Infinitesimal Perturbation Analysis of Stochastic Hybrid Systems: Application to Congestion Management in Traffic-Light Intersections

TL;DR

This paper introduces a novel control method for traffic-light congestion management using infinitesimal perturbation analysis within stochastic hybrid systems, enabling adaptive, robust, and efficient regulation of traffic flow.

Contribution

It develops an adaptive integral control approach based on infinitesimal perturbation analysis for congestion management at traffic-light intersections, demonstrating robustness and potential for extension.

Findings

Control gains computed via infinitesimal perturbation analysis improve tracking.

The method shows robustness to modeling uncertainties and random effects.

Initial traffic-light model captures key features of traffic flow.

Abstract

This paper presents a new approach to congestion management at traffic-light intersections. The approach is based on controlling the relative lengths of red/green cycles in order to have the congestion level track a given reference. It uses an integral control with adaptive gains, designed to provide fast tracking and wide stability margins. The gains are inverse-proportional to the derivative of the plant-function with respect to the control parameter, and are computed by infinitesimal perturbation analysis. Convergence of this technique is shown to be robust with respect to modeling uncertainties, computing errors, and other random effects. The framework is presented in the setting of stochastic hybrid systems, and applied to a particular traffic-light model. This is but an initial study and hence the latter model is simple, but it captures some of the salient features of traffic-light processes. The paper concludes with comments on possible extensions of the proposed approach to traffic-light grids with realistic flow models.