 A comparison of the predictive potential of artificial neural networks and nested logit models for commuter mode choiceDavid Hensher and Tu T. Ton Transportation Research Part E: Logistics and Transportation Review, 2000, vol. 36, issue 3, 155-172 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. Date: 2000 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:transe:v:36:y:2000:i:3:p:155-172 Ordering information: This journal article can be ordered from Access Statistics for this article Transportation Research Part E: Logistics and Transportation Review is currently edited by W. Talley More articles in Transportation Research Part E: Logistics and Transportation Review from Elsevier |
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