Vessel Traffic Density Prediction
A Federated Learning Approach
Bibliographic Data
| ID | 22032172 |
|---|---|
| Authors | Amin Khodamoradi (0000-0002-2700-5384, Uninova), Paulo Alves Figueiras (0000-0002-6068-1982, Uninova, corresponding author), André Grilo (0000-0002-5168-4786, Uninova), Luis Lourenço (0000-0003-3419-3640, Uninova), Bruno Rêga (Uninova), Carlos Agostinho (0000-0002-2884-776X, Uninova), Rúben Costa (0000-0002-3385-1747, Uninova), Ricardo Jardim‐Gonçalves (0000-0002-3703-6854, Uninova) |
| Year | 2025 |
| Volume | 14 |
| Issue | 9 |
| Pages | 359 |
| Publication date | 2025-09-18 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14090359 |
| OpenAlex | W4414329915 |
| Language | EN |
| References cited | 27 |
Maritime safety, environmental protection, and efficient traffic management increasingly rely on data-driven technologies. However, leveraging Automatic Identification System (AIS) data for predictive modelling faces two major challenges: the massive volume of data generated in real-time and growing privacy concerns associated with proprietary vessel information. This paper proposes a novel, privacy-preserving framework for vessel traffic density (VTD) prediction that addresses both challenges. The approach combines the European Maritime Observation and Data Network’s (EMODNet) grid-based VTD calculation method with Convolutional Neural Networks (CNN) to model spatiotemporal traffic patterns and employs Federated Learning to collaboratively build a global predictive model without the need for explicit sharing of proprietary AIS data. Three geographically diverse AIS datasets were harmonized, processed, and used to train local CNN models on hourly VTD matrices. These models were then aggregated via a Federated Learning framework under a lifelong learning scenario. Evaluation using Sparse Mean Squared Error shows that the federated global model achieves promising accuracy in sparse data scenarios and maintains performance parity when compared with local CNN-based models, all while preserving data privacy and minimizing hardware performance needs and data communication overheads. The results highlight the approach’s effectiveness and scalability for real-world maritime applications in traffic forecasting, safety, and operational planning
Artificial neural network · Convolutional neural network · Data modeling · Deep learning · Federated learning · Scalability · Server · Maritime Navigation and Safety · Maritime Ports and Logistics · Maritime Transport Emissions and Efficiency
| Highly cited | No |
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