<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the vigorous development of maritime traffic, the importance of maritime navigation safety is increasing day by day. Ship trajectory extraction and analysis play an important role in ensuring navigation safety. At present, the DBSCAN (density-based spatial clustering of applications with noise) algorithm is the most common method in the research of ship trajectory extraction, but it has shortcomings such as missing ship trajectories in the process of trajectory division. The improved multi-attribute DBSCAN algorithm avoids trajectory division and greatly reduces the probability of missing sub-trajectories. By introducing the position, speed and heading of the ship track point, dividing the complex water area and vectorising the ship track, the function of guaranteeing the track integrity can be achieved and the ship clustering effect can be better realised. The result shows that the cluster fitting effect reaches up to 99.83%, which proves that the multi-attribute DBSCAN algorithm and cluster analysis algorithm have higher reliability and provide better theoretical guidance for the analysis of ship abnormal behaviour.</p>
</abstract>

With the vigorous development of maritime traffic, the importance of maritime navigation safety is increasing day by day. Ship trajectory extraction and analysis play an important role in ensuring navigation safety. At present, the DBSCAN (density-based spatial clustering of applications with noise) algorithm is the most common method in the research of ship trajectory extraction, but it has shortcomings such as missing ship trajectories in the process of trajectory division. The improved multi-attribute DBSCAN algorithm avoids trajectory division and greatly reduces the probability of missing sub-trajectories. By introducing the position, speed and heading of the ship track point, dividing the complex water area and vectorising the ship track, the function of guaranteeing the track integrity can be achieved and the ship clustering effect can be better realised. The result shows that the cluster fitting effect reaches up to 99.83%, which proves that the multi-attribute DBSCAN algorithm and cluster analysis algorithm have higher reliability and provide better theoretical guidance for the analysis of ship abnormal behaviour.

Research on Ship Trajectory Extraction Based on Multi-Attribute DBSCAN Optimisation Algorithm

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the vigorous development of maritime traffic, the importance of maritime navigation safety is increasing day by day. Ship trajectory extraction and...

Research on Ship Trajectory Extraction Based on Multi-Attribute DBSCAN Optimisation Algorithm

Publié en ligne: 30 avr. 2021

Pages: 136 - 148

DOI: https://doi.org/10.2478/pomr-2021-0013

Mots clés
clustering algorithm, abnormal route, DBSCAN, Feature trajectory extraction, fitting analysis

© 2021 Xiaofeng Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Ship Trajectory Extraction Based on Multi-Attribute DBSCAN Optimisation Algorithm

Publié en ligne: 30 avr. 2021

Pages: 136 - 148

DOI: https://doi.org/10.2478/pomr-2021-0013

Mots clésclustering algorithm, abnormal route, DBSCAN, Feature trajectory extraction, fitting analysis

© 2021 Xiaofeng Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
clustering algorithm, abnormal route, DBSCAN, Feature trajectory extraction, fitting analysis