Prediction and exploration of contributory factors related to fatigue driving among long-haul truck drivers traveling across India: A tree-based machine learning approach

Abstract

IntroductionLong-haul Truck drivers (LHTDs) have long working hours, insufficient rest, and poor health conditions and often experience fatigue that substantially may lead to crashes and injuries. Despite its potential harmfulness, we have little understanding on non-linear hidden patterns of influential factors on fatigue driving, especially in developing nations like India. ObjectivesThis paper aimed to predict fatigue driving among LHTDs using four tree-based machine learning techniques including Decision tree (DT), Random Forest (RF), Adaptive boosting (AdaBoost), and Extreme gradient boosting (XGBoost) to analyze the non-linear hidden pattern of most influential variables contributing to fatigue driving among Indian LHTDS. MethodsUsing a cross-sectional study design, a face-to-face interview was conducted among LHTDs using carefully designed questionnaire in Salem, Tamil Nadu, India. A total of 756 responses were obtained from LHTDs based on four aspects including socio-economic characteristics, work and vehicle characteristics, health-related lifestyle characteristics, and fatigue-inducing characteristics using a convenience sampling technique. Four tree-based machine learning algorithms namely DT, RF, Adaboost, and Xgboost were employed to predict fatigue driving. The influence of predictors on fatigue driving for the most suitable model was determined through variable importance plot and their causality effect on probability of fatigue were examined using Partial dependency plot (PDP). All the analysis were carried out using IBM SPSS statistics Version 27.0 and R programming language version 4.2.1. ResultsFrom the analysis, it was found that RF model outperformed other investigated classifiers (Accuracy = 81.2%, F1 score = 58.82%, AUROC = 0.854). Furthermore, variable importance plot of befitting RF classifier showed that type of commodity carried, pressured delivery of goods, countermeasure mostly followed, and education level of the LHTD as some of most influential predictors causing fatigue. ConclusionThese findings provide insights to state and highway transportation officials and Indian trucking industries for framing effective strategies to promote safety and well-being among LHTDs.