Solomon Kidane Zegeye

Learn More
Model-based traffic control for balanced reduction of fuel consumption, emissions, and travel time, " Pro-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(More)
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(More)
Reduction of travel times and traffic emissions using model predictive control, " Abstract— 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(More)
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(More)
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(More)
based traffic control for the reduction of fuel consumption, emissions, and travel time, " Proceedings of mobil. Abstract In this paper we use a model-based traffic control approach to determine dynamic speed limits with the aim of reducing fuel consumption and emissions, while still guaranteeing small travel times. The approach we propose is based on model(More)
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(More)
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(More)