Xin, X, Liu, K, Loughney, S, Wang, J, Li, H and Yang, Z (2023) Graph-based ship traffic partitioning for intelligent maritime surveillance in complex port waters. Expert Systems with Applications, 231. ISSN 0957-4174

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Abstract

Maritime Situational Awareness (MSA) is a critical component of intelligent maritime traffic surveillance. However, it becomes increasingly challenging to gain MSA accurately given the growing complexity of ship traffic patterns due to multi-ship interactions possibly involving classical manned ships and emerging autonomous ships. This study proposes a new traffic partitioning methodology to realise the optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management. First, a composite similarity measure is designed using a probabilistic conflict detection approach and a newly formulated maritime traffic route network learned through maritime knowledge mining. Then, an extended graph-based clustering framework is used to produce balanced traffic clusters with high intra-connections but low inter-connections. The proposed methodology is thoroughly demonstrated and tested using Automatic Identification System (AIS) trajectory data in the Ningbo-Zhoushan Port. The experimental results show that the proposed methodology 1) has effective performance in decomposing the traffic complexity, 2) can assist in identifying high-risk/density traffic clusters, and 3) is sufficiently generic to handle various traffic scenarios in complex geographical waters. Therefore, this study makes significant contributions to intelligent maritime surveillance and provides a theoretical foundation for promoting maritime anti-collision risk management for the future mixed traffic of both manned and autonomous ships.

Item Type:	Article
Uncontrolled Keywords:	01 Mathematical Sciences; 08 Information and Computing Sciences; 09 Engineering; Artificial Intelligence & Image Processing
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Elsevier
SWORD Depositor:	A Symplectic
Date Deposited:	29 Jun 2023 09:33
Last Modified:	29 Jun 2023 09:45
DOI or ID number:	10.1016/j.eswa.2023.120825
URI:	https://researchonline.ljmu.ac.uk/id/eprint/20119

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