Scalable Adaptive Traffic Light Control Over a Traffic Network Including Turns, Transit Delays, and Blocking

TL;DR

This paper presents a scalable, data-driven adaptive traffic light control system for complex traffic networks, incorporating turns, transit delays, and blocking effects, using stochastic hybrid models and online gradient optimization.

Contribution

It introduces a flexible, scalable framework employing Infinitesimal Perturbation Analysis for real-time adaptive traffic signal control considering diverse intersection behaviors.

Findings

Effective real-time control in multi-intersection networks

Incorporates transit delays and blocking effects

Demonstrates scalability and adaptability

Abstract

We develop adaptive data-driven traffic light controllers for a grid-like traffic network considering straight, left-turn, and right-turn traffic flows. The analysis incorporates transit delays and blocking effects on vehicle movements between neighboring intersections. 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 controllable parameters. We then iteratively adjust them through an online gradient-based algorithm to improve performance metrics. By integrating a flexible modeling framework to represent diverse intersection and traffic network configurations with event-driven IPA-based adaptive controllers, we develop a general scalable, adaptive framework for real-time traffic light control in multi-intersection traffic networks.