Paper Contents
Abstract
Intelligent transportation systems are critical to assuring safety and efficiency in the ever-changing terrain of smart city development. This research provides a powerful ensemble-based deep learning system for detecting traffic accidents utilizing historical and contextual data. The approach improves prediction accuracy by combining classic machine learning algorithms and neural networks. The system is designed as a web-based application with real-time user input, allowing for quick analysis and prediction. The model provides accurate accident detection results by using a variety of features such as weather, location, and time. This method demonstrates the feasibility of proactive traffic management and emergency response in smart urban areas.
Copyright
Copyright © 2025 K Madhu Sudhan Reddy. This is an open access article distributed under the Creative Commons Attribution License.
