| Title: | Privacy-preserving AI-based framework for container transportation demand forecasting in sea-rail intermodal systems |
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| Authors: | ID Huang, L. (Author)
ID Jiang, D. Y. (Author)
ID Bai, T. (Author) |
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| Files: |  APEM20-1_099-115.pdf (1,37 MB)
MD5: 7AEA551929B81A5039F16AB8115A4C42
 https://apem-journal.org/Archives/2025/VOL20-ISSUE01.html
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | 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. |
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| Keywords: | 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 |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 20.10.2024 |
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| Article acceptance date: | 03.03.2025 |
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| Publication date: | 29.03.2025 |
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| Publisher: | Fakulteta za strojništvo |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 99-115 |
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| Numbering: | Vol. 20, no. 1 |
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| PID: | 20.500.12556/DKUM-96604  |
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| UDC: | 656.025.4 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 265430019 |
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| DOI: | 10.14743/apem2025.1.530 |
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| Publication date in DKUM: | 20.01.2026 |
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| Views: | 188 |
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| Downloads: | 4 |
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| Metadata: |    |
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| Categories: | Misc.
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