Predicting and understanding long-haul truck driver turnover using driver-level operational data and supervised machine learning classifiers

Abstract

Truck drivers are essential to modern supply chains. However, the companies that employ them are having trouble keeping them. At many trucking firms, annual turnover rates approach 100 percent or higher. This paper develops a method for predicting individual truck driver turnover events before they happen by applying supervised machine learning classifiers to a new source of operational truck driver data. Our paper uses Electronic Logging Device (ELD) data, which comprise newly federally-mandated, time-stamped work logs collected from approximately 1200 American long-haul truck drivers over 3 years. We train three supervised machine learning classifiers (logistic regression, random forests, and support vector machines) on this data and achieve 60 to 70 percent prediction accuracy and 50 to 60 percent recall across two 5-fold cross-validated experiments. We observe that the quantity and consistency of week-day driving assignments explain most of our models’ predictive power. We offer these results as both a new technical tool, as well as novel managerial insights for improving global truck driver retention problems for the benefit of supply chains worldwide.