Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability

Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability

Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. The proposed model leverages residual learning to effectively model intricate relationships between numerical a...

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Bibliographic Details
Main Authors: Ilyass Benfaress, Afaf Bouhoute, Ahmed Zinedine
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:AI
Subjects:
traffic accident severity
explainable Artificial Intelligence
ResNet architecture
SHAP
Machine Learning
deep learning
Online Access:https://www.mdpi.com/2673-2688/5/4/124
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