A dynamic trajectory temporal density model for analyzing maritime traffic patterns
Jiang, D.; Shi, G.; Ma, L.; Li, W.; Wang, X.; Zhu, G. (2025). A dynamic trajectory temporal density model for analyzing maritime traffic patterns. J. Mar. Sci. Eng. 13(2): 381. https://dx.doi.org/10.3390/jmse13020381
In: Journal of Marine Science and Engineering. MDPI: Basel. ISSN 2077-1312; e-ISSN 2077-1312, more
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| Abstract |
This study investigates the spatiotemporal density aggregation and pattern distribution of vessel traffic amidst bustling maritime logistics scenarios. Firstly, a relatively new spatiotemporal segmentation and reconstruction method is proposed for ship AIS trajectories to address trajectory disruptions caused by berthing, anchorage, and other factors. Subsequently, a trajectory filtering algorithm utilizing time window panning is introduced to mitigate position jumps and deviation errors in trajectory points, ensuring that the dynamic trajectory adheres to the spatiotemporal correlations of ship motion. Secondly, to establish a geographical spatial mapping of dynamic trajectories, spatial gridding is applied to maritime traffic areas. By associating the geographical space of traffic activities with the temporal attributes of dynamic trajectories, a dynamic trajectory temporal density model is constructed. Finally, a case study is conducted to evaluate the effectiveness and applicability of the proposed method in identifying spatiotemporal patterns of maritime traffic and spatiotemporal density aggregation states. The results show that the proposed method can identify dynamic trajectory traffic patterns after the application of compression algorithms, providing a novel approach to studying the spatiotemporal aggregation of maritime traffic in the era of big data. |
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