Road crash injury severity prediction using a graph neural network framework
Crash severity prediction is a challenging research area, where the objective is to accurately assess the extent of severity of an injury resulting from road traffic accidents. The main aim of existing studies is to precisely assess the potential severity of crashes under diverse circumstances, su...
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/111054/ http://psasir.upm.edu.my/id/eprint/111054/1/Road_Crash_Injury_Severity_Prediction_Using_a_Graph_Neural_Network_Framework.pdf |
| _version_ | 1848865584020717568 |
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| author | Sattar, Karim A. Ishak, Iskandar Affendey, Lilly Suriani Mohd Rum, Siti Nurulain |
| author_facet | Sattar, Karim A. Ishak, Iskandar Affendey, Lilly Suriani Mohd Rum, Siti Nurulain |
| author_sort | Sattar, Karim A. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Crash severity prediction is a challenging research area, where the objective is to accurately
assess the extent of severity of an injury resulting from road traffic accidents. The main aim of existing
studies is to precisely assess the potential severity of crashes under diverse circumstances, such as weather
conditions, vehicle attributes, road characteristics and layout, and traffic control factors. This effort aids
authorities in establishing effective emergency response systems. The novelty and objective of our work
involve contributing to this research area by employing a graph architecture to capture relationships among
various crash records to uncover any hidden patterns that traditional ML models might overlook. The
current study extends existing knowledge by leveraging Graph Neural Networks (GNN) and comparing
their performance to popular ensemble-based models, which include Extreme Gradient Boosting (XGBoost),
Random Forest (RF), and Artificial Neural Networks (ANNs). Real data from the United Kingdom (UK)
was employed to achieve our goal. The data was obtained from the Department for Transport open data
portal. All models underwent training using the training dataset, followed by performance evaluation
using diverse metrics such as the accuracy, precision, recall, f1-score, Matthews Correlation Coefficient
(MCC), confusion matrix, and computational cost on the test dataset. Overall, our proposed GNN-based
model demonstrated better performance when compared to other models. Specifically, the GNN model
outperformed all other models across all metrics. For instance, the accuracy of the GNN model was 85.55%
as compared to 83.36%, 83.18%, and 83.27% for the XGBoost, RF, and ANN models, respectively. The GNN
model assisted in identifying hidden patterns by considering non-linear relationships among crash records.
Thus, the model had the potential to improve its ability to predict severe accidents, which could in turn
significantly improve emergency response efforts and reduce the likelihood of severe accidents resulting in
fatalities. |
| first_indexed | 2025-11-15T14:07:01Z |
| format | Article |
| id | upm-111054 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:07:01Z |
| publishDate | 2024 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1110542024-04-25T09:24:44Z http://psasir.upm.edu.my/id/eprint/111054/ Road crash injury severity prediction using a graph neural network framework Sattar, Karim A. Ishak, Iskandar Affendey, Lilly Suriani Mohd Rum, Siti Nurulain Crash severity prediction is a challenging research area, where the objective is to accurately assess the extent of severity of an injury resulting from road traffic accidents. The main aim of existing studies is to precisely assess the potential severity of crashes under diverse circumstances, such as weather conditions, vehicle attributes, road characteristics and layout, and traffic control factors. This effort aids authorities in establishing effective emergency response systems. The novelty and objective of our work involve contributing to this research area by employing a graph architecture to capture relationships among various crash records to uncover any hidden patterns that traditional ML models might overlook. The current study extends existing knowledge by leveraging Graph Neural Networks (GNN) and comparing their performance to popular ensemble-based models, which include Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Artificial Neural Networks (ANNs). Real data from the United Kingdom (UK) was employed to achieve our goal. The data was obtained from the Department for Transport open data portal. All models underwent training using the training dataset, followed by performance evaluation using diverse metrics such as the accuracy, precision, recall, f1-score, Matthews Correlation Coefficient (MCC), confusion matrix, and computational cost on the test dataset. Overall, our proposed GNN-based model demonstrated better performance when compared to other models. Specifically, the GNN model outperformed all other models across all metrics. For instance, the accuracy of the GNN model was 85.55% as compared to 83.36%, 83.18%, and 83.27% for the XGBoost, RF, and ANN models, respectively. The GNN model assisted in identifying hidden patterns by considering non-linear relationships among crash records. Thus, the model had the potential to improve its ability to predict severe accidents, which could in turn significantly improve emergency response efforts and reduce the likelihood of severe accidents resulting in fatalities. Institute of Electrical and Electronics Engineers 2024-03-05 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/111054/1/Road_Crash_Injury_Severity_Prediction_Using_a_Graph_Neural_Network_Framework.pdf Sattar, Karim A. and Ishak, Iskandar and Affendey, Lilly Suriani and Mohd Rum, Siti Nurulain (2024) Road crash injury severity prediction using a graph neural network framework. Road Crash Injury Severity Prediction Using a Graph Neural Network Framework, 12. pp. 37540-37556. ISSN 2169-3536 https://ieeexplore.ieee.org/document/10460524 10.1109/access.2024.3373885 |
| spellingShingle | Sattar, Karim A. Ishak, Iskandar Affendey, Lilly Suriani Mohd Rum, Siti Nurulain Road crash injury severity prediction using a graph neural network framework |
| title | Road crash injury severity prediction using a graph neural network framework |
| title_full | Road crash injury severity prediction using a graph neural network framework |
| title_fullStr | Road crash injury severity prediction using a graph neural network framework |
| title_full_unstemmed | Road crash injury severity prediction using a graph neural network framework |
| title_short | Road crash injury severity prediction using a graph neural network framework |
| title_sort | road crash injury severity prediction using a graph neural network framework |
| url | http://psasir.upm.edu.my/id/eprint/111054/ http://psasir.upm.edu.my/id/eprint/111054/ http://psasir.upm.edu.my/id/eprint/111054/ http://psasir.upm.edu.my/id/eprint/111054/1/Road_Crash_Injury_Severity_Prediction_Using_a_Graph_Neural_Network_Framework.pdf |