Mixed Effects Random Forest Model for Maintenance Cost Estimation in Heavy-Duty Vehicles Using Diesel and Alternative Fuels

Abstract

Maintenance & Repair costs in heavy-duty trucks are an important component of the total cost of ownership. Due to the very limited availability of real-time data collected from medium- and heavy-duty vehicles using alternative fuels, this topic has not been well studied resulting in a very slow diffusion of alternative fuel vehicles in the market. This study focuses on collecting maintenance data related to diesel and alternative fuels such as natural gas and propane for the school bus, delivery truck, vocational truck, refuse truck, goods movement truck, and transit bus. The novelty of this work lies in identifying the mixed effects in the maintenance data and using a mixed-effect model for developing a single prediction model on clustered longitudinal data. A mixed-effect random forest machine learning model is trained on the maintenance data for estimating the average cost per mile. The model achieved an R2 of 98.96% with a mean square error of 0.0089 /mile for training and an R2 of 94.31% with a mean square error of 0.0312 /mile for the validation dataset. The prediction model is evaluated on each cluster of data and observed to perform well capturing the variations in each cluster very well. Furthermore, the performance of the mixed-effect random forest model is compared with the XGBoost ensemble model.