Crash severity analysis in distracted driving using unlabeled and imbalanced data: A novel approach using Robust Two-Phase Ensemble Predictor

Abstract

Distracted driving plays a pivotal role in road accidents. Therefore, prediction of the crash severity due to distracted driving is essential. Although several machine learning techniques exist for such prediction, it is difficult to use them in case of the unavailability of class labels and class imbalance issues. Moreover, there is a severe lack of research considering environmental factors and driver’s behaviour to predict the crash severity. To address the issues, in this study, a robust two-phase ensemble prediction model has been developed, considering the geolocation information and driver’s behaviour. An analysis of the unlabeled and high-dimensional data is generally challenging. We perform dimensionality reduction using t-SNE, followed by agglomerative hierarchical clustering to get labelled data. We have used Synthetic Minority Over-sampling Technique (SMOTE) to mitigate the class imbalance issue. Subsequently, we observe that some localities have much more severe crashes, so we develop a feature considering the geolocation information. Then, we create a novel predictor called Robust Two-Phase Ensemble Predictor (R2PEP) to predict the crash severity. The performance of the proposed model has been compared with five state-of-the-art algorithms using a dataset we obtained from the Nevada Department of Transportation. The comparison demonstrates the superiority of our model over the other models, with an accuracy of 99.6%.