Daily schedule changes in the automated vehicle era: Supporting Data and Software

Supporting data and software for the paper 'Daily schedule changes in the automated vehicle era: Uncovering the heterogeneity behind the veil of low survey commitment'
Authors: F. Debbaghi, M.Kroesen, Gerdien de Vries, B. Pudane
MOBI— Electromobility Research Center, Vrije Universiteit Brussel
Email: fatima-zahra.debbaghi@vub.be

This dataset contains data from an interactive stated activity-travel survey aimed to investigate daily schedules of future automated vehicle users. 
The survey was conducted in the Netherlands, in July 2019. 509 respondents completed this survey.
This dataset also contains the analysis files supporting the paper. The full survey can be found in https://data.4tu.nl/articles/dataset/A_Day_in_the_Life_with_an_Automated_Vehicle_Supporting_Data_and_Software/14125880

The following information documents all the files available in the folder.

The folder includes an updated Excel summary of the survey responses 'Survey_data_AV_Time-use_Reduced.xlsx'
The main sheet 'Data' contains responses to all survey questions and a summarised version of the schedules. 
That is, the summary contains the total durations of each selected activity (which is differentiated between stationary and on-board locations), but it has no information on the clock-times of the activities.
The next sheet 'Codebook' contains the key for the encoded answers to multiple choice questions and the categorical socio-demographic data.
The last sheet 'Error count' summarises the cleaning of the 'Data' sheet. The cleaning corrected 6 main types of errors; however, we were conservative to label a situation as erroneous. 
An example of a corrected error is the following: if respondent selected the activity type 'Other', and then keyed in description of an existing activity type (e.g., 'watch Netflix'), then the type is changed to the existing one (e.g., 'Leisure').
The corresponding cells in the 'Data' sheet that were altered in the cleaning process, have an added comment with the error code. 
In addition to cleaning the data, 15 of the 509 responses were excluded based on three conditions: (1) no work or (2) no sleep activity in the data; (3) first (last) trip not starting (ending) at home; (4) extreme response time (steps 1 and 4 time).
These 15 rows are labelled in the Excel sheet, in the right-most columns of sheet 'Data'. 
We also merge some categories in the socio-demographic variables that had few responses. These include the following:
- Travel Mode: car drivers and car passengers are merged as simply car users 
- Travel Mode: pedestrians (very few) are merged with cyclists as active mode users
- Age: the oldest age group (very few) is merged with the second oldest


Instructions to define and run the 3 step model on Latent Gold are provided in 'Instructions for LatentGold Model.docx'.
The data file used in the model is 'Activities_Travel_Differences.sav', which includes the differences in the activity duration departure times for workbound and homebound trips, as well as the socio-demographics and other characteristics of the respondents. 
The definition of the first classification model on Latent Gold is in file 'Definition LatentGold Classification Model.lfg', while the definition of the 3-step model with covariates is in file 'Definition 3-step model.lfg'. These files can be opened and ran on Latent Gold without having to define the model from the beginning using the aforementioned sav file. 
The file 'Latent Gold Classification Model Results.html' contains the results of the classification model produced using LatentGold (with only indicators), while the file
'Latent Gold 3-Step Model Results.html' contains the final model with the covariates. 

