AIS Data Pre-Processing for Trajectory Clustering Data Preparation

Hartawan, I Putu Noven; Widyantara, I Made Oka; Karyawati, Anak Agung Istri Ngurah Eka; Er, Ngurah Indra; Artana, Ketut Buda; Sastra, Nyoman Putra

doi:10.1109/icares53960.2021.9665187

Cited by 6 publications

(1 citation statement)

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“…Data pre-processing is a crucial phase in data mining, and it also applies to vessel trajectory clustering [13]. The most time-consuming phase in data mining is data-preparation, which will take longer than the main data mining process itself.…”

Section: Introductionmentioning

confidence: 99%

Automatic identification system-based trajectory clustering framework to identify vessel movement pattern

Widyantara

Hartawan²,

Karyawati³

et al. 2023

IJ-AI

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<span lang="EN-US">Automatic identification system (AIS) is a vessel radio navigation equipment that has been determined by international maritime organization (IMO). Historical AIS data can be utilized for anomaly detection, trajectory prediction, and vessel trajectory planning. These benefits can be achieved by identifying the vessel's trajectory pattern through trajectory clustering. However, more effort is needed in trajectory clustering using AIS data due to their large volume and the significant number of deficiencies. In addition, trajectory clustering cannot be directly applied to trajectory data, which also applies to vessel trajectory. Therefore, we propose a trajectory clustering framework by combining douglas peucker (DP), longest common subsequence (LCSS), multi-dimensional scaling (MDS), and density-based spatial clustering of applications with noise (DBSCAN). Our experiments, carried out with AIS data for the Lombok Strait, Indonesia, showed that the trajectory compression with DP significantly accelerates the similarity measurement process. Moreover, we found that the LCSS is the optimal algorithm for similarity measurement of vessel trajectories based on AIS data. We also applied the right combination of MDS and DBSCAN in density-based clustering. The proposed framework can distinguish trajectoriess in different directions, identify the noise, and produce good quality clusters in relatively fast total processing time.</span>

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Section: Introductionmentioning

confidence: 99%