[Supporting Data and Software] The perception of COVID-19 infection risk in Long-distance travel

doi:10.4121/8a77dbc7-cde0-4c5d-9763-53823af947a4.v1
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: 10.4121/8a77dbc7-cde0-4c5d-9763-53823af947a4
Datacite citation style:
Geržinič, Nejc; van Dalen, Maurizio; Donners, Barth; Cats, Oded (2024): [Supporting Data and Software] The perception of COVID-19 infection risk in Long-distance travel. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/8a77dbc7-cde0-4c5d-9763-53823af947a4.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

The files included below are part of a research on passengers' perception of infection risk with COVID-19 and its relation to long-distance (international) travel.

 

Data was collected through a Hierarchical Information Integration (HII) approach, where respondents were first asked to rate their perceived risk of infection, based on various safety measures. This subjective risk was then included in a mode choice experiment for long-distance trips, where respondents could choose between car, train and aircraft.


Information on the data and model can be found in the README file and the python script below.

history
  • 2024-10-28 first online, published, posted
publisher
4TU.ResearchData
format
*.py, *.html,*.csv,*.docx
organizations
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Smart Public Transport Lab

DATA

files (4)