Railway safety research: Mapping trends, strategic clusters, and future pathways

Fuqaha, Sameh

Journal of Railway Transportation and Technology
Vol. 4 No. 1 (2025): March

This study presents a comprehensive bibliometric analysis of railway safety research from 2019 to 2024, offering critical insights into the thematic evolution, intellectual structure, and future pathways in this vital domain. By analyzing 445 peer-reviewed articles retrieved from leading academic databases, the study identifies major research clusters centered around risk assessment, human factors, and AI-enabled infrastructure monitoring. The findings reveal a significant shift toward intelligent safety systems, with deep learning, predictive maintenance, and human reliability modeling emerging as dominant themes. China, the United Kingdom, and India are identified as leading contributors, with strong international collaboration driving innovation in the field. Despite notable progress, the analysis uncovers persistent gaps—particularly in cybersecurity resilience, cognitive integration in risk assessment, infrastructure adaptation to climate risks, and localization technologies for autonomous train systems. Future research directions are proposed to address these gaps, including multi-sensor fusion for train positioning, AI-based decision-making frameworks for autonomous operations, and integration of human factors into machine learning-based safety evaluations.