A hybrid GA-ANN machine learning with novel DBSTLink data balancing approach for driving action detection: solution to road crash prediction

Road crashes have been viewed as one of the major issues leading to numerous economic losses, health problems, and fatalities, which are often due to driver actions (DA). Predicting effective DA for road crashes is crucial for developing effective intelligent transportation systems. The research community focused on transportation safety has made significant advancements in utilizing machine learning models to examine crash incidents in recent years. The application of various machine learning (ML) models has been widespread, but the specific focus on assessing DA has received relatively little attention. The article aims to propose a hybrid genetic algorithm combined with artficial neural network (GN-ANN) ML model to predict risky DA related to road accidents considering effective sampling strategies. This article also proposes a novel sampling strategy that combines Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with Synthetic Minority Oversampling Technique (SMOTE)-Tomek Link named DBSTLink, where DBSCAN and SMOTE-Tomek Links are integrated to purify datasets from noise and outliers using DBSCAN and to balance class distribution by oversampling minority classes and deleting overlaps with SMOTE-Tomek Links to enhance classifier accuracy. This method is then compared with other sampling strategies like SMOTE, SMOTE Tomek Link, and DBSM (DBSCAN with SMOTE). The objective of this study is to strengthen the existing knowledge of crash probability by examining the influence of various data balancing with the proposed balancing approach on forecast F1-score, Matthew’s correlation coefficient (MCC), and G-mean. The results demonstrate that DBSTLink gives higher performance than other measures. The proposed hybrid GA-ANN machine learning model achieved an accuracy of 99%, an F1-score of 98%, and a recall of 99%. Additionally, it achieved a G-mean of 98% and an MCC of 96%. The research found the important attributes of DA that are responsible for road crashes.