A novel method for ship trajectory clustering

Shen, Helong; Tang, Huang; Yin, Yixin

doi:10.1016/j.ijnaoe.2022.100474

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…Measuring the similarity [9] between trajectories is the basis for implementing clustering, which requires the distance function to measure the similarity between two trajectories and thus determine whether they should be assigned to the same cluster. In practical applications, different similarity measurement methods will have a significant impact on the clustering results.…”

Section: Similarity Measurementioning

confidence: 99%

Research on an improved ship trajectory clustering method

Jiang,

Yan

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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Aiming at the problem that the trajectory direction cannot be distinguished from the historical data of ship Automatic Identification System (AIS) in the traditional clustering process, a DBSCAN clustering method based on the combination of improved Euclidean distance and cosine similarity is proposed. To measure the differences between arbitrary trajectory clusters in different directions, the improved DP algorithm is used to quickly and accurately extract the ship trajectory feature points, and maintain a high similarity with the original trajectory. The historical AIS data of the waterway near Port area of Zhoushan was selected to analyze and verify the algorithm. The results show that the proposed method can achieve the expected clustering effect, and the results are consistent with the actual situation, which has certain reference value in channel planning, navigation forecast, ship dynamic monitoring and other aspects.

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Section: Similarity Measurementioning

confidence: 99%

Research on an improved ship trajectory clustering method

Jiang,

Yan

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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“…Zhao et al [12] proposed a method based on Douglas Pucker (DP) compression and improved adaptive DBSCAN to improve the clustering performance of ship trajectory data characterized by large data volume 3 and distribution complexity, which demonstrated excellent performance in both time and quality. Shen et al [13] addressed the issue of traditional clustering methods being unable to distinguish trajectories in opposite directions. They used cosine Hausdorff distance to construct a similarity function, adopted an adaptive compression ratio-based trajectory simplification method to simplify trajectories, and finally clustered trajectories using the DBSCAN algorithm.…”

Section: Trajectory Clusteringmentioning

confidence: 99%

Ship collision avoidance decision-making research in coastal waters considering uncertainty of target ships

Gao,

Zhang

2024

Brodogradnja

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Ship collision avoidance has always been a concern and it is crucial for achieving safe navigation of ships at sea. There are many studies on ship collision avoidance in open water, but less attention on coastal waters considering the uncertainty of target ships due to the complexity of the environment and traffic flow. In this paper, collision avoidance decision-making research in coastal waters considering the uncertainty of target ships was proposed. Firstly, accurate ship trajectories are obtained by preprocessing the raw Automatic Identification System (AIS) data. Subsequently, the processed trajectories are clustered using the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm and Hausdorff distance, acquiring a dataset for trajectory prediction of target ships. Then, a mixed Gaussian model is utilized to calculate the prior probability distribution of the prediction model, thus establishing a trajectory prediction model that considers the uncertainty of the target ship. Finally, ship maneuverability is simulated using the Mathematical Model Group (MMG) and Proportion Integration Differentiation (PID) models, and a collision avoidance decision-making model for ships is constructed. The proposed algorithm has been tested and verified in a case study. The results show that the approach effectively predicts the trajectory of the target ship and facilitates informed collision avoidance decision-making.

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“…Agrawal et al (1993) used Euclidean distance to calculate the similarity between global trajectory points to measure similar users. Text similarity measurement methods, such as cosine (Shen et al, 2022) and Jaccard (Bouros et al, 2012) distance are also used to calculate the similarity between trajectories. However, these distance measures did not take into account the temporal attributes of trajectories and cannot fully represent trajectory features.…”

Section: Rel Ated Workmentioning

confidence: 99%

A novel trajectory similarity measurement method based on node‐sequence hierarchical digraph

Fan,

Wang,

Wang

et al. 2023

Transactions in GIS

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Trajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node‐Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time‐Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T‐Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%.

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A novel method for ship trajectory clustering

Cited by 9 publications

References 17 publications

Research on an improved ship trajectory clustering method

Research on an improved ship trajectory clustering method

Ship collision avoidance decision-making research in coastal waters considering uncertainty of target ships

A novel trajectory similarity measurement method based on node‐sequence hierarchical digraph

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