Model Predictive Control for Integrating Traffic Control Measures

Dynamic traffic control measures, such as ramp metering and dynamic speed limits, can be used to better utilize the available road capacity. Due to the increasing traffic volumes and the increasing number of traffic jams the interaction between the control measures has increased such that local control measures are often not sufficient anymore. In order to take into account these interactions, the control measures need to be coordinated such that they all serve the same objective. The central topic of this Ph.D. thesis is the coordinated control of traffic control measures with model predictive control. Besides the theoretical formulation of the general traffic control problem in the framework of model predictive control, a number of case studies are performed with this control approach. Model predictive control is a very suitable control method for traffic control problems, since it includes all elements that are necessary for a successful control of a traffic network: - the integration of the control measures, - the prediction of the effects of the control measures, which is necessary to take into account the effects on longer distances in the network, - a policy-based control goal, - the ability to cope with constraints on the control signal and the admissible traffic states. In this thesis we investigate through simulation the following traffic control scenarios, formulated in the MPC framework: - dynamic speed limits to eliminate shock waves on freeways, - coordinated speed limits and ramp metering to eliminate traffic jams on the freeway at the on-ramp, - coordinated dynamic route guidance and ramp metering to optimize route choice and to provide travel time information to drivers, - integrated control of a mixed urban-freeway network, to prevent shifting problems from one network to the other. Although these simulations give excellent results, traffic control is not a panacea for all traffic problems. Therefore, we present the necessary conditions for successful control, which are essential for an analysis before the control system is applied in practice.Design, Engineering and Productio