Development of a Multidimensional Analysis and Integrated Visualization Method for Maritime Traffic Behaviors Using DBSCAN-Based Dynamic Clustering
Automatic Identification System (AIS) data offer essential insights into maritime traffic patterns; however, effective visualization tools for decision-making remain limited. This study presents an integrated visualization processing method to support ship operators by identifying maritime traffic b...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/529 |
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| author | Daehan Lee Daun Jang Sanglok Yoo |
| author_facet | Daehan Lee Daun Jang Sanglok Yoo |
| author_sort | Daehan Lee |
| collection | DOAJ |
| description | Automatic Identification System (AIS) data offer essential insights into maritime traffic patterns; however, effective visualization tools for decision-making remain limited. This study presents an integrated visualization processing method to support ship operators by identifying maritime traffic behavior information, such as traffic density, direction, and flow in specific sea navigational areas. We analyzed AIS dynamic data from a specific sea area, calculated ship density distributions across a grid lattice, and obtained visualizations of traffic-dense areas as heat maps. Using the density-based spatial clustering of applications with a noise algorithm, we detected traffic direction at each grid point, which was visualized in the form of directional arrows, and clustered ship trajectories to identify representative traffic flows. The visualizations were integrated and overlaid onto an S-57-based electronic nautical map for Mokpo’s entry and exit routes, revealing primary shipping lanes and critical inflection points within the target area. This integrated visualization method simultaneously displays traffic density, flow, and customary routes. It is adapted for the electronic nautical chart (S-101) under the next-generation hydrographic information standard (S-100), which can be used as a tool to support decision-making for ship operators. |
| format | Article |
| id | doaj-art-ec6cb2dea7bd4b0883a210b29c022337 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ec6cb2dea7bd4b0883a210b29c0223372025-01-24T13:19:42ZengMDPI AGApplied Sciences2076-34172025-01-0115252910.3390/app15020529Development of a Multidimensional Analysis and Integrated Visualization Method for Maritime Traffic Behaviors Using DBSCAN-Based Dynamic ClusteringDaehan Lee0Daun Jang1Sanglok Yoo2Department of Maritime Transportation System, Mokpo National Maritime University, Mokpo 58628, Republic of KoreaDivision of Cadet Training, Mokpo National Maritime University, Mokpo 58628, Republic of KoreaResearch Institute, Future Ocean Information Technology, Inc., Jeju 63208, Republic of KoreaAutomatic Identification System (AIS) data offer essential insights into maritime traffic patterns; however, effective visualization tools for decision-making remain limited. This study presents an integrated visualization processing method to support ship operators by identifying maritime traffic behavior information, such as traffic density, direction, and flow in specific sea navigational areas. We analyzed AIS dynamic data from a specific sea area, calculated ship density distributions across a grid lattice, and obtained visualizations of traffic-dense areas as heat maps. Using the density-based spatial clustering of applications with a noise algorithm, we detected traffic direction at each grid point, which was visualized in the form of directional arrows, and clustered ship trajectories to identify representative traffic flows. The visualizations were integrated and overlaid onto an S-57-based electronic nautical map for Mokpo’s entry and exit routes, revealing primary shipping lanes and critical inflection points within the target area. This integrated visualization method simultaneously displays traffic density, flow, and customary routes. It is adapted for the electronic nautical chart (S-101) under the next-generation hydrographic information standard (S-100), which can be used as a tool to support decision-making for ship operators.https://www.mdpi.com/2076-3417/15/2/529visualizationAIS datamarine traffic behaviorsmarine traffic densitymarine traffic directionmarine traffic stream |
| spellingShingle | Daehan Lee Daun Jang Sanglok Yoo Development of a Multidimensional Analysis and Integrated Visualization Method for Maritime Traffic Behaviors Using DBSCAN-Based Dynamic Clustering Applied Sciences visualization AIS data marine traffic behaviors marine traffic density marine traffic direction marine traffic stream |
| title | Development of a Multidimensional Analysis and Integrated Visualization Method for Maritime Traffic Behaviors Using DBSCAN-Based Dynamic Clustering |
| title_full | Development of a Multidimensional Analysis and Integrated Visualization Method for Maritime Traffic Behaviors Using DBSCAN-Based Dynamic Clustering |
| title_fullStr | Development of a Multidimensional Analysis and Integrated Visualization Method for Maritime Traffic Behaviors Using DBSCAN-Based Dynamic Clustering |
| title_full_unstemmed | Development of a Multidimensional Analysis and Integrated Visualization Method for Maritime Traffic Behaviors Using DBSCAN-Based Dynamic Clustering |
| title_short | Development of a Multidimensional Analysis and Integrated Visualization Method for Maritime Traffic Behaviors Using DBSCAN-Based Dynamic Clustering |
| title_sort | development of a multidimensional analysis and integrated visualization method for maritime traffic behaviors using dbscan based dynamic clustering |
| topic | visualization AIS data marine traffic behaviors marine traffic density marine traffic direction marine traffic stream |
| url | https://www.mdpi.com/2076-3417/15/2/529 |
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