improved computation time. The parameterized control scheme consist of a speed-limited area in which a constant speed limit is imposed. By changing the position of the head and tail of this speed-limited area over time it is possible to change the density and flow in and downstream of this area. The controller optimizes the location of the head and tail of this area over time in such a way that the flow into a bottleneck or jam wave is changed such that congestion can be prevented or resolved. An advantage of this approach is that the complexity of the optimization problem does not increase with an increase in the number of variable speed limit gantries. The controller is tested using a second-order macroscopic traffic flow model. It is shown that the controller improves the total time spent by all the vehicles in the network with 3.7% compared to the no-control situation. This improvement is realized by resolving a jam wave. It is also shown that the controller can achieve a better performance then other model predictive control strategies, when using the same amount of computation time.