A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choice

Abstract

Abstract Research in the field of artificial intelligence systems has been exploring the use of artificial neural networks (ANN) as a framework within which many traffic and transport problems can be studied. One appeal of ANN is their use of pattern association and error correction to represent a problem. This contrasts with the random utility maximisation rule in discrete choice modelling. ANN enables a full set of human perceptions about a particular problem to be represented by artificial networks of neurons. A claim of ANN is that it can tackle the problem of travel demand forecasting and modelling as well if not better than the discrete choice approach. The use of such tools in studying individual traveller behaviour thus opens up an opportunity to consider the extent to which there are representation frameworks which complement or replace discrete choice methods. This paper explores the merits of neural networks by comparing the predictive capability of ANN and nested logit models in the context of commuter mode choice.