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.

Published:

06-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1573-1578

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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