An Intelligent Graph Neural Network Model for Road Accident Injury Severity Prediction

1K.V. Jhansi Rani,2M. Anusha Nagu,3K. Lakshmi Prasanna,4S. Chaitanya,5V. Dheeraj Krishna

doi:10.62643/

Authors

1K.V. Jhansi Rani,2M. Anusha Nagu,3K. Lakshmi Prasanna,4S. Chaitanya,5V. Dheeraj Krishna Author

DOI:

https://doi.org/10.62643/

Keywords:

Road Accident Analysis, Injury Severity Prediction, Graph Neural Networks (GNN), Deep Learning, Traffic Safety, Machine Learning, Accident Risk Assessment, Intelligent Transportation Systems, Data Mining, Predictive Modeling

Abstract

Road traffic accidents remain one of the leading causes of fatalities and severe injuries
worldwide, demanding accurate and timely injury severity prediction to improve emergency
response and resource allocation. Traditional statistical and machine learning models often
fail to capture the complex spatial, temporal, and relational dependencies in crash data. This
study proposes a Graph Neural Network (GNN) framework to predict road crash injury
severity by leveraging the interconnections between crash features, road networks, and
environmental factors. By modeling crash events as graph structures, the proposed system
enables better feature representation and improved prediction accuracy. The framework
integrates heterogeneous data sources—such as traffic conditions, weather, road topology,
and vehicle attributes—to provide actionable insights for transportation agencies and
emergency services