20.500.12556/DKUM-96604 Privacy-preserving AI-based framework for container transportation demand forecasting in sea-rail intermodal systems In response to the growing demand for accurate freight forecasting in sea-rail intermodal transportation, particularly under the constraints of stringent data protection regulations, we introduce a privacy-preserving, AI-based framework that focuses on the micro-level identification of container transport potential. The framework combines Vertical Federated Learning (VFL) with advanced feature and sample selection techniques. It leverages privacy-preserving methods, such as homomorphic encryption and random noise, enabling secure collaboration between ports and railways while safeguarding commercially sensitive data. Through extensive experiments, our framework demonstrates superior performance in predicting container transport demand, significantly improving the accuracy of resource allocation and scheduling decisions for rail operators. The framework not only ensures compliance with data protection regulations but also provides valuable insights into intermodal transportation planning, optimizing both railway operations and customer service quality. This approach offers a practical solution for improving strategic decision-making in the sea-rail intermodal sector amid increasing privacy demands and complex logistical challenges. freight demand forecasting container transportation demand forecasting vertical federated learning privacy-preserving methods sample and feature selection machine learning homomorphic encryption resource allocation and scheduling prevoz tovora kontejnerski transport napovedovanje transportnih zahtev razporejanje umetna inteligenca strojno učenje intermodalni transport true false true Fakulteta za strojništvo Angleški jezik Slovenski jezik https://apem-journal.org/ Članek v reviji 2026-01-20 14:19:20 2026-01-20 14:24:27 2026-01-22 03:11:45 0000-00-00 00:00:00 2025 0 0 str. 99-115 no. 1 Vol. 20 March 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-10-20 2025-03-03 2025-03-29 656.025.4 265430019 10.14743/apem2025.1.530 1854-6250 APEM20-1_099-115.pdf APEM20-1_099-115.pdf 1 7AEA551929B81A5039F16AB8115A4C42 294cfd70349dd7766e183f885cebb6aa6a7da822bcfbc22e593af7200776638f e19c62fc-f602-11f0-8ae2-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=202293 https://apem-journal.org/Archives/2025/VOL20-ISSUE01.html 0 efe00e59-f602-11f0-8ae2-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=202294 Fakulteta za strojništvo Advances in production engineering & management 0 0 0