A method for mining empirical route networks adaptable to complex geographical maritime areas.
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| Title: | A method for mining empirical route networks adaptable to complex geographical maritime areas. |
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| Authors: | Zhou, Yinfei1,2 (AUTHOR), Zhang, Lihua1,2 (AUTHOR) zlhua@163.com, Jia, Shuaidong1,2 (AUTHOR), Dai, Zeyuan1,2 (AUTHOR), Dong, Jian1,2 (AUTHOR) |
| Source: | Marine Geodesy. Jan2026, Vol. 49 Issue 1, p1-25. 25p. |
| Subjects: | Coasts, Spatial data structures, Collision detection (Computer animation), Naval logistics, Clustering algorithms, Trajectories (Mechanics) |
| Abstract: | Given that existing methods for mining empirical route networks can only cover major routes and cannot adapt to complex geographical maritime areas such as coastlines and areas near islands and reefs, this paper proposes a method capable of adapting to these complex geographical maritime regions. First, considering the navigational constraints imposed by the geographical environment, an adaptive quadtree operator is introduced to generate a multi-scale navigable space that aligns with the geographical environment. This ensures effective full coverage of complex maritime areas such as coastlines and areas near islands and reefs. Then, based on the association characteristics between quadtree spatial subdivision coding and latitude and longitude, massive trajectory data is efficiently matched to the navigable space. The trajectory data is reorganized and simplified using quadtree code indexing. Next, collision detection is performed between the trajectory data and the navigable space to filter out effective tracks. Finally, using the single-center density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, dense cores of each navigable space are extracted. The filtered track data is subjected to segmented clustering within the navigable space to mine multi-scale empirical maritime route networks. Experimental results show that: (1) the proposed method can mine route networks in complex geographical maritime areas such as coastlines and islands, overcoming the limitations of existing empirical networks that can only handle major routes; (2) by fully utilizing the navigable space, the proposed method shortens the average voyage distance of the main routes by 4.96% compared to existing methods. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Given that existing methods for mining empirical route networks can only cover major routes and cannot adapt to complex geographical maritime areas such as coastlines and areas near islands and reefs, this paper proposes a method capable of adapting to these complex geographical maritime regions. First, considering the navigational constraints imposed by the geographical environment, an adaptive quadtree operator is introduced to generate a multi-scale navigable space that aligns with the geographical environment. This ensures effective full coverage of complex maritime areas such as coastlines and areas near islands and reefs. Then, based on the association characteristics between quadtree spatial subdivision coding and latitude and longitude, massive trajectory data is efficiently matched to the navigable space. The trajectory data is reorganized and simplified using quadtree code indexing. Next, collision detection is performed between the trajectory data and the navigable space to filter out effective tracks. Finally, using the single-center density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm, dense cores of each navigable space are extracted. The filtered track data is subjected to segmented clustering within the navigable space to mine multi-scale empirical maritime route networks. Experimental results show that: (1) the proposed method can mine route networks in complex geographical maritime areas such as coastlines and islands, overcoming the limitations of existing empirical networks that can only handle major routes; (2) by fully utilizing the navigable space, the proposed method shortens the average voyage distance of the main routes by 4.96% compared to existing methods. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01490419 |
| DOI: | 10.1080/01490419.2025.2491421 |
