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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | AI |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-2688/5/4/124 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846106275909206016 |
|---|---|
| author | Ilyass Benfaress Afaf Bouhoute Ahmed Zinedine |
| author_facet | Ilyass Benfaress Afaf Bouhoute Ahmed Zinedine |
| author_sort | Ilyass Benfaress |
| collection | DOAJ |
| description | 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 and categorical variables, resulting in a notable increase in prediction accuracy. Methods: A comparative analysis was performed with other Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Darknet, and Extreme Inception (Xception), showing superior performance of the proposed Resnet. Key factors influencing accident severity were identified, with Shapley Additive Explanations (SHAP) values helping to address the need for transparent and explainable Artificial Intelligence (AI) in critical decision-making areas. Results: The generalizability of the ResNet model was assessed by training it, initially, on a UK road accidents dataset and validating it on a distinct dataset from India. The model consistently demonstrated high predictive accuracy, underscoring its robustness across diverse contexts, despite regional differences. Conclusions: These results suggest that the adapted ResNet model could significantly enhance traffic safety evaluations and contribute to the formulation of more effective traffic management strategies. |
| format | Article |
| id | doaj-art-69e1363d801e47e0854ffdd3d27ff64b |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-69e1363d801e47e0854ffdd3d27ff64b2024-12-27T14:05:04ZengMDPI AGAI2673-26882024-11-01542568258510.3390/ai5040124Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for InterpretabilityIlyass Benfaress0Afaf Bouhoute1Ahmed Zinedine2Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30050, MoroccoFaculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30050, MoroccoFaculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30050, MoroccoBackground/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 and categorical variables, resulting in a notable increase in prediction accuracy. Methods: A comparative analysis was performed with other Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Darknet, and Extreme Inception (Xception), showing superior performance of the proposed Resnet. Key factors influencing accident severity were identified, with Shapley Additive Explanations (SHAP) values helping to address the need for transparent and explainable Artificial Intelligence (AI) in critical decision-making areas. Results: The generalizability of the ResNet model was assessed by training it, initially, on a UK road accidents dataset and validating it on a distinct dataset from India. The model consistently demonstrated high predictive accuracy, underscoring its robustness across diverse contexts, despite regional differences. Conclusions: These results suggest that the adapted ResNet model could significantly enhance traffic safety evaluations and contribute to the formulation of more effective traffic management strategies.https://www.mdpi.com/2673-2688/5/4/124traffic accident severityexplainable Artificial IntelligenceResNet architectureSHAPMachine Learningdeep learning |
| spellingShingle | Ilyass Benfaress Afaf Bouhoute Ahmed Zinedine Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability AI traffic accident severity explainable Artificial Intelligence ResNet architecture SHAP Machine Learning deep learning |
| title | Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability |
| title_full | Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability |
| title_fullStr | Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability |
| title_full_unstemmed | Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability |
| title_short | Enhancing Traffic Accident Severity Prediction Using ResNet and SHAP for Interpretability |
| title_sort | enhancing traffic accident severity prediction using resnet and shap for interpretability |
| topic | traffic accident severity explainable Artificial Intelligence ResNet architecture SHAP Machine Learning deep learning |
| url | https://www.mdpi.com/2673-2688/5/4/124 |
| work_keys_str_mv | AT ilyassbenfaress enhancingtrafficaccidentseveritypredictionusingresnetandshapforinterpretability AT afafbouhoute enhancingtrafficaccidentseveritypredictionusingresnetandshapforinterpretability AT ahmedzinedine enhancingtrafficaccidentseveritypredictionusingresnetandshapforinterpretability |