Code and data underlying the publication: Towards developing socially compliant automated vehicles: Advances, expert insights, and a conceptual framework

DOI:10.4121/3a46e61c-f5f0-4399-a4b8-4d146b62a4f7.v4
The DOI displayed 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: 10.4121/3a46e61c-f5f0-4399-a4b8-4d146b62a4f7

Datacite citation style

Dong, Yongqi; van Arem, Bart; Haneen Farah (2025): Code and data underlying the publication: Towards developing socially compliant automated vehicles: Advances, expert insights, and a conceptual framework. Version 4. 4TU.ResearchData. dataset. https://doi.org/10.4121/3a46e61c-f5f0-4399-a4b8-4d146b62a4f7.v4
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

*** Code and data underlying the publication: Towards developing socially compliant automated vehicles: Advances, expert insights, and a conceptual framework ***


Authors: Dong, Y., van Arem, B., & Farah, H


Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology (TU Delft)


Corresponding author: Yongqi Dong

Contact Information: [email protected], [email protected]



***General Introduction***

This is the source code with processed data of the paper:

Dong, Y., van Arem, B., & Farah, H. (2025). Towards developing socially compliant automated vehicles: Advances, expert insights, and a conceptual framework (Under Review)



Data description:

The data involves online survey data. Unqualified responses and sensitive data related to job positions have been removed from the collected survey results. The processed data are presented in .CSV and .xlsx files. The provided data can generate the results by using the steps described above.


Code description: Codes are provided in .py files. To run the codes, Python 3.9 (or above) is needed, and relevant packages, e.g., pandas, need to be installed. The processed data files need to be put in the same folder as the code. The Python code can be run on both Windows and Linux systems but they were developed and tested on Windows 10.


Experiment design description: The questionnaire design and detailed questionnaire question lists are provided at https://lnkd.in/gpceU6gQ . The survey can be accessed at https://lnkd.in/evg6Dn9W.



How to use the codes and data to reproduce the figures:


(1) Put the code files and the processed data files in the same folder and simply run the Python code.

(2) For the Sankeymatic diagram to generate Fig. 3 and Fig. 4, one needs to go to https://sankeymatic.com/build/ and copy the relevant source codes in Sankeymatic_data&codesource_Fig3_MethodsUsed.txt and Sankeymatic_data&codesource_Fig4_UseCase.txt, and then simply run and show the plots.

(3) Fig. 7-13 could be generated using the “.xlsx” files in the folder of [TemProcessedDataPlots].

(4) Set up requirements for Python

Python 3.9 (or above)

Relevant packages e.g., pandas.


All relevant figures are also provided in high resolution in the folder of [Figures].


History

  • 2025-04-01 first online
  • 2025-04-15 published, posted

Publisher

4TU.ResearchData

Format

csv, xlsx, svg, png, py, txt

Funding

  • Safe and efficient operation of AutoMated and human drivEN vehicles in mixed traffic (grant code 17187) [more info...] Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO)

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning

DATA

Files (2)