  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.
