Abstract

Recognition of ship traffic patterns can provide insights into the rules of navigation, maneuvering, and collision avoidance for ships at sea. This is essential for ensuring safe navigation at sea and improving navigational efficiency. With the popularization of the Automatic Identification System (AIS), numerous studies utilized ship trajectories to identify maritime traffic patterns. However, the current research focuses on the spatiotemporal behavioral feature clustering of ship trajectory points or segments while lacking consideration for multiple factors that influence ship behavior, such as ship static and maritime geospatial features, resulting in insufficient precision in ship traffic pattern recognition. This study proposes a ship traffic pattern recognition method that considers multi-attribute trajectory similarity (STPMTS), which considers ship static feature, dynamic feature, port geospatial feature, as well as semantic relationships between these features. First, A ship trajectory reconstruction method based on grid compression was introduced to eliminate redundant data and enhance the efficiency of trajectory similarity measurements. Subsequently, to quantify the degree of similarity of ship trajectories, a trajectory similarity measurement method is proposed that combines ship static and dynamic information with port geospatial features. Furthermore, trajectory clustering with hierarchical methods was applied based on the trajectory similarity matrix for dividing trajectories into different clusters. The quality of the similarity measurement results was evaluated by quality criterion to recognize the optimal number of ship traffic patterns. Finally, the effectiveness of the proposed method was verified using actual port ship trajectory data from the Tianjin Port of China, ranging from September to November 2016. Compared with other methods, the proposed method exhibits significant advantages in identifying traffic patterns of ships entering and leaving the port in terms of geometric features, dynamic features, and adherence to navigation rules. This study could serve as an inspiration for a comprehensive exploration of maritime transportation knowledge from multiple perspectives.

Details

Title
An approach for traffic pattern recognition integration of ship AIS data and port geospatial features
Author
Li, Gaocai 1   VIAFID ORCID Logo  ; Zhang, Xinyu 1   VIAFID ORCID Logo  ; Jiang, Lingling 2 ; Wang, Chengbo 3   VIAFID ORCID Logo  ; Huang, Ruining 1 ; Liu, Zhensheng 1 

 Maritime Intelligent Transportation Research Team, Navigation College, Dalian Maritime University, Dalian, China 
 College of Environmental Science and Engineering, Dalian Maritime University, Dalian, China 
 Department of Automation, School of Information Science and Technology, University of Science and Technology of China, Hefei, China; Maritime Intelligent Transportation Research Team, Navigation College, Dalian Maritime University, Dalian, China 
Pages
2048-2075
Publication year
2024
Publication date
Dec 2024
Publisher
Taylor & Francis Ltd.
ISSN
10095020
e-ISSN
19935153
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3143099417
Copyright
© 2024 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.