Infinitesimal Perturbation Analysis for Quasi-Dynamic Traffic Light Controllers

TL;DR

This paper introduces a novel traffic light control method using Infinitesimal Perturbation Analysis to optimize thresholds based on partial state information, improving traffic flow in simulations.

Contribution

It develops an online gradient estimation approach for quasi-dynamic traffic light policies using IPA, enabling adaptive threshold adjustments for better traffic management.

Findings

Effective threshold optimization in simulated urban traffic

Improved traffic flow and reduced delays

Robustness across various traffic conditions

Abstract

We consider the traffic light control problem for a single intersection modeled as a stochastic hybrid system. We study a quasi-dynamic policy based on partial state information defined by detecting whether vehicle backlogs are above or below certain controllable thresholds. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimators of a cost metric with respect to these threshold parameters and use these estimators to iteratively adjust the threshold values through a standard gradient-based algorithm so as to improve overall system performance under various traffic conditions. Results obtained by applying this methodology to a simulated urban setting are also included.