Predictive Maintenance in Logistics Fleets: A Comparative Evaluation of Random Forest, XGBoost, and Logistic Regression Using Operational and Technical Indicators

Abstract

Background: Predictive maintenance (PdM) is increasingly recognized as essential for reducing fleet downtime and maintenance costs, yet the existing literature on transport and logistics fleets relies predominantly on traditional or single-model approaches, with ensemble methods such as random forest and gradient boosting remaining comparatively underexplored. Methods: This study develops and compares three classification algorithms—logistic regression, random forest and XGBoost—for predicting vehicle maintenance needs. Each classifier was first fitted once on a 70:30 train–test split (35,000/15,000 observations) to support interpretation of coefficients, predictor importance, and ROC curves, using precision, recall, and ROC-AUC. Performance and model ranking were then confirmed for robustness using 5-fold stratified cross-validation with formal significance testing. Results: Reported issues and service history emerged as the dominant predictors of maintenance needs. Random forest and XGBoost achieved comparable predictive performance under 5-fold cross-validation (mean AUC-ROC of 0.8546 and 0.8528, respectively), with the difference between them not statistically significant; both clearly outperformed logistic regression (AUC-ROC 0.8215). Conclusions: By integrating operational and technical data within a validated, comparative machine learning framework, the study provides a practical basis for decision-support systems enabling the reduction of unnecessary interventions, minimizing downtime, and improving fleet cost efficiency and reliability.