Predicting Traffic Accident Severity Using a CNN-Based Deep Learning Framework

Miss. Seema Y. Somawanshi.; Prof. Shah H.P

Predicting Traffic Accident Severity Using a CNN-Based Deep Learning Framework

Authors: Miss. Seema Y. Somawanshi., Prof. Shah H.P

Unique Paper ID: 178816
Volume: 11
Issue: 12
PageNo: 6744-6751

Keywords: Traffic Accident Severity Convolutional Neural Networks (CNNs) Deep Learning Predictive Modeling Image Analysis Sensor Data Traffic Safety Accident Prediction etc.

Abstract:
Predicting the severity of traffic accidents is essential for enhancing road safety and enabling timely emergency response. In this research, we propose a Convolutional Neural Network (CNN)-based deep learning model for classifying the severity of road traffic accidents using visual and structured data. The model leverages the spatial learning capabilities of CNNs to extract meaningful patterns from accident-related images and metadata. We train and evaluate multiple CNN architectures, including [mention specific architectures like VGG16, ResNet50, or a custom CNN], on a curated dataset containing annotated accident severity levels. Our experimental results demonstrate that the proposed model achieves high accuracy and generalization in distinguishing between different severity levels, outperforming traditional machine learning baselines. Additionally, we explore the impact of data augmentation, preprocessing techniques, and hyperparameter tuning on model performance. The study further discusses the challenges of real-world deployment, such as data imbalance, noise, and generalizability across regions. Our findings highlight the potential of deep learning in automating accident severity assessment and lay the groundwork for future advancements in intelligent transportation systems.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 6744-6751

Predicting Traffic Accident Severity Using a CNN-Based Deep Learning Framework

Available:https://ijirt.org/Article?manuscript=178816

03 Dec, 2024

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Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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