Artificial Intelligence and Data-Driven Approaches for Proactive Road Safety Analysis

Smart Traffic Flow Prediction Using Deep Learning: A Systematic Review of Recent Advances, Challenges, and Future Research Directions

Rapid urbanization, population growth, and the increasing number of vehicles have significantly intensified traffic congestion across metropolitan regions worldwide. Conventional traffic management systems are often inadequate for addressing the dynamic and nonlinear nature of urban transportation networks. Consequently, artificial intelligence (AI), particularly deep learning (DL), has emerged as a transformative approach for intelligent traffic flow prediction. Accurate traffic forecasting enables proactive traffic management, optimized route planning, reduced travel time, lower fuel consumption, and improved road safety, thereby contributing to the development of smart and sustainable cities. This systematic review critically examines recent advancements in deep learning-based traffic flow prediction models, emphasizing studies published between 2018 and 2026. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this review synthesizes findings from high-quality journal articles, conference proceedings, and technical reports indexed in Scopus and Web of Science. The paper evaluates major deep learning architectures, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Transformer-based architectures, and hybrid deep learning models. The review further analyzes their predictive performance, computational efficiency, scalability, interpretability, and real-world applicability in intelligent transportation systems. Existing challenges such as data heterogeneity, missing sensor data, privacy concerns, model explainability, computational cost, and deployment limitations are critically discussed. A conceptual research framework highlighting emerging technologies—including edge computing, Internet of Things (IoT), digital twins, federated learning, explainable AI, and large foundation models—is proposed to guide future research. The review contributes theoretically by synthesizing fragmented literature, technologically by identifying advanced predictive architectures, managerially by providing recommendations for transportation authorities, and sustainably by demonstrating how AI-driven traffic prediction supports greener and more efficient urban mobility. The findings indicate 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. Keywords: Smart Traffic Prediction; Deep Learning; Intelligent Transportation Systems; Graph Neural Networks; Traffic Forecasting; Explainable Artificial Intelligence; Smart Cities

Emerging Trends in Ai-Based Road Safety Systems: Challenges, Opportunities, And Future Research Directions

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.

INTEGRATING SITUATIONAL FACTORS AND HETEROGENEOUS DATA FOR ENHANCED TRAFFIC ACCIDENT ANALYSIS

Road traffic accidents (RTAs) pose a significant threat to public safety and transportation networks, requiring advanced modeling techniques to study their causes, trends, and consequences. The complexity of RTAs modeling stems from the need to integrate diverse data sources, including spatiotemporal changes, environmental variables, vehicle and driver characteristics, and infrastructure elements. Recent advances in machine learning, including graph neural networks, attention-based deep learning models, and hybrid tree classifiers, have improved prediction accuracy and interpretability. These methods leverage heterogeneous data to provide real-time crash prediction, severity assessment, and identification of problem areas. However, obstacles remain, such as data sparsity, class imbalance, and model interpretability. This article reviews modern approaches to RTAs modeling and identifies the impact of diverse information on crash frequency and severity estimates, thereby improving road safety, optimizing traffic management, and refining crash prevention approaches and technologies. Promising research directions in this area of modeling and machine learning are identified: integration of heterogeneous data, use of causal machine learning methods and real-time decision support systems.

Improving road safety through optimized and interpretable ensemble models of driver behavior

Survey of Artificial Intelligence Approaches for Traffic Accident Analysis, Prediction, And Prevention

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.

The role of Artificial Intelligence in improving construction site safety

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.