  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.
