Ship trajectory clustering is one of the main methods for mining ship feature trajectories based on AIS data. However, there are two main problems in trajectory clustering: First, the clustering algorithm itself has the problems of difficult to determine the parameters and poor noise recognition ability; second, the trajectory similarity metric, most of the measurements are only similarity metrics for the ship’s position, and do not take into account the other dimensional information of the ship’s trajectory. In order to solve these problems, this paper proposes a fast adaptive density clustering method for ship trajectories, which integrally considers multiple attributes of ship position, heading and speed to construct similarity metrics between ship trajectories; introduces Silhouette Coefficient (SC) and Davies-Bouldin Index (DBI) The DBTCAN algorithm is constructed to evaluate the comprehensive CMI index, which in turn realizes the adaptive selection of clustering parameters. An example study was conducted using AIS data of real waters, and the results show that the method can adaptively cluster ship trajectories to match the traffic situation of real waters.


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    Title :

    Adaptive Density Ship Trajectory Clustering Based on AIS data


    Additional title:

    Advances in Engineering res



    Conference:

    International Symposium on Traffic Transportation and Civil Architecture ; 2024 ; Tianjin, China June 21, 2024 - June 23, 2024



    Publication date :

    2024-09-24


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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