MAPPING THE LANDSCAPE OF DRIVING BEHAVIOR AND ROAD SAFETY RESEARCH: A SCIENTOMETRIC PERSPECTIVE (2000–2025)

Abstract

This study conducts a scientometric analysis of global research output pertaining to driving behaviour and road safety from 2000 to 2025, with the objective of delineating thematic evolution, impactful contributions, and institutional productivity. Bibliographic metadata were extracted from the Scopus and Web of Science databases and subjected to analysis to examine publication trends, keyword co-occurrence networks, citation patterns, and research productivity metrics. A total of 2,211 publications were retained for analysis following pre-processing and duplicate removal procedures. The results demonstrate a discernible increase in scholarly output post-2010, with publication activity in Scopus increasing from fewer than 50 documents annually prior to 2010 to approximately 175 by 2025, indicate a substantial growth within the field. Journal articles constitute the predominant publication type, followed by conference papers and review articles, thereby underscoring the central role of peer-reviewed dissemination. Prominent publication venues include Accident Analysis & Prevention, Transportation Research Part F: Traffic Psychology and Behaviour, and Traffic Injury Prevention, while emerging trends are evident in technology-oriented sources. Keyword analysis reveals that established themes such as distraction, risk perception, and driving performance continue to exert influence on the field, alongside increasing emphasis on machine learning, autonomous driving, and decision making. Highly cited works by Anstey et al. (2005), Fuller (2005), and Ulleberg and Rundmo (2003) remain foundational to the intellectual structure of the domain. Institutional analysis identifies Tsinghua University, Tongji University, and Changan University as leading contributors, with augmented contributions from other Chinese, European, and Australian institutions. In summary, the findings suggest a maturing and increasingly interdisciplinary research landscape, characterized by enhanced integration of behavioral science, intelligent transportation systems, and data-driven safety modelling approaches.