  We address the traffic light control problem for a single intersection by
viewing it as a stochastic hybrid system and developing a Stochastic Flow Model
(SFM) for it. We adopt a quasi-dynamic control policy based on partial state
information defined by detecting whether vehicle backlog is above or below a
certain threshold, without the need to observe an exact vehicle count. The
policy is parameterized by green and red cycle lengths which depend on this
partial state information. Using Infinitesimal Perturbation Analysis (IPA), we
derive online gradient estimators of an average traffic congestion metric with
respect to these controllable green and red cycle lengths when the vehicle
backlog is above or below the threshold. The estimators are used to iteratively
adjust light cycle lengths so as to improve performance and, in conjunction
with a standard gradient-based algorithm, to seek optimal values which adapt to
changing traffic conditions. Simulation results are included to illustrate the
approach and quantify the benefits of quasidynamic traffic light control over
earlier static approaches.
