Trip generation : comparison of neural networks and regression models

Abstract

This paper deals with trip generation, examining the performance of neural networks (NNs) and commonly used regression models. The research reported herein aims to answer the question of whether NNs can outperform traditional regression models or not. The NNs are tested in 2 situations with regards to the data availability: 1) when data is scarce; and 2) when data is sufficient. Synthetic households, generated using travel diary data, are the basis for the research. These households are divided over a zone in varying complexities, from homogeneous without statistical deviation on the household characteristics, to inhomogeneous with a deviation on household characteristics. The use of synthetic data, without unknown noise, provides an opportunity to clearly determine the impact of complexity on the forecasting results. The question of whether NNs can be used in trip generation modeling is answered affirmatively. Overall, however, NNs do not outperform classical regression models in situations where data is scarce. Advantages over regression models are negligible.