Calastri, C, Hess, S orcid.org/0000-0002-3650-2518, Daly, A et al. (1 more author) (2017) Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary data. Transportation Research, Part A: Policy and Practice, 104. pp. 1-20. ISSN 0965-8564
Abstract
An understanding of activity choices and duration is a key requirement for better policy making, in transport and beyond. Previous studies have failed to make the important link with individuals' social context. In this paper, the Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) model is applied to the choice of activity type and duration over the course of two days, using data from the Chilean city of Concepcion. In common with other studies, heterogeneity across decision makers is accommodated in the model by analysing the impact of different sociodemographic, mobility and residential location variables on both the activity choice and the time allocation decision. In addition, different social network and social capital measures are found to be signi cantly correlated with the choice and duration of different activities, and we show how these relationships seem to differ from the effects of socio-demographic variables. Finally, we perform a forecasting exercise using the MDCNEV model, highlighting the differences in substitution patterns from a standard MDCEV model.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017, Elsevier. This is an author produced version of a paper published in Transportation Research Part A: Policy and Practice. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | MDCNEV; activity modelling; social networks; travel behaviour |
Dates: |
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Institution: | The University of Leeds |
Funding Information: | Funder Grant number EU - European Union 615596 |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Jul 2017 10:50 |
Last Modified: | 31 Jul 2018 00:39 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.tra.2017.07.003 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:118984 |