Abstract
A key problem in traffic engineering is the optimization of the flow of vehicles through a given road network. Improving the timing of the traffic signals at intersections in the network is generally the most powerful and cost-effective means of achieving this goal. Recent efforts have resulted in the development of an approach for optimal centralized signal timing that eliminates the need for an open-loop model. The approach is based on a neural network (NN) serving as the basis for the control law, with the internal NN weight estimation occurring real-time in closed-loop mode via the simultaneous perturbation stochastic approximation algorithm. This paper investigates the application of such a non-network-model-based approach and illustrates the approach through a simulation on a nine-intersection, mid-Manhattan, New York network. The simulated traffic network contains varying short and long-term congestion behavior and short-term stochastic, nonlinear effects. The approach results in a net 10% reduction in vehicle wait time relative to the performance of the existing, in-place strategy.
Published Version
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