Research on the Public’s Intention to Use Shared Autonomous Vehicles: Based on Social Media Data Mining and Questionnaire Survey

Yang Liao, Hanying Guo () and Hongguo Shi
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Yang Liao: Department of Automobile and Transportation, Xihua University, Chengdu 610039, China
Hanying Guo: Department of Automobile and Transportation, Xihua University, Chengdu 610039, China
Hongguo Shi: Department of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China

Sustainability, 2024, vol. 16, issue 11, 1-25

Abstract: While the emergence of shared autonomous vehicles can be an effective solution to improve transport issues and achieve sustainable development, the benefits associated with shared autonomous vehicles can only be realized when the public intends to use them. Therefore, it is necessary to conduct an in-depth study on the public’s intention to use shared autonomous vehicles and identify the key influencing factors. This study mined social media data to obtain real public perceptions. A qualitative exploratory analysis was used to identify thematic variables regarding social media data on shared autonomous vehicles, from which a research model of the public’s intention to use SAVs was proposed. Then, a questionnaire survey was conducted, and the structural equation model and Bayesian network were used to analyze the questionnaire data quantitatively. The findings reveal how perceived risk, social information, trust, perceived usefulness, and personality traits affect the public’s intention to use shared autonomous vehicles, and how to enhance the public’s intention to use them. This study will enrich the research on traveler psychology in the context of intelligent travel and provide theoretical basis and decision support for future policies to promote shared autonomous vehicles.

Keywords: shared autonomous vehicle; sustainable development; intention to use; data mining; structural equation model; Bayesian network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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