Calastri, C, Hess, S orcid.org/0000-0002-3650-2518, Pinjari, AR et al. (1 more author) (2020) Accommodating correlation across days in multiple discrete-continuous models for time use. Transportmetrica B: Transport Dynamics, 8 (1). pp. 108-128. ISSN 2168-0566
Abstract
The MDCEV modelling framework has established itself as the preferred method for modelling time allocation, with data very often collected through travel or activity diaries. However, standard implementations fail to recognise the fact that many of these datasets contain information on multiple days for the same individual, with possible correlations and substitution between days. This paper discusses how the theoretical accommodation of these effects is not straightforward, especially with budget constraints at the day and multi-day level. We rely on additive utility functions where we accommodate correlation between activities at the within-day and between-day level using a mixed MDCEV model, with multivariate random distributions. We illustrate our approach using a well-known time use datasets, confirming our theoretical points and highlighting the benefits of allowing for correlation across days in terms of model fit and behavioural insights.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2020 Crown Copyright. This is an author produced version of a paper published in Transportmetrica B: Transport Dynamics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | MDCEV, activity modelling, multi-day, time use |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Choice Modelling The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Economics and Discrete Choice (Leeds) |
Funding Information: | Funder Grant number EU - European Union 615596 |
Depositing User: | Symplectic Publications |
Date Deposited: | 27 Jan 2020 12:07 |
Last Modified: | 09 Feb 2021 01:38 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/21680566.2020.1721379 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156047 |