APPLICATION OF A MACHINE LEARNING MODEL FOR FORECASTING FREIGHT RATE IN ROAD TRANSPORT

Abstract

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.