An Extreme Value Theory Approach for Understanding Queue Length Dynamics in Adaptive Corridors

TL;DR

This paper applies extreme value theory to analyze maximum queue lengths in adaptive corridor traffic control, providing a new method to assess the extremity of queue lengths and improve corridor management.

Contribution

It introduces the first use of extreme value analysis for queue length time series in adaptive traffic corridors, offering a novel approach to evaluate queue extremities.

Findings

Maximum queue lengths follow extreme value distributions.

Extreme queue lengths can predict spillover effects.

Method aids in evaluating adaptive controller effectiveness.

Abstract

This paper introduces a novel approach employing extreme value theory to analyze queue lengths within a corridor controlled by adaptive controllers. We consider the maximum queue lengths of a signalized corridor consisting of nine intersections every two minutes, roughly equivalent to the cycle length. Our research shows that maximum queue lengths at all the intersections follow the extreme value distributions. To the best knowledge of the authors, this is the first attempt to characterize queue length time series using extreme value analysis. These findings are significant as they offer a mechanism to assess the extremity of queue lengths, thereby aiding in evaluating the effectiveness of the adaptive signal controllers and corridor management. Given that extreme queue lengths often precipitate spillover effects, this insight can be instrumental in preempting such scenarios.