Abstract: A method and apparatus for model-free, real-time, system-wide signal timing for a complex road network is provided. It provides timings in response to instantaneous flow conditions while accounting for the inherent stochastic variations in traffic flow through the use of a simultaneous perturbation stochastic approximation (SPSA) algorithm. This is achieved by setting up several (M) parallel neural networks, each of which produces optimal controls (signal timings) for any time instant (within one of the M time periods) based on observed traffic conditions. The SPSA optimization technique is critical to the feasibility of the approach since it provides the values of weight parameters in each of the neural networks without the need for a model of the traffic flow dynamics.

Abstract: A method of developing a controller for general (nonlinear) discrete-time systems, where the equations governing the system are unknown and where a controller is estimated without building or assuming a model for the system. The controller is constructed through the use of a function approximator (FA) such as a neural network or polynomial. This involves the estimation of the unknown parameters within the FA through the use of a stochastic approximation that is based on a simultaneous perturbation gradient approximation, which requires only system measurements (not a system model).