Advances in Production Engineering & Management
Volume 20 | Number 1 | March 2025 | pp 99–115
https://doi.org/10.14743/apem2025.1.530
Privacy-preserving AI-based framework for container transportation demand forecasting in sea-rail intermodal systems
Huang, L.; Jiang, D.Y.; Bai, T.Y.
ABSTRACT AND REFERENCES (PDF) |
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A B S T R A C T
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.
A R T I C L E I N F O
Keywords • Freight demand forecasting; Vertical federated learning; Privacy-preserving methods; Sample and feature selection; Machine learning; Homomorphic encryption; Resource allocation and scheduling
Corresponding author • Huang, L.
Article history • Received 20 October 2024, Revised 19 February 2025, Accepted 3 March 2025
Published on-line • 29 March 2025
E X P O R T C I T A T I O N
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