Calastri, C (2019) Travel, social networks and time use: modeling complex real-life behavior. In: Goulias, KG and Davis, AW, (eds.) Mapping the Travel Behavior Genome. Elsevier , pp. 279-297. ISBN 9780128173404
Abstract
This chapter encompasses the different themes and contributions presented in the Ph.D. thesis that was awarded the 2017 Eric Pas dissertation Prize at the 15th International Conference on Travel Behavior Research. Four main topics are discussed: performing choice modeling with semi-ubiquitous data, modeling decisions related to social networks, estimating and forecasting discrete-continuous choices and revealed preferences data collection. Results on how the incorporation of availability and consideration in mode choice models using GPS data can help obtaining meaningful results are discussed. The advanced models applied to different decisions related to social networks allow to reach conclusions that contribute to the current debate on the substitution of travel for social purposes with ICT. Most of the modeling of discrete-continuous choices is focused on the analysis of time use and on finding methods to allow for correlation across different activities and days of the week, especially in forecasting. These contributions exclusively make use of revealed preference datasets with repeated observations. The experience gained by the work in the thesis inspires the development of a unified data collection, gathering information about time use, social network and short-term and long-term travel behavior. The data collection protocol is not unique to travel behavior research and can be used in different fields.
Metadata
Item Type: | Book Section |
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Institution: | The University of Leeds |
Academic Units: | 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: | 11 Mar 2020 12:32 |
Last Modified: | 11 Mar 2020 12:32 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/C2018-0-02132-5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158200 |