Standard and Advanced Backpropagation Models for Image Processing Application in Traffic Engineering

Neural networks have been increasingly applied to many problems in transport planning engineering and the feedforward network with the error backpropagation learning rule, usually called simply “Backpropagation,” has been the most popular neural network. Backpropagation is easy to implement and has been shown to produce relatively good results in many applications. It is capable of approximating arbitrary nonlinear mappings. However, it is noted that one serious disadvantage in the standard Backpropagation is the slow rate of convergence, requiring very long training times. In order to overcome the long training time and susceptibility to trapping at local minima, several enhanced Backpropagation models have been proposed. In this research, the standard Backpropagation and three enhanced Backpropagation models, Backpropagation with Momentum, Quickprop, and Backpropagation with Momentum & Prime-offset (BPMP), have been studied to compare their performance in terms of computing cost and predictive accuracy.