Predicting the Severity of Road Accidents Using Deep and Transfer Learning Approaches

Road traffic accidents are a major concern worldwide, often resulting in serious injuries and fatalities. Rapid and accurate assessment of accident severity can significantly aid emergency response and resource allocation. This project presents a comprehensive, automated system for predicting the severity of road traffic accidents using both tabular (structured) and image (unstructured) data. For tabular data, models including Multilayer Perceptron (MLP), XGBoost and TabNet were implemented to classify severity levels. For image-based classification, deep convolutional neural networks such as MobileNetV2 and ResNet50 were used, leveraging transfer learning to enhance performance. An EfficientNetB7 model was employed to autolabel unlabelled accident images, improving dataset consistency and reducing manual labelling effort. The system was evaluated using accuracy, precision, recall and F1-score, with performance visualized through confusion matrices and bar charts. The results demonstrate high classification accuracy across all models. This intelligent, multi-input framework serves as a practical tool for real-time accident severity assessment, with potential applications in emergency response systems and smart traffic management.