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\nDOI: [https://doi.org/10.20858/sjsutst.2025.126.2](https://doi.org/10.20858/sjsutst.2025.126.2)[ ](https://doi.org/10.20858/sjsutst.2025.126.2)Journal homepage: [http://sjsutst.polsl.pl](http://sjsutst.polsl.pl)  \nArticle citation information:  \nBudzyński, A., Cieśla, M. Application of a machine learning model for forecasting freight rate in road transport. Scientific Journal of Silesian University of Technology. Series Transport. 2025, 126, 23-48. ISSN: 0209-3324.  \nDOI: [https://doi.org/10.20858/sjsutst.2025.126.2](https://doi.org/10.20858/sjsutst.2025.126.2) .  \nArtur BUDZYŃSKI1, Maria CIEŚLA2  \nAPPLICATION OF A MACHINE LEARNING MODEL FOR FORECASTING FREIGHT RATE IN ROAD TRANSPORT  \nSummary. 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.  \nKeywords: forecasting model, freight price, freight rate, machine learning, road transport, sustainable transport  \n1 Institute of Quality Science and Product Management, The Cracov University of Economics, Rakowicka 27 Street, 31-510 Cracow, Poland. Email: [abudzyns@uek.krakow.pl](abudzyns@uek.krakow.pl).  \nORCID: [https://orcid.org/0000-0002-5803-6749](https://orcid.org/0000-0002-5803-6749)  \n2 Faculty of Transport and Aviation Engineering, The Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, [Poland. Email: maria.ciesla@polsl.pl. ORCID:](Poland. Email: maria.ciesla@polsl.pl. ORCID:) [https://orcid.org/ 0000-0003-4566-6554](https://orcid.org/ 0000-0003-4566-6554)  \n1. INTRODUCTION  \nThe freight price or freight rate refers to the charges or fees associated with the transportation of goods or cargo from one point to another. It is associated with the transportation cost that ashipper or consignee is charged for the transportation of goods. For this reason, in many companies, it is one of the most important elements of decision rationalization in the field of transport processes. This is a very difficult process because it involves making decisions about changing external conditions. In addition, the dynamics of the global economy are shaped, among others, by transportation costs [1]. According to estimates, more than 80% of the volume of international trade in goods is carried by sea [2], however, the road and rail modes are mainly the ones dealing with intra-regional flows related to the delivery of cargo to the largest seaports. In 2020, due to the coronavirus pandemic, the revenue of the road freight transport industry decreased by approximately 22% and reached over 1.7 trillion Euros, which increased further to over two trillion Euros [3] . International road transport represented 25.3% of the EU’sexports and 19.1% of its imports [4] . The European road freight market grew 3.5% in 2022; however, the war in Ukraine acted as a major set-back to recovery. Furthermore, the Ti’s 20","cbCaioA5ClnJ0JNX","https://ap.wps.com/l/cbCaioA5ClnJ0JNX","pdf",1051094,26,"English","# Summary\n## Model purpose and methodology\n# Keywords\n# 1. Introduction","[{\"question\":\"What is the research purpose of the study?\",\"answer\":\"To present a universal machine-learning based method for forecasting freight prices to support decision-making in road transport.\"},{\"question\":\"How was the analysis conducted?\",\"answer\":\"The study used Python with experiments performed in Jupyter Notebook, applying the Pandas library for research and using the eli5 library to demonstrate variable influence.\"},{\"question\":\"Why is freight price forecasting difficult?\",\"answer\":\"It is difficult due to multiple influencing factors and uncertainties, including unforeseen events and market fluctuations, which affect pricing dynamics.\"}]","Application of a Machine Learning Model for Forecasting Freight Rate in Road Transport | PDF",66]