The traffic light control (TLC) problem involves the adjustment of green and red signal settings in order to control the traffic flow through an intersection and, more generally, through a set of intersections and traffic lights in an urban roadway network. The ultimate objective is to minimize congestion (hence delays experienced by drivers and resulting reductions in fuel usage and pollution) at a particular intersection, as well as an entire area consisting of multiple intersections. We model an intersection as a stochastic hybrid system and 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 (IPA), 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. This approach capitalizes on several properties of IPA, including gradient estimate unbiasedness under very mild conditions, robustness with respect to the stochastic processes involved, and scalability in the number of events in the system.
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