J. Hellendoorn

Optimal coordination of variable speed limits to suppress shock waves

When freeway traffic is dense, shock waves may appear. These shock waves result in longer travel times and in sudden large variations in the speeds of the vehicles, which could lead to unsafe situations. Dynamic speed limits can be used to eliminate or at least to reduce the effects of shock waves. However, coordination of the variable speed limits is necessary in order to prevent the occurrence of new shock waves and/or a negative impact on the traffic flows in other locations. In this paper, we present a model predictive control approach to optimally coordinate variable speed limits for freeway traffic with the aim of suppressing shock waves. First, we optimize continuous valued speed limits, such that the total travel time is minimal. Next, we include a safety constraint that prevents drivers from encountering speed limit drops larger than, e.g., 10 km/h. Furthermore, to get a better correspondence between the computed and applied control signals, we also consider discrete speed limits. We illustrate our approach with a benchmark problem.

Multi-agent model predictive control for transportation networks: Serial versus parallel schemes

We consider the control of large-scale transportation networks, like road traffic networks, power distribution networks, water distribution networks, etc. Control of these networks is often not possible from a single point by a single intelligent control agent; instead control has to be performed using multiple intelligent agents. We consider multi-agent control schemes in which each agent employs a model-based predictive control approach. Coordination between the agents is used to improve decision making. This coordination can be in the form of parallel or serial schemes. We propose a novel serial coordination scheme based on Lagrange theory and compare this with an existing parallel scheme. Experiments by means of simulations on a particular type of transportation network, viz., an electric power network, illustrate the performance of both schemes. It is shown that the serial scheme has preferable properties compared to the parallel scheme in terms of the convergence speed and the quality of the solution.

A macroscopic traffic flow model for integrated control of freeway and urban traffic networks

We develop a macroscopic model for mixed urban and freeway traffic networks that is particularly suited for control purposes. In particular, we use an extended version of the METANET traffic flow model to describe the evolution of the traffic flows in the freeway part of the network. For the urban network we propose a new model that is based on the Kashani model. Furthermore, we also describe the interface between the urban and the freeway model. This results in an integrated model for mixed freeway and urban traffic networks. This model is especially suited for use in a model predictive traffic control approach.

A Predictive Traffic Controller for Sustainable Mobility Using Parameterized Control Policies

We present a freeway-traffic control strategy that continuously adapts traffic control measures to prevailing traffic conditions and features faster computation speed than conventional model-based predictive control (MPC). The control approach is based on the principles of state feedback control and MPC. Instead of computing the control input sequence, the proposed controller optimizes the parameters of control laws that parametrize the control input sequences. This way, the computational burden of the controller is substantially reduced. We demonstrate the proposed control approach on a calibrated model of part of the Dutch A12 freeway using variable speed limits and ramp-metering rate.

Model predictive control for residential energy resources using a mixed-logical dynamic model

With the increase in the number of distributed energy resources and the amount of intelligence in electricity infrastructures, the possibilities for minimizing costs of household energy consumption increase. Household systems are hybrid systems, in the sense that they exhibit both continuous and discrete dynamics. In this paper the mixed-logical dynamic framework is used to construct a dynamic model of a household system equipped with distributed energy resources. A model predictive controller (MPC) is then proposed that uses the mixed-logical dynamic model to control the energy flows inside the household. In simulation studies we assess the performance of the proposed controller, and we illustrate how additional profits can be obtained by increasing the decision freedom of the controller.

OPTIMAL COORDINATION OF VARIABLE SPEED LIMITS TO SUPPRESS SHOCK WAVES

A model predictive control (MPC) approach is presented to optimally coordinate variable speed limits for highway traffic. A safety constraint incorporated in the controller is formulated that prevents drivers from encountering speed limit drops larger than, say, 10 km/h. The control objective is to minimize the total time that vehicles spend in the network. This approach results in dynamic speed limits that reduce or even eliminate shock waves. To predict the evolution of the traffic flows in the network, which is required by MPC, an adapted version of the METANET model is used that takes the variable speed limits into account. The performance of the discrete-valued and safety-constrained controllers is compared with the performance of the continuous-valued unconstrained controller. It is found that both types of controllers result in a network with less congestion, a higher outflow, and hence a lower total time spent for drivers. For the benchmark problem, the performance of the discrete controller with safety constraints is comparable with the continuous controller without constraints.

A simplified macroscopic urban traffic network model for model-based predictive control

Abstract A model predictive control (MPC) approach offers several advantages for controlling and coordinating urban traffic networks. To apply MPC in large urban traffic networks, a fast model that has a low on-line computational complexity is needed. In this paper, a simplified macroscopic urban traffic network model is proposed and tested. Compared with a previous model, the model reduces the computing time by enlarging its updating time intervals, and preserves the computational accuracy as much as possible. Simulation results show that the simplified model reduces the computing time significantly, compared with the previous model that provided a good trade-off between accuracy and computational complexity. We also illustrate that the simplifications introduced in the simplified model have a limited impact on the accuracy of the simulation results. As a consequence, the simplified model can be used as prediction model for MPC for larger urban traffic network.

Model predictive control for perturbed continuous piecewise affine systems with bounded disturbances

Continuous piecewise-affine systems are a powerful tool for describing or approximating both nonlinear and hybrid systems. In this paper, we extend the model predictive control (MPC) framework for continuous piecewise-affine systems that we have developed previously to deterministic uncertainty. We show that the resulting MPC optimization problem can be transformed into a sequence of linear optimization problems (LP), which can be solved very efficiently.

Integrated model predictive control of dynamic route guidance information systems and ramp metering

We propose an integrated approach for dynamic route guidance and ramp metering control using model predictive control (MPC). The main control objective is to minimize the total time spent in the network by giving accurate travel times as controller input to the network while taking into account the effect of other traffic control measures, such as ramp metering. The travel times shown on the dynamic route guidance panels allow the drivers to make a choice based on possible alternatives. By aiming at minimizing the total time spent as well as the difference between travel times shown to the drivers and the travel times realized by the drivers, the interests of both the individual drivers as well as the road administration are pursued. Simulation results for a case study show that the proposed integrated MPC traffic control results in a lower total time spent while the drivers get accurate travel time information.

A Test Bed for Multi-Agent Control Systems in Road Traffic Management

In this paper we present a test bed for multi-agent control systems in road traffic management. In literature no consensus exists about the best configuration of the traffic managing multi-agent system and how the activities of the agents that comprise the multi-agent system should be coordinated. The system should be capable of managing different levels of complexity, a diversity of policy goals, and different forms of traffic problems. The test bed aids in-depth research in this field, which we demonstrate by means of two example scenarios we have implemented.