  We study the Traffic Light Control (TLC) problem for a single intersection,
considering both straight driving vehicle flows and corresponding crossing
pedestrian flows with the goal of achieving a fair jointly optimal sharing
policy in terms of average waiting times. Using a stochastic hybrid system
model, we design a quasi-dynamic policy controlling the traffic light cycles
with several threshold parameters applied to the light cycles and the partially
observed contents of vehicle and pedestrian queues. Infinitesimal Perturbation
Analysis (IPA) is then used to derive a data-driven gradient estimator of a
cost metric with respect to the policy parameters and to iteratively adjust
these parameters through an online gradient-based algorithm in order to improve
overall performance on this intersection and adapt the policy to changing
traffic conditions. The controller is applied to a simulated intersection in
the town of Veber\"od, Sweden, to illustrate the performance of this approach
using real traffic data from this intersection.
