Version 1
: Received: 23 October 2024 / Approved: 24 October 2024 / Online: 25 October 2024 (07:55:31 CEST)
How to cite:
Braga Marsola, K.; Leda Ramos de Oliveira, A.; Yasuo Ribeiro Utino, M.; Mann, P.; Caroline Oliveira da Conceição, T. The Impact of Exogenous Variables on Soybean Freight: A Machine Learning Analysis. Preprints2024, 2024101988. https://doi.org/10.20944/preprints202410.1988.v1
Braga Marsola, K.; Leda Ramos de Oliveira, A.; Yasuo Ribeiro Utino, M.; Mann, P.; Caroline Oliveira da Conceição, T. The Impact of Exogenous Variables on Soybean Freight: A Machine Learning Analysis. Preprints 2024, 2024101988. https://doi.org/10.20944/preprints202410.1988.v1
Braga Marsola, K.; Leda Ramos de Oliveira, A.; Yasuo Ribeiro Utino, M.; Mann, P.; Caroline Oliveira da Conceição, T. The Impact of Exogenous Variables on Soybean Freight: A Machine Learning Analysis. Preprints2024, 2024101988. https://doi.org/10.20944/preprints202410.1988.v1
APA Style
Braga Marsola, K., Leda Ramos de Oliveira, A., Yasuo Ribeiro Utino, M., Mann, P., & Caroline Oliveira da Conceição, T. (2024). The Impact of Exogenous Variables on Soybean Freight: A Machine Learning Analysis. Preprints. https://doi.org/10.20944/preprints202410.1988.v1
Chicago/Turabian Style
Braga Marsola, K., Paulo Mann and Thayane Caroline Oliveira da Conceição. 2024 "The Impact of Exogenous Variables on Soybean Freight: A Machine Learning Analysis" Preprints. https://doi.org/10.20944/preprints202410.1988.v1
Abstract
Freight price prediction is a complex problem with multiple determinants. Identifying the most important variables can be used to develop more accurate prediction models and increase the competitiveness of Brazilian soybeans. This study aims to assess the influence of various exogenous variables on the price of soybean road freight and how this influence varies across different distance ranges. A combination of machine learning techniques was employed to evaluate a dataset containing variables related to freight, region, production, fuel, storage, and commercialization. The results indicate that distance is the most significant variable in determining freight costs, aligning with operational expenses such as fuel and labor. Additionally, the exchange rate and export volume emerge as key factors, reflecting the macroeconomic context of Brazilian soybean exports. The stratified analysis highlights the differentiation between short, medium, and long-distance freights, showing that different variables influence the price. Short-distance transportation is mainly geared toward the domestic market, while longer distances are more related to export logistics.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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