Trajectory Similarity Analysis with the Weight of Direction and k-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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Bibliographic Details
Published in:ISPRS international journal of geo-information 2021-11, Vol.10 (11), p.757
Main Authors: Nie, Pin, Chen, Zhenjie, Xia, Nan, Huang, Qiuhao, Li, Feixue
Format: Article
Language:English
Subjects:
Accuracy
AIS data
Analysis
Data mining
Direction
k-neighborhood
mapping
Mathematical analysis
Methods
movement direction
Navigation
Neighborhoods
Ship accidents & safety
Similarity
similarity analysis
Traffic management
Trajectory analysis
Weight
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Summary: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 k-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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi10110757