Dumont, J, Giergiczny, M and Hess, S (2015) Individual level models vs. sample level models: contrasts and mutual benefits. Transportmetrica A: Transport Science. ISSN 2324-9935
Abstract
With a view to better capturing heterogeneity across decision makers and improving prediction of choices, there is increasing interest in estimating separate models for each person. Almost exclusively, this work has however taken place outside the field of transport research. The aim of the present paper is twofold. We first wish to give an account of the potential benefits of a greater focus on individual level estimates in transport applications. Secondly, we wish to offer further insights into the relative benefits of sample level and individual level models (ILM) by drawing on a dataset containing an unusually large number (144) of decisions on holiday travel per individual. In addition to comparing existing approaches, we also put forward the use of a novel technique which draws on the relative benefits of both sample level and individual level models by estimating ILMs in a Bayesian fashion with priors drawn from a sample level model. Our results show only limited differences between ILMs and conditionals from sample level models when working with the full set of choices. When working with more realistic sample sizes at the person level, our results suggest that ILMs can offer better performance on the estimation data but that this is a result of overfitting which can lead to inferior prediction performance. Our proposed Bayesian ILM model offers good intermediary performance. The use of best-worst data rather than simple stated choice, as is done commonly in published ILM work, does not lead to major changes to these findings.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2015 Hong Kong Society for Transportation Studies Limited. This is an Accepted Manuscript of an article published by Taylor & Francis in Transportmetrica A: Transport Science, on 16/03/15, available online: http://wwww.tandfonline.com/10.1080/23249935.2015.1018681 |
Keywords: | Individual level models; hierarchical Bayes; heterogeneity; prediction |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Mar 2015 10:10 |
Last Modified: | 15 Mar 2016 13:50 |
Published Version: | http://dx.doi.org/10.1080/23249935.2015.1018681 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/23249935.2015.1018681 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:83861 |