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Smart City Transportation Deep Learning Ensemble Approach for Traffic Accident Detection

 

Jangam Bhargavi1, Bharatha Ashwin2, Lavishetty Tagore3, MD Rehan4

Assistant Professor of Department of CSE(AI&ML) of ACE Engineering College 1 Students of Department CSE(AI&ML) of ACE Engineering College 2,3,4

 

Abstract
This project proposes a real-time traffic accident detection system for smart cities using a deep learning approach. The system aims to detect accidents like rear-end collisions, T-bone crashes, and frontal impacts by processing video sequences from traffic surveillance cameras. This system will utilize Convolutional Neural Networks (CNN) to analyze RGB frames and optical flow information helping to identify accidents more accurately. By automating the detection process the system seeks to minimize manual intervention, improve response times and provide real-time location visual data to emergency services. Key challenges include data imbalance and varying road conditions which the model will address through the use of specialized datasets. This system is designed to be computationally efficient and suitable for integration into smart city infrastructure. The project requires Python and Flask for development with future potential to enhance traffic management and accident response in urban environments.

Keyword: Real-Time Monitoring, Crash Detection, Deep Learning, Convolutionary Neural Networks (CNN), Traffic Monitoring, Emergency Response, Automated Accident Detection, Geospatial Analysis, Accident Patterns, Traffic Management, Video Processing, Flask Framework, Collision Detection, Traffic Flow Analysis, Smart Transportation, AI in Transportation, Computer Vision, Object Detection.

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