Context reasoning-based vehicle traffic accident severity prediction using a machine learning algorithm in edge computing

Abstract The rapid development of the Internet of Things (IoT) has transformed intelligent transportation systems. However, traffic accidents remain a significant challenge, and it is imperative to develop effective strategies to reduce their frequency and severity. This research introduces a hybrid model that integrates Adaptive Bayesian Optimization (ABO) with Random Forest (RF) to enhance the prediction of traffic accident severity. By leveraging ABO to optimize RF parameters, the model achieves superior accuracy compared to conventional approaches while maintaining interpretability through relative importance and partial dependence plots. Furthermore, utilizing real-time vehicle data, the system implements context-based vehicle accident detection within vehicular ad hoc networks (VANETs). The integration of Enhanced Local Adaptive Machine Learning (ELAML), RF, and ABO-RF demonstrates robust performance in collision detection, outperforming models such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN), with a detection accuracy of 90%. Compared to existing models, the proposed ABO-RF achieved the highest accuracy (90%), perfect recall (100%), and the best F1 score (94.74%), with lower error rate (10%), FAR (6.00%), MDR (25.00%), and detection latency (25.24%) than conventional methods such as MLP, ResNet-50, AdaBoost and XGBoost. These findings offer valuable insights into improving road safety and mitigating accident severity in the Internet of Vehicles (IoV)-enabled intelligent transportation systems.