The Study of Fishing Vessel Behavior Identification Based on AIS Data: A Case Study of the East China Sea

Abstract

The goal of this paper is to strengthen the supervision of fishing behavior in the East China Sea and effectively ensure the sustainable development of fishery resources. Based on AIS data, this paper analyzes three types of fishing boats (purse seine operation, gill net operation and trawl operation) and uses the cubic spline interpolation algorithm to optimize the ship trajectory and construct high-dimensional features. It proposes a new coding method for fishing boat trajectory sequences. This method uses the Geohash algorithm to divide the East China Sea into grids and generate corresponding numbers. Then, the ship trajectory is mapped to the grid, the fishing boat trajectory points are associated with the divided grid, and the ship trajectory ID is extracted from the corresponding grid. The extracted complete trajectory sequence passes through the CBOW (continuous bag of words) model, and the correlation of trajectory points is fully learned. Finally, the fishing boat trajectory is converted from coordinate sequence to trajectory vector, and the processed trajectory sequence is trained by the LightGBM algorithm. In order to obtain the optimal classification effect, the optimal superparameter combination is selected. We put forward a LightGBM algorithm based on the Bayesian optimization algorithm, and obtained the classification results of three kinds of fishing boats. The final result was evaluated using the F1_score. Experimental results show that the F1_score trained with the proposed trajectory vectorization method is the highest, with a training accuracy of 0.925. Compared to XgBoost and CatBoost, the F1_score increased by 1.8% and 1.2%, respectively. The results show that this algorithm demonstrates strong applicability and effectiveness in fishery area evaluations and is significant for strengthening fishery resource management.

Improving the Maritime Traffic Evaluation with the Course and Speed Model

“…However, the scope of movement for maritime transport is considerably broader than that of road traffic, rendering the same survey method inadequate owing to the significant restrictions on observation points at sea. Therefore, the IMO mandates that all passenger ships, including domestic cargo ships of 500 tons or more, irrespective of their size, and international service ships of 300 tons or more, must be equipped with an installed AIS [4]. Additionally, domestic vessels are required to install and operate a vessel pass (V-Pass) in compliance with the Fishing Vessels Act.…”

Recognizing Instantaneous Group Patterns in Vessel Trajectory Data: A Snapshot Perspective

“…Maritime administrators primarily monitor ship navigation through the real-time information provided by the Automatic Identification System (AIS). Recently, AIS data have been extensively used for, e.g., anomaly detection [2][3][4], behavior analysis [5][6][7], and maritime traffic monitoring [8][9][10]. Nevertheless, the large amount of trajectory data leads to difficulties in analyzing and managing vessel traffic patterns, and complexities in marine traffic environments may increase potential risks and their environmental impact [1].…”