Jul 2026· International Research Journal of Advanced Engineering and Technology· 0 citations
Abstract
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.
The rapid advancements in intelligent mobility, linked cars, and autonomous transportation systems have elevated transportation safety to the forefront of research priorities. When it comes to monitoring, decision-making, and road safety, traditional transport is being utterly transformed by technologies like AI, ML, the Internet of Things (IoT), V2X communications, edge computing, and autonomous cars. The present review provides an extensive overview of the key concepts of transportation safety, such as VRUs, camera-based perception, AI detection and recognition, tracking and trajectory prediction techniques. In addition, the state-of-the-art in the areas of IoT enabled transportation systems, intelligent vehicles and V2X communication frameworks is discussed to explore how these innovations enable cooperative and connected mobility. Besides that, the main challenges faced by the autonomous vehicles, especially safety and reliability issues, legal and moral concerns, infrastructure issues and human-machine interaction are considered. Moreover, the recent literature in the area of emerging transportation technologies is reviewed in order to examine the most recent trends, advancements and open problems. This analysis demonstrates how critical it is to build a trustworthy transportation ecosystem by integrating intelligent communication networks, artificial intelligence (AI), and edge computing.
Dharmendra Jain· International Journal of Nex...· 0 citations
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.
A multi-layered safety model that integrates emerging technologies with human-centred practices, emphasising resilience engineering, adaptive training, transparent AI governance, and continuous learning across transportation ecosystems is proposed, arguing that technological innovation must be framed not as a replacement for human expertise but as an enabler of enhanced human performance.
Road traffic accidents are a major public safety concern, causing substantial fatalities, injuries, and economic losses every year. The increasing availability of traffic data and advances in Artificial Intelligence (AI) have accelerated the development of intelligent techniques for traffic accident analysis, prediction, and prevention. This paper presents 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). The survey discusses the role of AI in accident risk prediction, smart traffic monitoring, and intelligent transportation infrastructure, along with conventional accident prevention strategies. Furthermore, recent literature is systematically reviewed to compare commonly used algorithms, application domains, advantages, and existing limitations. The paper also examines key enabling technologies, including IoT, connected vehicles, GPS, wireless communication, and traffic surveillance systems, that support real-time accident prevention. Finally, current research challenges and emerging trends are discussed to provide insights into future intelligent traffic accident prevention systems. This survey provides a structured overview of current research and highlights emerging directions for developing more accurate, explainable, and scalable AI-driven traffic accident prevention systems.
Dr.Jvalant Kumar Kanaiyalal Patel· International Journal of Adv...· 0 citations
Road traffic collisions are a serious public safety issue that continues to cause high rates of death, injury and economic losses around the world. Reporting systems for accidents are frequently inefficient and slow, especially in low-traffic regions and remote areas, which can delay the response to an accident. With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), computer vision and intelligent transportation systems, automatic crash detection systems have been developed that have ability to monitor in real-time and report the crash quickly. A comprehensive review of the various IoT and AI-based crash detection technologies is provided including the sensing hardware, communication infrastructure, cloud and fog computing, computer vision and machine learning techniques. It reviews the latest studies on using accelerometers, GPS, surveillance cameras, CNN and YOLO-based models to detect accidents, and offers a comparative assessment of their techniques, advantages, obstacles, and suggestions. There are several research gaps identified in the review, such as limited accident severity assessment, limited multimodal sensor fusion, and scalability issues; interoperability problems, privacy concerns, and real-world validation problems in different traffic environments. Based on these findings, future research directions for the paper are discussed, focusing on Explainable AI, Real-time inference at the edge, Secure IoT communication and Scalable intelligent transportation frameworks. The study illustrates how the synergy between AI technologies and IoT-enabled sensing and communication capabilities can significantly improve the capabilities of accident detection, reduce emergency response times and contribute to overall road safety.
D. Upadhyay· International Research Journ...· 0 citations
: The rapid increase in vehicle numbers and resulting traffic congestion have amplified critical challenges related to safety, environmental impact, and transportation efficiency. Road accidents account for approximately 1.19 million deaths annually, with an additional 20 to 50 million people injured. Moreover, congestion leads to the loss of nearly 50 billion hours and around 3 billion gallons of fuel each year. These pressing issues necessitate innovative and integrated solutions that can enhance the overall performance of the road. This study investigates the integration of Emerging Computing Technologies (ECT) into smart road infrastructures as a potential response to these challenges. It explores key enabling technologies (e.g., advanced communication systems, the Internet of Things (IoT), sensors, Artificial Intelligence (AI), big data, blockchain, and energy harvesting) for their capacity to support intelligent transportation systems. The paper further reviews real-world applications of smart roads, such as smart traffic lights, automated road maintenance, intelligent lighting, and energy-efficient infrastructure, illustrating their contribution to sustainable urban mobility. In parallel, it examines challenges impeding widespread adoption, including technical limitations, financial and regulatory constraints, cybersecurity risks, and societal acceptance. Finally, the study highlights future opportunities by discussing ongoing advancements in ECT and renewable energy integration, outlining their potential to revolutionize road network efficiency, safety, and sustainability.
Afzal Badshah, Ali Daud, S. Arafat et al.· Computers, Materials & C...· 0 citations