Maria Kontorinaki

Real-time route diversion control at congested motorway off-ramp areas - Part I: User-optimum route guidance

This paper investigates the application of real-time route diversion policies in the case where recurrent motorway traffic congestion is created due to a saturated off-ramp. In particular, the proposed route diversion policies attempt to avoid the off-ramp queue spill-over onto the motorway mainstream and the resulting motorway congestion by re-routing the motorway vehicles through nearby off-ramps towards the same destination. In particular, this paper addresses the case where real-time route guidance strategies, based on user-optimum considerations, are sufficient to resolve the spill-over and motorway congestion problem; while other cases are treated in a companion paper. The proposed route guidance concepts are tested for a hypothetical but quite typical network infrastructure and traffic scenario by use of macroscopic simulation. The obtained simulation results are compared to the case where no route guidance is applied to the network and reveal interesting features and the potential for significant improvements.

Overview of nonlinear programming methods suitable for calibration of traffic flow models

The calibration of a macroscopic traffic flow model aims at enabling the model to reproduce, as accurately as possible, the real traffic conditions on a motorway network. Essentially, this procedure targets the best value for the parameter vector of the model and this can be achieved using appropriate optimization algorithms. The parameter calibration problem is formulated as a nonlinear, non-convex, least-squares optimization problem, which is known to attain multiple local minima; for this reason gradient-based solution algorithms are not considered to be an option. The methodologies that are more appropriate for application to this problem are mainly some meta-heuristic algorithms which use direct search approaches that allow them to avoid bad local minima. This paper presents an overview of the most suitable nonlinear programming methods for the calibration procedure of macroscopic traffic flow models. Furthermore, an application example, where two well-known macroscopic traffic flow models are evaluated through the calibration procedure, is presented.

Robust Global Adaptive Exponential Stabilization of Discrete-Time Systems With Application to Freeway Traffic Control

This paper is devoted to the development of adaptive control schemes for uncertain discrete-time systems, which guarantee robust global exponential convergence to the desired equilibrium point of the system. The proposed control scheme consists of a nominal feedback law, which achieves robust global exponential stability properties when the vector of the parameters is known, in conjunction with a nonlinear dead-beat observer. The proposed adaptive control scheme depends on certain parameter observability assumptions. The obtained results are applicable to highly nonlinear uncertain discrete-time systems with unknown constant parameters. The successful applicability of the obtained results to real control problems is demonstrated by the rigorous application of the proposed adaptive control scheme to uncertain freeway models. A provided example demonstrates the efficiency of the approach.

Global Exponential Stabilization Of Acyclic Traffic Networks

ABSTRACT This work is devoted to the construction of explicit feedback control laws for the robust global exponential stabilisation of general uncertain discrete-time acyclic networks. We consider discrete-time uncertain network models, which satisfy very weak assumptions. The construction of the controllers and the rigorous proof of the robust global exponential stability for the closed-loop system are based on recently proposed vector-Lyapunov function criteria, as well as the fact that the network is acyclic. It is shown, in this study, that the latter requirement is necessary for the existence of a robust global exponential stabiliser of the desired uncongested equilibrium point of the network. Our main focus is on traffic networks and all assumptions are related to features appearing in traffic models. An illustrative example demonstrates the applicability of the obtained results to realistic traffic flow networks.

Nonlinear adaptive control scheme for discrete-time systems with application to freeway traffic flow networks

This paper is devoted to the development of adaptive control schemes for uncertain discrete-time systems, which guarantee robust, global, exponential convergence to the desired equilibrium point of the system. The proposed control scheme consists of a nominal feedback law, which achieves robust, global, exponential stability properties when the vector of the parameters is known, in conjunction with a nonlinear, dead-beat observer. The obtained results are applicable to highly nonlinear, uncertain, discrete-time systems with unknown constant parameters. The applicability of the obtained results to real control problems is demonstrated by the rigorous application of the proposed adaptive control scheme to uncertain freeway models. A provided example demonstrates some features of the approach.