Application of a machine learning model for forecasting freight rate in road transport

• Autorzy: Budzyński Artur , Cieśla Maria

Identyfikatory

DOI

10.20858/sjsutst.2025.126.2

• Języki publikacji: EN

Abstrakty

EN

Recent global trends related to the forecasting freight prices is a complex task that involves considering various factors and variables that can affect the pricing dynamics in the sustainable transportation industry and business. Since freight price forecasting is subject to various uncertainties, including unforeseen events and market fluctuations, scientists are working on methods and tools, which also include artificial intelligence methods, to improve this process. The research purpose of this study is to present a universal machine learning based method enabling forecast freight prices for decision-making in the field of road transport. The paper presents the methodological assumptions of the model and shows an example of its use. The analysis was carried out with Python programming language and experiments were performed in Jupyter Notebook. Pandas library was used in research. The influence of individual variables was demonstrated using the eli5 library. The analysis allowed to conclude that machine learning models can be effective in forecasting freight prices in the context of sustainable transport due to their ability to capture complex patterns and relationships in large datasets.

Słowa kluczowe

EN

forecasting model; freight price; freight rates; machine learning; road transport; sustainable transport

PL

model prognozowania; cena frachtu; stawki frachtowe; uczenie maszynowe; transport drogowy; zrównoważony transport

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2025
• Tom: z. 126
• Strony: 23--48
• Opis fizyczny: Bibliogr. 70 poz.

Twórcy

autor

Budzyński Artur

• Institute of Quality Science and Product Management, The Cracov University of Economics, Rakowicka 27 Street, 31-510 Cracow, Poland

• https://orcid.org/0000-0002-5803-6749

autor

Cieśla Maria

• Faculty of Transport and Aviation Engineering, The Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland

• https://orcid.org/0000-0003-4566-6554

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).