Abstract
Due to the many complex aspects of a traffic system, it has been difficult to determine the optimal signal timing. Much of this difficulty has stemmed from the need to build extremely complex models of the traffic dynamics as a component of the control strategy. This paper presents a fundamentally different approach for optimal signal timing that eliminates the need for such complex models. The approach is based on a neural network serving as the basis for the control law, with the weight estimation occurring in closed-loop mode via the simultaneous perturbation stochastic approximation (SPSA) algorithm. Since the SPSA algorithm requires only loss function measurements, there is no system-wide model required for the weight estimation.
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