Privacy-preserving AI-based framework for container transportation demand forecasting in sea-rail intermodal systemsHuang, L. (Avtor) Jiang, D. Y. (Avtor) Bai, T. (Avtor) freight demand forecastingcontainer transportation demand forecastingvertical federated learningprivacy-preserving methodssample and feature selectionmachine learninghomomorphic encryptionresource allocation and schedulingIn 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.Fakulteta za strojništvo20252026-01-20 14:19:20Članek v reviji96604UDK: 656.025.4COBISS_ID: 265430019DOI: 10.14743/apem2025.1.530ISSN pri članku: 1854-6250sl