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

*** Code and data underlying the publication: Towards developing socially compliant automated vehicles: Advances, expert insights, and a conceptual framework ***<br>Authors: Dong, Y., van Arem, B., &amp; Farah, H<br>Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology (TU Delft)<br>Corresponding author: Yongqi DongContact Information: [e-mail], [e-mail]<br><br>***General Introduction***This is the source code with processed data of the paper:Dong, Y., van Arem, B., &amp; Farah, H. (2025). Towards developing socially compliant automated vehicles: Advances, expert insights, and a conceptual framework (Under Review)<br><br>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.<br>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.<br>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.<br><br>How to use the codes and data to reproduce the figures:<br>(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&amp;codesource_Fig3_MethodsUsed.txt and Sankeymatic_data&amp;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 PythonPython 3.9 (or above)Relevant packages e.g., pandas.<br>All relevant figures are also provided in high resolution in the folder of [Figures].<br>

*** 本数据集对应已投稿待审论文：《面向符合社会规范的自动驾驶汽车研发：进展、专家见解与概念框架》***
作者：Dong, Y.、van Arem, B. 与 Farah, H.
单位：代尔夫特理工大学（TU Delft）土木与地球科学学院交通与规划系
通讯作者：董永琦
联系方式：[e-mail]、[e-mail]

*** 通用引言 ***
本数据集包含该论文的源代码与处理后的数据：Dong, Y., van Arem, B., & Farah, H. (2025). Towards developing socially compliant automated vehicles: Advances, expert insights, and a conceptual framework (Under Review)

数据说明：本数据集涵盖在线调研数据。已从收集的调研结果中剔除不合格应答及与职位相关的敏感数据。处理后的数据以CSV与XLSX格式文件提供。按照前文所述步骤运行代码，即可基于本数据集生成对应研究结果。

代码说明：本数据集提供的代码均为.py格式的Python脚本文件。运行代码需使用Python 3.9及以上版本，并安装pandas等相关依赖包。需将处理后的数据文件与代码文件置于同一文件夹下。本Python代码可在Windows与Linux系统上运行，但其开发与测试环境均为Windows 10。

实验设计说明：问卷设计方案及详细题目清单可通过https://lnkd.in/gpceU6gQ 获取；调研问卷可通过https://lnkd.in/evg6Dn9W 访问。

如何通过代码与数据复现研究图表：
(1) 将代码文件与处理后的数据文件置于同一文件夹，直接运行Python代码即可。
(2) 若需生成图3与图4的Sankeymatic桑基图，需访问https://sankeymatic.com/build/ ，复制Sankeymatic_data&codesource_Fig3_MethodsUsed.txt与Sankeymatic_data&codesource_Fig4_UseCase.txt中的相关源代码，随后运行即可生成并展示对应图表。
(3) 图7至图13可通过[TemProcessedDataPlots]文件夹内的XLSX格式文件生成。
(4) Python环境配置要求：Python 3.9及以上版本；需安装pandas等相关依赖包。
所有相关研究图表均已以高分辨率格式存储于[Figures]文件夹中。