Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation

This study compares the performance of multilayer perceptron neural networks and maximum-likelihood doubly-constrained models for commuter trip distribution. Our experiments produce overwhelming evidence at variance with the existing literature that the predictive accuracy of neural network spatial interaction models is inferior to that of maximum-likelihood doubly-constrained models with an exponential function of distance decay. The study points to several likely causes of neural network underperformance, including model non-transferability, insufficient ability to generalize, and reliance on sigmoid activation functions, and their inductive nature. It is concluded that current perceptron neural networks do not provide an appropriate modeling approach to forecasting trip distribution over a planning horizon for which distribution predictors (number of workers, number of residents, commuting distance) are beyond their base-year domain of definition.