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

Artificial Intelligence Models for Smart Traffic Management and Road Safety

High traffic volume, urbanization and car ownership have exacerbated traffic congestion, travel time and road accidents; these are some of the problems facing modern transportation systems. Artificial Intelligence (AI) has become a viable solution, allowing intelligent, adaptive and data-informed traffic management. This paper provides an overview of the most prominent types of AI models employed in smart traffic control and road safety, such as supervised and unsupervised machine learning, deep learning, computer vision, and reinforcement learning. It discusses their applications for traffic flow prediction, adaptive traffic signal control, vehicle and pedestrian detection, accident prediction, driver behaviour monitoring, and prioritisation of emergency vehicles. The research also covers the features of Vehicle-to-Everything (V2X) communication, connected vehicles, the Internet of Things (IoT), and Intelligent Transportation Systems (ITS), as well as their potential for enhancing transportation efficiency and road safety. In addition, the paper points out potential roadblocks for the implementation of AI such as data quality, computational complexity, cybersecurity, privacy, infrastructure cost, and model interpretability. Finally, future research directions are outlined, highlighting explainable AI, generative AI, digital twins and integration of intelligent transportation systems in smart cities for sustainable cities. Overall, the review shows that AI can revolutionize traditional transportation systems, turning them into intelligent networks that can help alleviate congestion, lower the risk of accidents, optimize traffic flow, and facilitate safer and more sustainable urban mobility.

Artificial Intelligence for Smart Traffic Monitoring and Road Accident Prevention

The findings show that AI technologies, such as smart traffic signals, real-time monitoring, and data analysis, can improve traffic flow and help reduce accidents.

Real-Time Traffic Accident Detection Using an Enhanced Deep Learning Ensemble Model

Smart city transportation has become an important part of improving road safety and traffic management. This project focuses on detecting traffic accidents using a deep learning ensemble approach that combines I3DConvLSTM2D with RGB and optical flow information. By analyzing both the appearance of vehicles and their movement, the system can identify accident events more accurately than traditional methods. It is designed to work in real time, making it suitable for surveillance cameras and smart city environments. The proposed model also addresses challenges such as limited training data and different road conditions, helping improve its reliability in practical situations. Early accident detection allows emergency services to respond quickly, reducing the impact of road accidents and improving public safety. Overall, this system demonstrates how artificial intelligence can support modern transportation by providing faster, more accurate, and efficient accident detection while contributing to safer roads and better traffic monitoring.

Prediction of Highway Multi‑Accident Sections Based on Machine Learning

INTEGRATING SITUATIONAL FACTORS AND HETEROGENEOUS DATA FOR ENHANCED TRAFFIC ACCIDENT ANALYSIS

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.

Intelligent Traffic Accident Detection Using Computer Vision

Rapid urbanization and increasing vehicle numbers have led to a significant rise in road accidents, posing serious threats to human life and economic stability. Traditional accident detection systems rely on manual reporting, causing delays in emergency response. To address this, computer vision integrated with intelligent transportation systems offers an effective solution. This study analyzes pre-2018 approaches to traffic accident detection using video surveillance. The proposed system automatically detects accidents by analyzing traffic camera footage through feature extraction, motion analysis, and pattern recognition. It identifies abnormal vehicle behavior such as sudden speed drops, collisions, and irregular trajectories. Classical computer vision techniques like optical flow, background subtraction, edge detection, and machine learning methods such as Support Vector Machines (SVM) and decision trees are used. The system includes preprocessing, object detection, trajectory tracking, and classification of normal and abnormal events, supported by mathematical modeling and threshold-based decisions. Results show high detection accuracy with low false alarms, especially in controlled environments like highways and intersections. However, challenges remain in real-time implementation due to lighting, occlusion, and camera angle issues. Future work suggests incorporating deep learning and multi-sensor data fusion to improve performance. In conclusion, computer vision-based accident detection is a promising approach to enhancing road safety and reducing response time, contributing to the advancement of smart transportation systems.