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

Rabimov, Nodir Rahmatilloevich, Akharov , Akmal Rustamovich, Nazarov , Fayzullo Makhmadiyarovich

Innovative Multidisciplinary Journal of Applied Technology · 2025-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarRabimov, Nodir Rahmatilloevich, Akharov , Akmal Rustamovich, Nazarov , Fayzullo Makhmadiyarovich
JurnalInnovative Multidisciplinary Journal of Applied Technology
NashriyotТДТУ Қўқон филиали
ISSN2995-486X, 2995-486x
Nashr sanasi2025-11-08
Jild3
Son12
Betlar24-35
TilIngliz
Ilmiy sohaTexnika
DOI10.51699/rdsf2d55
Indeksga qo‘shilgan2026-08-26

DOI: 10.51699/rdsf2d55 · Maqolaning asl sahifasi

Ilmiy soha

Texnika

Iqtibos keltirish

APARabimov, Nodir Rahmatilloevich, Akharov , Akmal Rustamovich, & Nazarov , Fayzullo Makhmadiyarovich (2025). Machine Learning Forecasts of Spot Truckload Prices Using Operational Carrier Data: A Comparative Study of XGBoost and Benchmark Models. Innovative Multidisciplinary Journal of Applied Technology, 3(12), 24-35. https://doi.org/10.51699/rdsf2d55
GOST (OAK)Rabimov, Nodir Rahmatilloevich, Akharov , Akmal Rustamovich, Nazarov , Fayzullo Makhmadiyarovich Machine Learning Forecasts of Spot Truckload Prices Using Operational Carrier Data: A Comparative Study of XGBoost and Benchmark Models // Innovative Multidisciplinary Journal of Applied Technology. – 2025. – T. 3, № 12. – P. 24-35. – DOI: 10.51699/rdsf2d55.
MLARabimov, Nodir Rahmatilloevich, Akharov , Akmal Rustamovich, and Nazarov , Fayzullo Makhmadiyarovich. “Machine Learning Forecasts of Spot Truckload Prices Using Operational Carrier Data: A Comparative Study of XGBoost and Benchmark Models.” Innovative Multidisciplinary Journal of Applied Technology, 3(12), 24-35, 2025. https://doi.org/10.51699/rdsf2d55
ChicagoRabimov, Nodir Rahmatilloevich, Akharov , Akmal Rustamovich, and Nazarov , Fayzullo Makhmadiyarovich. “Machine Learning Forecasts of Spot Truckload Prices Using Operational Carrier Data: A Comparative Study of XGBoost and Benchmark Models.” Innovative Multidisciplinary Journal of Applied Technology, 3(12), 24-35 (2025). https://doi.org/10.51699/rdsf2d55
HarvardRabimov, Nodir Rahmatilloevich, Akharov , Akmal Rustamovich, and Nazarov , Fayzullo Makhmadiyarovich (2025) ‘Machine Learning Forecasts of Spot Truckload Prices Using Operational Carrier Data: A Comparative Study of XGBoost and Benchmark Models’, Innovative Multidisciplinary Journal of Applied Technology, 3(12), 24-35. https://doi.org/10.51699/rdsf2d55
BibTeX
@article{rabimovnodirrahmatilloevich2025,
  title = {Machine Learning Forecasts of Spot Truckload Prices Using Operational Carrier Data: A Comparative Study of XGBoost and Benchmark Models},
  author = {Rabimov, Nodir Rahmatilloevich and Akharov , Akmal Rustamovich and Nazarov , Fayzullo Makhmadiyarovich},
  journal = {Innovative Multidisciplinary Journal of Applied Technology},
  year = {2025},
  volume = {3},
  number = {12},
  pages = {24-35},
  doi = {10.51699/rdsf2d55},
  url = {https://doi.org/10.51699/rdsf2d55}
}

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