A multilayer neural network model is introduced in order to realize a self-organizing traffic control system. The neural model inputs split lengths of signal phases and outputs measures of effectiveness such as queue lengths or performance indexes. The operation is separated into two processes, a training process and an optimization process. In the training process, iterations of the training operation by the backpropagation method were effective in forming a steady input-output relationship between splits and measures of effectiveness. In the optimization process, a stepwise method combining the cauchy machine with a feedback method was proposed. The cauchy machine is a sort of monte carlo method and gives the adjustments in a statistical way. This machine was introduced to urge the convergence and avoid the entrapment into local minimums. The feedback method is based on the steepest descent method and gives the adjustments in a deterministic way. This method has a self-organization ability because it can make adjustments that are closely related to traffic situations. The neural model was applied to a road network consisting of three intersections, and split lengths were optimized in order to minimize the squared sum of queue lengths on inflow links. The neural network model was able to give approximated splits and queue lengths that were in good accordance with analytical ones. This paper appears in transportation research record no. 1324, Communications, traffic signals, and traffic control devices 1991
Abstract