1403 KB0 KB2025Bai, T.Huang, L.Jiang, D. Y.številka:1letnik:20str. 99-115DOI:10.14743/apem2025.1.530ISSN:1854-6250COBISSID_HOST:265430019URN:URN:NBN:SI:doc-R1MNGKZ4enFakulteta za strojništvo, Inštitut za proizvodno strojništvoAdvances in production engineering and managementcontainer transportation demand forecastingfreight demand forecastinghomomorphic encryptionintermodalni transportkontejnerski transportmachine learningnapovedovanje transportnih zahtevprevoz tovoraprivacy-preserving methodsrazporejanjeresource allocation and schedulingsample and feature selectionstrojno učenjeumetna inteligencavertical federated learningPrivacy-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 challengesTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Mariboru, Fakulteta za strojništvo