Maritime traffic network is essential for navigation efficiency and safety of the maritime transport system. This study proposes a framework for extracting maritime traffic network based on Automatic Identification System (AIS) data. The framework consists of maritime traffic pattern recognition, semantic routes extraction, route decomposition, and network generation. Firstly, a data-driven method is introduced to recognize ship behavior patterns and extends the single ship behaviors to regional characteristics to determine the departure-arrival areas. Then, based on the different combination of departure-arrival areas, the ship trajectories are classified to traffic groups. Subsequently, the grid-system is used to rasterize each traffic group, which realizes the fusion of trajectory data and geographic location information. Finally, to obtain the main routes and navigation channels, the extraction method is introduced by establishing the cumulative grid importance function. The main routes, together with the navigation channels, compose the maritime traffic network. The method is applied to AIS data in the Beibu Gulf, and the results show that the traffic network contains 12 stop areas, 4 entry/exit locations, 13 main routes as well as their corresponding navigation channels. It is therefore concluded that the proposed method helps (1) provide a theoretical framework to obtain and analyze the maritime traffic network and (2) enrich navigation channel identification methods for maritime transport management.
- Big data analytics
- Machine learning
- Maritime traffic network
- Maritime transport management
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Researchers at Aalto University Release New Data on Data Mining (A Data Mining Method To Extract Traffic Network for Maritime Transport Management)
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