Diamantis Manolis

Queue Management Techniques for Metered Freeway On-Ramps

Ramp metering is beneficial for freeway throughput, but the ramp queues that are created should be prevented from extending to adjacent street junctions. A recently proposed ramp queue controller is investigated in conjunction with the local ramp-metering algorithm ALINEA. Microscopic simulation is used to compare the queue controller with a popular queue override scheme that is based on specifically designed scenarios and evaluation criteria. It is found that the queue controller outperforms the queue override and leads to fewer instances of ramp queue spillover. The concept is also demonstrated to work properly in an operational field environment.

Real-Time Estimation of Critical Occupancy for Maximum Motorway Throughput

The local ramp metering strategy ALINEA aims at stabilizing downstream motorway occupancy near critical occupancy to achieve maximum motorway throughput. Two previously proposed real-time estimation schemes for the critical occupancy are fine-tuned, modified, and evaluated by use of extensive traffic data from eight locations of the UK M6 motorway. The evaluation results indicate satisfactory estimation accuracy, rapid tracking behavior, and high robustness for measurement noise, detector failures, and different kinds of traffic conditions. The reported investigations open the way to field implementation of the adaptive AD-ALINEA ramp metering strategy that incorporates real-time estimation of critical occupancy.

Adaptive Cruise Control Operation for Improved Motorway Traffic Flow

This study presents an ACC (adaptive cruise control)–based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that motorway traffic flow efficiency is improved. The potential benefits obtained by applying the proposed control concept are demonstrated for different ACC penetration rates by use of validated microscopic simulation applied to a real motorway stretch where recurrent traffic congestion is created under the current manual driving conditions because of an on-ramp bottleneck. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average vehicle delay and fuel consumption by reducing the space-time extent of congestion compared with the case of only manually driven or regular ACC vehicles.

An Autonomic Methodology for Embedding Self-tuning Competence in Online Traffic Control Systems

Recent advances in technology, control and computer science play a key role towards the design and deployment of the next generation of intelligent transportation systems (ITS). The architecture of such complex systems is crucial to include supporting algorithms that can embody autonomic properties within the existing ITS strategies. This chapter presents a recently developed adaptive optimization algorithm that combines methodologies from the fields of traffic engineering, automatic control, optimization and machine learning in order to embed self-tuning properties in traffic control systems. The derived adaptive fine-tuning (AFT) algorithm comprises an autonomic tool that can be used in online ITS applications of various types, in order to optimize their performance by automatically fine-tuning the system’s design parameters. The algorithm has been evaluated in simulation experiments, examining its ability and efficiency to fine-tune in real time the design parameters of a number of traffic control systems, including signal control for urban road networks. Field results are in progress for the urban network of Chania, Greece, as well as for energy-efficient building control. Some promising preliminary field results for the traffic control problem of Chania are presented here.

Integrated Ramp Metering and Mainstream Traffic Flow Control on Freeways Using Variable Speed Limits

Abstract In this paper we integrate local ramp metering (RM) with Mainstream Traffic Flow Control (MTFC) enabled via Variable Speed Limits (VSL), which is a recently proposed freeway traffic management tool. The integration is performed by the extension of an existing simple local cascade feedback MTFC controller via a split-range-like scheme. The integrated controller remains simple yet efficient and suitable for field implementation. The controller is evaluated in simulation and compared to stand-alone RM or MTFC via VSL, as well as optimal control results. The controllers performance is shown to meet the specifications and to approach the optimal control results for the investigated scenario.

Neural Network Control of Unknown Nonlinear Systems with Efficient Transient Performance

In this paper we provide a neural-based semi-global stabilization design for unknown nonlinear state-feedback stabilizable systems. The proposed design is shown to guarantee arbitrary good transient performance outside the regions where the system is uncontrollable. This is made possible through an appropriate combination of recent results developed by the author in the areas of adaptive control and adaptive optimization and a new result on the convex construction of Control Lyapunov Functions (CLF) for nonlinear systems.