S. K. Zegeye

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-based traffic control for balanced reduction of fuel consumption, emissions, and travel time

Abstract In this paper we integrate the macroscopic traffic flow model METANET with the microscopic dynamic emission and fuel consumption model VT-Micro. We use the integrated models in the model predictive control (MPC) framework to reduce exhaust emissions, fuel consumption, and travel time using dynamic speed limit control. With simulation experiments we demonstrate the countereffects and conflicting nature of the different traffic control objectives. Our simulation results indicate that a model-based traffic control approach, particularly MPC, can be used to obtain a balanced trade-off between the conflicting traffic control objectives.

Reduction of travel times and traffic emissions using model predictive control

In this paper we present a model-based traffic flow control approach to improve both traffic flow and emissions in a traffic network. A model predictive control (MPC) is implemented using a microscopic car-following traffic flow model and an average-speed-based emission model.We consider reduction of total time spent (TTS) and total emissions (TE) as performance measures of the control strategy. Moreover, with the help of simulations we illustrate that a traffic control strategy, particularly an MPC strategy, aiming at the reduction of the TTS does not necessarily reduce the level of emissions. In particular, when the traffic flow is congested, we demonstrate that a traffic control strategy that addresses TTS (or improvement of the traffic flow) alone can cause an increment in the level of emissions and vice versa. Therefore, in this paper we explain how to integrate both requirements so that a balanced trade-off is obtained.

Integrated urban traffic control for the reduction of travel delays and emissions

An integrated macroscopic traffic model is proposed, which integrates a macroscopic urban traffic flow model with a microscopic traffic emission model for individual vehicles. As a macroscopic model, the integrated model is fast enough for on-line control purposes. Nevertheless, the model can still capture the traffic emissions for vehicles in different states compared with using a macroscopic traffic emission model, because of the accuracy of the microscopic traffic emission model. Model Predictive Control is applied to control urban traffic areas based on the integrated traffic model, aiming at reducing both travel delays and traffic emissions.

Variable speed limits for area-wide reduction of emissions

Although traffic congestion is a pressing problem that drivers face every day, improving the traffic flow does not always create a healthy environment to the people residing in the neighborhood of the freeway. Improved traffic flow neither means efficient fuel consumption of the vehicles. Moreover, reduction of total emissions or travel times in a traffic network does not always guarantee reduction in the area-wide emission levels, because there are many other factors that affect the area-wide emissions. In particular, the direction and speed of wind are important factors that play a significant role in the area-wide emission levels. Therefore, in this paper, we systematically model the effect of wind on the area-wide emission levels and design a model-based traffic controller to reduce the dispersion of emissions. More specifically, a model predictive control (MPC) is used to integrate various variable speed limits in order to provide a balanced trade-off between the area-wide emissions and the travel times. Furthermore, we present a case study to demonstrate the proposed control approach.

Nonlinear MPC for the Improvement of Dispersion of Freeway Traffic Emissions

Abstract In this paper a model-based traffic control is used to design variable speed limits and on-ramp metering rates in order to reduce road traffic generated area-wide emissions near freeways. First an area-wide emission model is proposed and next a nonlinear model predictive control (MPC) approach is applied. The objectives of the MPC controller considered are the emissions, dispersions of emissions in a public area near a freeway, travel times, or the combination of these performance indicators. We compare different controlled scenarios with respect to the uncontrolled case and with respect to each other. The simulation-based case studies indicate that balanced solutions can be obtained using the proposed nonlinear MPC control strategy.

Reduction of area-wide emissions using an efficient model-based traffic control strategy

In addition to the challenge to reduce traffic jams, reduction of traffic emissions in such a way that the dispersion of the emissions to residential areas, hospitals, schools, and other neighborhoods is decreased is a problem that requires state-of-the-art traffic control and management solutions. In this paper we model the dispersion of the emissions from a freeway traffic using a grid-based approach where the variability of the speed and direction wind is considered into account. The model is developed in such a way that the computation time is less than a previously proposed point-source model, while still capturing the important dispersion dynamics so that it can be used for on-line control applications. In order to reduce the dispersion of emissions to a neighborhood we design a parameterized model predictive control (MPC) strategy to optimize variable speed limits and ramp metering rates. We illustrate the proposed dispersion model and control approach with a simulation-based case study.

Variable speed limits for green mobility

Due to increasing environmental concerns the focus of traffic management and control is shifting towards optimizing the traffic control measures to also reduce traffic emissions and fuel consumption. In this context we propose a model-based predictive traffic control approach for the balanced reduction of travel times, emissions, and fuel consumption for freeway networks, where not only the local emissions are taken into account but also the dispersion of the emissions to various target zones near the freeways. The core of the approach is a new efficient model for describing the area-wide dispersion of the emissions that is much faster than the models we have proposed in earlier papers. We present a detailed description of the new so-called expanding grid-based model and we embed it in a model-based predictive traffic control approach using variable speed limits.

Model-Based Predictive Traffic Control: A Piecewise-Affine Approach Based on METANET

Abstract As an alternative to the rather intensive computations when using the nonlinear traffic flow model METANET in a model-based predictive control context, a piecewise-affine (PWA) approximation of the model is proposed. Here, several model equations amongst which the fundamental diagram are approximated by a PWA function. Some selected methods to determine this approximation are shortly discussed. In addition, we make use of model properties and physical insight to improve the PWA approximation. For the purpose of traffic control, the PWA approximation of the METANET model can be used in a model predictive control (MPC) framework. In view of the on-line optimization used by MPC and the related trade-off between accuracy and computational speed, in a case study the accuracy of various approximations is compared to the original nonlinear formulation of METANET.

Model-based Control of Intelligent Traffic Networks

Road traffic networks are increasingly being equipped and enhanced with various sensing, communication, and control units, resulting in an increased intelligence in the network and offering additional handles for control. In this chapter we discuss some advanced model-based control methods for intelligent traffic networks. In particular, we consider model predictive control (MPC) of integrated freeway and urban traffic networks. We present the basic principles of MPC for traffic control including prediction models, control objectives, and constraints. The proposed MPC control approach is modular, allowing the easy substitution of prediction models and the addition of extra control measures or the extension of the network. Moreover, it can be used to obtain a balanced trade-off between various objectives such as throughput, emissions, noise, fuel consumption, etc. Moreover, MPC also allows the integration and network-wide coordination of various traffic control measures such as traffic signals, speed limits, ramp metering, lane closures, etc. We illustrate the MPC approach for traffic control with two case studies. The first case study involves control of a freeway stretch with a balanced trade-off between total time spent, fuel consumption, and emissions as control objective. The second case study has a more complex layout and involves control of a mixed urban-freeway network with total time spent as control objective.