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Traffic accidents continue to be a significant cause of fatalities, injuries, and considerable disruptions on our highways. Understanding the underlying factors behind these incidents is crucial for improving safety on road networks. While recent studies have highlighted the usefulness of predictive modeling in uncovering factors leading to accidents, there remains a gap in explaining the inner workings of complex machine learning and deep learning models and how various features influence accident prediction. This lack of transparency may lead to these models being perceived as black boxes, potentially undermining trust in their findings among stakeholders. The primary aim of this research is to develop predictive models using diverse transfer learning techniques and shed light on the most influential factors using Shapley values. In predicting injury severity in accidents, we employ Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNET), EfficientNetB4, InceptionV3, Extreme Inception (Xception), Visual Geometry Group (VGG19), AlexNet, and MobileNet. Among these models, MobileNet emerges with the highest accuracy at 0.9817. Furthermore, by comprehending how different features impact accident prediction models, researchers can deepen their understanding of the factors contributing to accidents and devise more effective interventions for their prevention.

An Automated Approach for Predicting Road Traffic Accident Severity Using Transformer Learning and Explainable AI Technique