Scalable Adaptive Traffic Light Control Over a Traffic Network Including Transit Delays

TL;DR

This paper presents a scalable, data-driven traffic light control method that optimizes vehicle flow and reduces waiting times across multiple intersections by using an online gradient-based approach with IPA estimators.

Contribution

It introduces a novel scalable control framework for traffic networks that accounts for transit delays and employs IPA for real-time parameter optimization.

Findings

Controllers improve traffic flow and reduce waiting times.

Method scales efficiently with network size.

Online adaptation outperforms static schedules.

Abstract

We study the Traffic Light Control (TLC) problem for a traffic network with multiple intersections in an artery, including the effect of transit delays for vehicles moving from one intersection to the next. The goal is to minimize the overall mean waiting time and improve the ``green wave'' properties in such systems. Using a stochastic hybrid system model with parametric traffic light controllers, we use Infinitesimal Perturbation Analysis (IPA) to derive a data-driven cost gradient estimator with respect to these parameters. We then iteratively adjust them through an online gradient-based algorithm. We show that the event-driven nature of the IPA estimators driving the controllers leads to scalable computationally efficient controllers as the dimensionality of the traffic network increases.