Multi-intersection Traffic Light Control Using Infinitesimal Perturbation Analysis

TL;DR

This paper presents a novel stochastic hybrid system model for multi-intersection traffic light control and uses Infinitesimal Perturbation Analysis to adaptively optimize cycle lengths based on real-time traffic data.

Contribution

It introduces a stochastic flow model and an IPA-based method for online optimization of traffic light cycles at multiple intersections.

Findings

Effective online gradient estimation for traffic light control

Adaptive cycle length adjustment improves traffic flow

Simulation demonstrates approach's effectiveness

Abstract

We address the traffic light control problem for multiple intersections in tandem by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. Using Infinitesimal Perturbation Analysis (IPA), we derive on-line gradient estimates of a cost metric with respect to the controllable green and red cycle lengths. The IPA estimators obtained require counting traffic light switchings and estimating car flow rates only when specific events occur. The estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. Simulation results are included to illustrate the approach.