A novel algorithm for ship characteristic points extraction based on density clustering

Abstract

With the widespread application of ship Automatic Identification System (AIS) in maritime operations, a large number of ship trajectories become available. This study aims to improve the safety of ships navigating through densely trafficked areas and address the challenge of sufficient data exploration while fully describing the traffic conditions in these waters. To achieve this objective, traffic flow information is extracted from AIS data collected in Zhoushan waters. A combination of multi-algorithms is employed to extract the traffic flow frame, specifically, the Douglas-Peucker compression algorithm and trajectory intersection algorithm are utilised to identify the characteristic points of ship trajectories. Subsequently, a density clustering algorithm is applied to extract the three types of characteristic points: compressed trajectory points, intersection points, and ship position points, facilitating data mining efforts. The resulting initial traffic flow characteristic points are then subject to weighted fusion, followed by image superposition processing to create an overlapping map of the ship trajectories. This process culminates in the generation of a traffic flow frame for the region. The framework integrates various track characteristic points, offering insights into the distribution of essential routes in the vicinity waters, thereby providing a comprehensive depiction of ship traffic flow patterns. The proposed framework can be applied to the route planning and serve as a reference to the maritime authorities when selecting recommended shipping lanes.

KEYWORDS:

• Ship traffic flow; characteristic points; data mining; machine learning; automatic identification system

Acknowledgment

The author would like to express their gratitude to all individuals involved in the research for preserving maritime traffic routes and promoting their harmonisation with offshore wind farms.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by The Fundamental Research Funds for the Central Universities [grant number 3132023153] and China Environment and Zoology Protection for Offshore Oil and Ocean Foundation [grant number CF-MEEC-TR-2023-8].

Notes on contributors

Weifeng Li

Weifeng Li received his B.S. and M.S. degrees from the Dalian Maritime University, China. He is an Associate Professor in Navigation College, Dalian Maritime University, a Master's supervisor, and he mainly engaged in intelligent collision avoidance of ships, ship path planning.

Haoda Zhang

Haoda Zhang received his B.S. degree from the Dalian Maritime University, China, in 2020. He is a graduate student in Navigation College, Dalian Maritime University. His main research interests are ship trajectory extraction.

Siming Fang

Siming Fang received his B.S. degree from the Jimei University, China, in 2018. He is a PhD candidate in Navigation College, Dalian Maritime University. His main research interests are maritime safety, human evacuation.