Adaptive Density Ship Trajectory Clustering Based on AIS data

DOI

10.2991/978-94-6463-514-0_19How to use a DOI?

Keywords

AIS data; trajectory clustering; similarity measurement; adaptive

Abstract

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.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Changsheng Wu
AU  - Shuchang He
PY  - 2024
DA  - 2024/09/28
TI  - Adaptive Density Ship Trajectory Clustering Based on AIS data
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 172
EP  - 181
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_19
DO  - 10.2991/978-94-6463-514-0_19
ID  - Wu2024
ER  -