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Apollo Go Adoption Survey: Public Perceptions of Driverless Vehicles in Wuhan, China (2025)

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Citation Author(s):
XIAOYU ZHANG (zhangxiaoyu@kangwon.ac.kr )
Submitted by:
XIAOYU ZHANG
Last updated:
DOI:
10.21227/k26c-jm66
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Categories:
Keywords:
autonomous vehicles
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Abstract

This dataset contains responses from a structured questionnaire survey conducted in Wuhan, China, from February to April 2025, targeting public behavioral intentions toward the adoption of driverless vehicles (DVs). A total of 515 valid responses were collected from urban residents through online distribution channels. The questionnaire was designed based on an integrated theoretical framework combining the Unified Theory of Acceptance and Use of Technology (UTAUT), the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and Perceived Risk Theory. It includes items measuring perceived usefulness, perceived risk, attitude, environmental and policy factors, and behavioral intention. The dataset provides valuable empirical evidence from a non-Western megacity, supporting studies in smart mobility, technology adoption, urban transport policy, and environmental governance.

Instructions:

This dataset is provided to facilitate research on the behavioral adoption of driverless vehicles (DVs), with a focus on environmental and policy influences. It may be used for academic, educational, or non-commercial research purposes only.

Suggested Uses:

  • Structural Equation Modeling (SEM) analysis using software such as AMOS, LISREL, SmartPLS, or R (e.g., lavaan package).
  • Multivariate statistical analysis (e.g., CFA, regression, path analysis) in SPSS, Stata, or R.
  • Comparative studies between countries or regions on autonomous vehicle acceptance.
  • Policy simulation or scenario modeling in transportation planning or sustainability studies.

Contents:

  • Raw dataset in .csv and .xlsx formats.
  • Codebook including variable names, descriptions, scales, and measurement sources.
  • Questionnaire instrument used in data collection (English version).
  • A brief guide on how to prepare the data for SEM input.

Citation Requirement: If you use this dataset in your publication, please cite the original article:
Zhang, X., Tong, L., & Chen, M. (2025). Toward Safer and Greener Urban Mobility: Environmental and Policy Factors in Driverless Vehicle Adoption in Wuhan, China.

Contact: For questions regarding dataset usage, contact the corresponding author at: muwi@kangwon.ac.kr

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