Machine Learning Forecasts of Spot Truckload Prices Using Operational Carrier Data: A Comparative Study of XGBoost and Benchmark Models

This article creates and assesses a supervX`ised machine learning model in order to forecast spot truckload rates at the shipment level, this paper. We create a feature set with lane information, shipment date, distance, and cargo weight using operational data from carriers for 2022–2024. The target is defined as freight cost in USD per load and rate per mile. A global mean model, a lane mean model, multiple linear regression, random forest, and an XGBoost ensemble are the five methods we benchmark after formulating the problem as a regression task.