Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Wang, Lianhui; Chen, Pengfei; Chen, Linying; Mou, Junmin

doi:10.3390/jmse9060566

Cited by 82 publications

(45 citation statements)

References 29 publications

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“…It calculates the maximum value of the shortest distance from a point on one trajectory to all points on the other trajectory. Wang et al [39] found that the Hausdorff distance has an optimal effect when measuring the shape similarity between trajectories. However, as the Hausdorff distance must calculate the minimum value of the distance between a point and all points of the other trajectory, and does not consider the time sequence, the calculation efficiency is low and cannot identify the direction of motion.…”

Section: Related Researchmentioning

confidence: 99%

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

Nie

Chen

Xia

et al. 2021

IJGI

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Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.

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Section: Related Researchmentioning

confidence: 99%

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

Nie

Chen

Xia

et al. 2021

IJGI

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“…To begin, Wang et al and Lee et al have researched maritime traffic route extraction. In the study of Wang et al, the Douglas-Peucker algorithm was used to simplify the trajectory of the ship, and the hierarchical trajectory was clustered using Hierarchical DBSCAN (HDBSCAN) [17]. The study by Lee et al extracted density-based routes by analyzing 90% of maritime traffic with kernel density estimation (KDE) based on a geographic information system (GIS).…”

Section: Introductionmentioning

confidence: 99%

Extracting the Maritime Traffic Route in Korea Based on Probabilistic Approach Using Automatic Identification System Big Data

Lee

Cho

2022

Applied Sciences

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To protect the environment around the world, we are actively developing ecofriendly energy. Offshore wind farm generation installed in the sea is extremely large among various energies, and friction with ships occurs regularly. Other than the traffic designated area and the traffic separate scheme, traffic routes in other sea areas are not protected in Korea. Furthermore, due to increased cargo volume and ship size, there is a risk of collisions with marine facilities and marine pollution. In this study, maritime safety traffic routes that must be preserved are created to ensure the safety of maritime traffic and to prevent accidents with ecofriendly energy projects. To construct maritime traffic routes, the analysis area is divided, and ships are classified using big data. These data are used to estimate density, and 50% maritime traffic is chosen. This result is obtained by categorizing the main route, inner branch route, and outer branch route. The Korean maritime traffic route is constructed, and the width of the route is indicated. Furthermore, this route can be applied as a navigation route for maritime autonomous surface ships.

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“…Accordingly, the DBSCAN algorithm has better applicability than other algorithms because it can identify the trajectories of different shapes and has the ability to identify anomalies [25,26]. Lee et al [27] segmented ship trajectories and clustered the sub-trajectories using the DBSCAN algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid-Clustering Model of Ship Trajectories for Maritime Traffic Patterns Analysis in Port Area

Lei

Zhang

et al. 2022

JMSE

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A hybrid-clustering model is presented for the probabilistic characterization of ship traffic and anomaly detection. A hybrid clustering model was proposed to increase the efficiency of trajectory clustering in the port area and analyze the maritime traffic patterns in port. The model identified dissimilarities between trajectories based on characteristics, using K-Means and the density-based spatial clustering of applications with noise algorithm (DBSCAN). Firstly, the ship’s trajectory characteristics are constructed based on real ship trajectories considering static characteristics and dynamic characteristics of ship trajectories to calculate the characteristic dissimilarity between trajectories. Simultaneously, the spatial dissimilarity could be quantified using the Hausdorff algorithm. Then, the ship trajectory is clustered initially based on the departure and destination characteristics using K-Means algorithms to obtain various sub-trajectories. However, there are still different types of trajectories in each sub-trajectory. Thus, the DBSCAN algorithm is adopted to cluster the sub-trajectory based on the analysis of the different trajectory characteristics. Finally, the proposed model is applied to the characterization of the Zhanjiang Port, and the results show that the hybrid-clustering method can effectively cluster ship trajectory and present probabilistic characterization of ship traffic and anomaly detection. This lays a solid theoretical foundation for the supervision and risk control of intelligent ships.

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Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

Cited by 82 publications

References 29 publications

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

Extracting the Maritime Traffic Route in Korea Based on Probabilistic Approach Using Automatic Identification System Big Data

A Hybrid-Clustering Model of Ship Trajectories for Maritime Traffic Patterns Analysis in Port Area

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