This systematic review analyzes 21 peer-reviewed articles (2021–2025) from ScienceDirect, Elsevier, and IEEE Xplore to examine methodological advances in road safety research. Findings reveal a paradigm shift from retrospective crash analysis to proactive, data-driven approaches, with machine learning (ML) and deep learning (DL)—particularly ensemble methods such as Random Forest, XGBoost, and neural networks—achieving crash detection accuracies of 85–92%. Explainable AI (XAI) frameworks, especially SHAP, enhance model interpretability, while hybrid and ensemble models improve predictive stability. Real-time monitoring via IoT sensors, connected vehicles, and computer vision enables surrogate safety evaluations using conflict-based metrics. Despite these advances, challenges remain regarding data heterogeneity, model transferability, privacy, and computational demands. Future directions include integrating autonomous vehicles, implementing standardized data-sharing platforms, and deploying automated safety countermeasures to transition from prediction to proactive prevention.
This systematic review critically examines recent advancements in deep learning-based traffic flow prediction models, emphasizing studies published between 2018 and 2026 and indicates that although graph-based and transformer-based architectures currently achieve state-of-the-art predictive performance, integrating explainability, real-time adaptation, and privacy-preserving learning remains a significant research priority.
Dr. P. K. S. Bhadauria· International Journal of Cre...· 0 citations
Traffic accidents are still a major threat to public safety, causing a lot of deaths, damage to property, and problems in society around the world. Intelligent, data-driven, and proactive traffic management systems have been made possible by the fast development of AI and ML, which has revolutionized traditional road safety practice. This paper presents a comprehensive review of recent AI-based approaches for enhancing road safety, with emphasis on accident prediction, driver behavior analysis, and connected vehicle technologies. In addition, the review examines the major factors contributing to road accidents and discusses the Safe System approach as a framework for improving transportation safety. Current challenges, including data quality, model interpretability, cybersecurity, privacy preservation, and regulatory constraints, are critically analyzed to highlight existing research limitations. Furthermore, emerging research directions explored as potential solutions for developing robust, scalable, and trustworthy intelligent transportation systems. The findings indicate that AI-driven technologies have considerable potential to improve accident prevention, traffic efficiency, and decision-making while supporting the development of safer and more sustainable road transportation systems.
D. Upadhyay· International Research Journ...· 0 citations
The impact of diverse information on crash frequency and severity estimates is identified, thereby improving road safety, optimizing traffic management, and refining crash prevention approaches and technologies.
M. Uthaib, V. Tyutyunnik· NATURAL AND MAN-MADE RISKS (...· 0 citations
These findings demonstrate that RF–Bayesian provides a stable, interpretable, and computationally efficient framework for smartphone-based driver behavior classification, with practical relevance for telematics, fleet safety management, driver feedback systems, and intelligent transportation safety applications.
A.A. Al-Rababah, S. M. Rahman· Neural computing & applicati...· 0 citations
A comprehensive survey of AI-based approaches for traffic accident analysis, covering traditional statistical methods, Machine Learning (ML), Deep Learning (DL), computer vision, and Intelligent Transportation Systems (ITS).
Dr.Jvalant Kumar Kanaiyalal Patel· International Journal of Adv...· 0 citations
Artificial intelligence, when responsibly implemented, represents a transformative adjunct to traditional safety practices – capable of significantly improving construction site safety performance globally – but it must be deployed in tandem with organizational commitment, worker training, and robust safety cultures.
Musaed M. Al-Thubaiti, Saeed S. Al-Shahrani, Ryan A. Alsaihaty· World Journal of Advanced En...· 0 citations