Verkeerslichten die zowel bij rustig als (te) druk verkeer efficiënt en netwerkgericht blijven regelen, rekening houdend met wat er stroomopwaarts en stroomafwaarts gebeurt – het is met de huidige stand van techniek nog een brug te ver. Dat kan echter vlot veranderen. Wetenschappers werken aan een nieuwe generatie voorspellende verkeerslichten die wel raad weten met netwerkcomplexiteit. In deze wetenschappelijke bijdrage vertellen de auteurs over een veelbelovende regelmethode, model predictive control.@en

Controlling road traffic networks is a complex problem. One of the difficulties is the coordination of actuators, such as traffic lights, variables speed limits, ramp metering and route guidance, with the aim to improve the network performance over a near-future time horizon. This dissertation develops algorithms that specifically balance fast computation time and improved traffic network performance; both for freeway traffic in part I, and for urban traffic in part II.@en

In this paper, we develop a hierarchical approach to optimize the signal timings in an urban traffic network taking into account the different dynamics in all traffic regimes. The proposed hierarchical control framework consists of two layers. The first layer--the network coordination layer--uses a model predictive control strategy based on a simplified traffic flow model to provide reference outflow trajectories. These reference outflow trajectories represent average desired link outflows over time. These are then mapped to green-red switching signals which can be applied to traffic lights. To this end, the second layer--the individual intersection control layer--then selects at every intersection the signal timing stage that realizes an outflow which has the smallest error with respect to the reference outflow trajectory. The proposed framework is tested using both macroscopic and microscopic simulations. It is shown that the control framework can outperform a greedy control policy that maximizes the individual intersection outflows, and the control framework can distribute the queues over the network in a way that the network outflow is improved. Simulations using a macroscopic model allow the direct application of the reference outflows computed by the network coordination layer, and the results indicate that the mapping of the reference outflows to the detailed signal timings by the individual intersection control layer only introduces a small performance loss.@en

Freeway congestion can reduce the freeway throughput due to the capacity drop or due to blocking caused by spillback to upstream ramps. Research has shown that congestion can be reduced by the application of ramp metering and variable speed limits. Model predictive control is a promising strategy for the optimization of the ramp metering rates and variable speed limits to improve the freeway throughput. However, several challenges have to be addressed before it can be applied for the control of freeway traffic. This paper focuses on the challenge of reducing the computation time of MPC strategies for the integration of variable speed limits and ramp metering. This is realized via a parameterized control strategy that optimizes the upstream and downstream boundaries of a speed-limited area and the parameters of the ALINEA ramp metering strategy. Due to the parameterization, the solution space reduces substantially, leading to an improved computation time. More specifically, the number of optimization variables for the variable speed limit strategy becomes independent of the number of variable message signs, and the number of optimization variables for the ramp metering strategy becomes independent of the prediction horizon. The control strategy is evaluated with a macroscopic model of a two-lane freeway with two ON-ramps and OFF-ramps. It is shown that parameterization realizes improved throughput when compared with a non-parameterized strategy when using the same amount of computation time.@en

In this paper a novel computationally efficient model predictive control (MPC) method for optimizing flows at urban intersections is proposed. Several linear and quadratic MPC approaches have been developed in the literature to reduce the computational complexity of the problem, but without considering the back-propagating waves associated with spillback. As the principal contribution of this work, a linear optimization problem for an MPC approach is formulated, which considers downstream propagating waves linked to free-flow traffic, queuing dynamics, and upstream propagating waves related to spillback (i.e. forward and backward moving waves, respectively). The linear optimization problem is obtained by describing link dynamics using the link transmission model, and aggregating the traffic dynamics to (several) tens of seconds. The performance of the proposed controller is compared with two other existing strategies; a store-and-forward model-based, and a cell transmission model-based approach. The total time spent (TTS) by all the vehicles in the network and the computation time is applied as performance indexes for the evaluation of the control strategies. Simulation results show that including upstream propagating waves results in better controller performance, due to the explicit modeling of the impact of link outflow on the maximum link inflow.@en

In this paper, the recently developed link transmission model (LTM) is utilized in an online hybrid model-based predictive control (MPC) framework. The model is extended to include the effects of ramp metering and variable speed limits. Next, an integrated freeway traffic control based on the new model is presented in order to minimize the total time spent in the network. The integrated scheme has the capability of controlling large-scale freeway networks in real time as the model is computationally efficient, and it is yet accurate enough for our control purposes. In addition, the extended model is reformulated as a system of linear inequalities with mixed binary and real variables. The reformulated model along with the linearized total travel time objective function establish a mixed-integer linear optimization problem that is more tractable and even faster than the original optimization problem integrated in the MPC scheme. Finally, to investigate the performance of the proposed approaches (nonlinear MPC and the mixed-integer linear counterpart), a freeway network layout based on the Leuven Corridor in Belgium is selected. The extended LTM is calibrated for this network using microsimulation data and then is used for prediction and control of the large network. Microsimulation results show that the proposed methods are able to efficiently improve the total travel time.@en