Daily schedule changes in the automated vehicle era: Uncovering the heterogeneity behind the veil of low survey commitment. Supporting Data and Software (dataset)

Daily schedule changes in the automated vehicle era: Uncovering the heterogeneity behind the veil of low survey commitment. Supporting Data and Software

doi:10.4121/141522d5-84a8-4ec7-a362-d01ef2c75b7a.v3

The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/141522d5-84a8-4ec7-a362-d01ef2c75b7a

Datacite citation style:

Debbaghi, Fatima-Zahra; Kroesen, Maarten; De Vries, Gerdien; Pudane, Baiba (2024): Daily schedule changes in the automated vehicle era: Uncovering the heterogeneity behind the veil of low survey commitment. Supporting Data and Software. Version 3. 4TU.ResearchData. dataset. https://doi.org/10.4121/141522d5-84a8-4ec7-a362-d01ef2c75b7a.v3

Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

choose version:

version 3 - 2024-02-15 (latest)

version 2 - 2024-02-13 version 1 - 2023-03-31

usage stats

547

views

657

downloads

categories

keywords

activity schedules, automated vehicles, heterogeneity, latent class cluster model, multitasking, on-board activities, time use

geolocation

The Netherlands

time coverage

2019

licence

CC0

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by Fatima-Zahra Debbaghi , Maarten Kroesen , Gerdien De Vries

, Baiba Pudane

This dataset contains data from an interactive stated activity-travel survey aimed to investigate daily schedule changes of future automated vehicle users. The survey was conducted in the Netherlands, in July 2019. 509 respondents completed this survey.

The full survey can be found in A Day in the Life with an Automated Vehicle: Supporting Data and Software.

Complementing the first analysis available in the above dataset, the current package includes new analysis focused on the heterogeneity among the travellers regarding their expected changes in daily activity schedules with automated vehicles and on the link between the lack of expected changes and low commitment to survey. The analysis method is latent class cluster models, and it was performed in Latent Gold. See the README file for further details.

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