Advancing Sustainable Urban Mobility: Public Acceptance and Perceived Risks of Autonomous Vehicle Deployment in Dubai

Background: Understanding public acceptance of autonomous vehicles (AVs) is essential for cities transitioning toward smart mobility systems. Dubai aims to transform 25% of trips to autonomous mode by year 2030, yet little is known about residents’ readiness. Methods: An online survey (N = 302; 2024/2025) measured awareness, perceived benefits/risks, trust, cybersecurity concerns, and behavioral intention (BI). Constructs were analyzed using descriptive statistics and regression. Results: Cybersecurity concern was the strongest negative predictor of BI, while perceived usefulness (accident reduction) showed a weak, marginal positive effect. Gender, age, and cost effects were not statistically significant. Conclusions: Public acceptance is shaped more by trust, safety perception, and perceived system reliability than by demographics or cost. Policy actions should focus on transparent regulation, cybersecurity audits, and public AV pilots.

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Publication Details

Journal
Sustainability
Published
2025-12-09
DOI
https://doi.org/10.3390/su172411021
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

Advancing Sustainable Urban Mobility: Public Acceptance and Perceived Risks of Autonomous Vehicle Deployment in Dubai

Ismail Zohdy, Dalia Hafiz, Qing Hou
Sustainability
Human-Automation Interaction and Safety
article

Advancing Sustainable Urban Mobility: Public Acceptance and Perceived Risks of Autonomous Vehicle Deployment in Dubai

Ismail Zohdy, Dalia Hafiz, Qing Hou
article en

Abstract

Background: Understanding public acceptance of autonomous vehicles (AVs) is essential for cities transitioning toward smart mobility systems. Dubai aims to transform 25% of trips to autonomous mode by year 2030, yet little is known about residents’ readiness. Methods: An online survey (N = 302; 2024/2025) measured awareness, perceived benefits/risks, trust, cybersecurity concerns, and behavioral intention (BI). Constructs were analyzed using descriptive statistics and regression. Results: Cybersecurity concern was the strongest negative predictor of BI, while perceived usefulness (accident reduction) showed a weak, marginal positive effect. Gender, age, and cost effects were not statistically significant. Conclusions: Public acceptance is shaped more by trust, safety perception, and perceived system reliability than by demographics or cost. Policy actions should focus on transparent regulation, cybersecurity audits, and public AV pilots.

SustainabilityVol. 17(24)
Dubai Health Authority (AE), Ajman University (AE), University of Florida (US)
Openalex Percentile: Top 56%
Human-Automation Interaction and Safety
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