Real-Time Traffic Accident Detection Using an Enhanced Deep Learning Ensemble Model
TL;DR
The proposed model addresses challenges such as limited training data and different road conditions, helping improve its reliability in practical situations, and 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.
Abstract
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.