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<p>Rapid urbanization is intensifying transportation demand in cities, leading to a projected fivefold increase in urban vehicles by 2050 and a significant escalation in carbon dioxide emissions and environmental impacts. Although clean energy and smart city initiatives have been prioritized to address these challenges, the effective integration of artificial intelligence (AI) into clean transportation systems (CTS) remains insufficiently understood. To address this gap, this study systematically examines the application of AI in clean transportation and identifies dominant research themes, theoretical foundations, and emerging trends. A comprehensive bibliometric and systematic analysis was conducted using the PRISMA framework to identify peer-reviewed articles published between 2015 and 2025 from the Scopus database. Bibliometric mapping and keyword co-occurrence analyses were performed using VOSviewer and Biblioshiny to map research evolution, influential contributions, thematic clusters, as well as the intellectual structure, thematic evolution, and geographic distribution of the literature. The results reveal four dominant thematic clusters—intelligent systems and autonomous vehicles, electric transportation and renewable energy, sustainable urban mobility, and AI-driven data analytics—highlighting the central role of AI in improving energy efficiency, reducing emissions, and optimizing transportation performance. Additionally, the analysis identifies critical research gaps related to social equity, system-level integration, adaptive learning under dynamic conditions, governance, and cybersecurity.</p>

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Applications of artificial intelligence (AI) in clean transportation: an in-depth bibliometric analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Seyedeh Zahra Hosseini,
  • Parizad Saghari Chiha,
  • Zahra Sadat Saeideh Zarabadi,
  • Samane Moghadam

摘要

Rapid urbanization is intensifying transportation demand in cities, leading to a projected fivefold increase in urban vehicles by 2050 and a significant escalation in carbon dioxide emissions and environmental impacts. Although clean energy and smart city initiatives have been prioritized to address these challenges, the effective integration of artificial intelligence (AI) into clean transportation systems (CTS) remains insufficiently understood. To address this gap, this study systematically examines the application of AI in clean transportation and identifies dominant research themes, theoretical foundations, and emerging trends. A comprehensive bibliometric and systematic analysis was conducted using the PRISMA framework to identify peer-reviewed articles published between 2015 and 2025 from the Scopus database. Bibliometric mapping and keyword co-occurrence analyses were performed using VOSviewer and Biblioshiny to map research evolution, influential contributions, thematic clusters, as well as the intellectual structure, thematic evolution, and geographic distribution of the literature. The results reveal four dominant thematic clusters—intelligent systems and autonomous vehicles, electric transportation and renewable energy, sustainable urban mobility, and AI-driven data analytics—highlighting the central role of AI in improving energy efficiency, reducing emissions, and optimizing transportation performance. Additionally, the analysis identifies critical research gaps related to social equity, system-level integration, adaptive learning under dynamic conditions, governance, and cybersecurity.