Research Trends in Road Safety (2013-2023): A bibliometric Review Using Science Mapping Techniques on Human and Technological Factors
Abstract
Introduction: Road safety remains a pressing global public health issue, marked by rising rates of traffic-related accidents, injuries, and fatalities. While various interventions have been implemented, there is still limited understanding of how human factors, trust in automated systems, and hazard perception influence safety outcomes in both autonomous and semi-autonomous driving contexts. This study aims to explore research trends and knowledge gaps in road safety using a bibliometric approach, focusing on the intersection between human behavioral factors and emerging intelligent transport technologies.
Methods: This study employs a bibliometric research design to analyze the scholarly literature on road safety and campaign-related studies, covering the period from 2013 to 2023 and using Scopus as the primary database.
Results: By applying science mapping techniques specifically bibliographic coupling and co-word analysis, a total of 581 peer-reviewed journal articles were examined. From these, 114 highly cited publications were identified and grouped into five thematic clusters based on bibliographic coupling and four keywords-based domains from co-words analysis. The results underscore the growing role of Advanced Driver Assistance Systems (ADAS), Connected Intelligent Transport Systems (C-ITS), and artificial intelligence (AI)-based risk prediction in reducing traffic incidents. Key areas of concern include behavioral adaptation, trust in automation, and situational awareness. Recommendations include enhancing regulatory frameworks, reinforcing helmet and speed limit compliance, and incorporating AI-powered predictive models and real-time monitoring systems into urban mobility planning. Additionally, the study emphasizes shifting from traditional media to targeted digital road safety campaigns through social media and mobile applications.
Conclusion: The findings underscore the importance of integrating AI-powered monitoring systems, enforcing data-informed traffic regulations, and implementing targeted behavioral campaigns to reduce accident risks. These strategies should be designed with adaptive models that account for how road users respond to risk and safety interventions. Future research should build interdisciplinary models that merge cognitive psychology, risk perception metrics, and intelligent transport technologies to guide data-driven safety innovations, providing valuable insights for urban planners, policymakers, and traffic safety educators.
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