Trajectory Similarity Analysis with the Weight of Direction and <i>k</i>-Neighborhood for AIS Data

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...

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Autores principales:	Pin Nie, Zhenjie Chen, Nan Xia, Qiuhao Huang, Feixue Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	AIS data similarity analysis mapping <i>k</i>-neighborhood movement direction Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/f24da519de304fcd86bf6e3bfae7f98a
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id	oai:doaj.org-article:f24da519de304fcd86bf6e3bfae7f98a
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spelling	oai:doaj.org-article:f24da519de304fcd86bf6e3bfae7f98a2021-11-25T17:53:02ZTrajectory Similarity Analysis with the Weight of Direction and <i>k</i>-Neighborhood for AIS Data10.3390/ijgi101107572220-9964https://doaj.org/article/f24da519de304fcd86bf6e3bfae7f98a2021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/757https://doaj.org/toc/2220-9964Automatic 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 <i>k</i>-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.Pin NieZhenjie ChenNan XiaQiuhao HuangFeixue LiMDPI AGarticleAIS datasimilarity analysismapping<i>k</i>-neighborhoodmovement directionGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 757, p 757 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	AIS data similarity analysis mapping <i>k</i>-neighborhood movement direction Geography (General) G1-922
spellingShingle	AIS data similarity analysis mapping <i>k</i>-neighborhood movement direction Geography (General) G1-922 Pin Nie Zhenjie Chen Nan Xia Qiuhao Huang Feixue Li Trajectory Similarity Analysis with the Weight of Direction and <i>k</i>-Neighborhood for AIS Data
description	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 <i>k</i>-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.
format	article
author	Pin Nie Zhenjie Chen Nan Xia Qiuhao Huang Feixue Li
author_facet	Pin Nie Zhenjie Chen Nan Xia Qiuhao Huang Feixue Li
author_sort	Pin Nie
title	Trajectory Similarity Analysis with the Weight of Direction and <i>k</i>-Neighborhood for AIS Data
title_short	Trajectory Similarity Analysis with the Weight of Direction and <i>k</i>-Neighborhood for AIS Data
title_full	Trajectory Similarity Analysis with the Weight of Direction and <i>k</i>-Neighborhood for AIS Data
title_fullStr	Trajectory Similarity Analysis with the Weight of Direction and <i>k</i>-Neighborhood for AIS Data
title_full_unstemmed	Trajectory Similarity Analysis with the Weight of Direction and <i>k</i>-Neighborhood for AIS Data
title_sort	trajectory similarity analysis with the weight of direction and <i>k</i>-neighborhood for ais data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f24da519de304fcd86bf6e3bfae7f98a
work_keys_str_mv	AT pinnie trajectorysimilarityanalysiswiththeweightofdirectionandikineighborhoodforaisdata AT zhenjiechen trajectorysimilarityanalysiswiththeweightofdirectionandikineighborhoodforaisdata AT nanxia trajectorysimilarityanalysiswiththeweightofdirectionandikineighborhoodforaisdata AT qiuhaohuang trajectorysimilarityanalysiswiththeweightofdirectionandikineighborhoodforaisdata AT feixueli trajectorysimilarityanalysiswiththeweightofdirectionandikineighborhoodforaisdata
_version_	1718411898040352768

Trajectory Similarity Analysis with the Weight of Direction and <i>k</i>-Neighborhood for AIS Data

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