Adaptive Quasi-Dynamic Traffic Light Control

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 thresholds. The policy is parameterized by green and red cycle lengths as well as the road content thresholds. Using infinitesimal perturbation analysis, we derive online gradient estimators of a cost metric with respect to the controllable light cycles and threshold parameters and use these estimators to iteratively adjust all the controllable parameters through an online gradient-based algorithm so as to improve the overall system performance under various traffic conditions. The results obtained by applying this methodology to a simulated urban setting are also included.