Authors:
Dogan Altan
1
;
Dusica Marijan
1
and
Tetyana Kholodna
2
Affiliations:
1
Simula Research Laboratory, Oslo, Norway
;
2
Navtor AS, Egersund, Norway
Keyword(s):
Vessel Traffic Prediction, Automatic Identification System, Historical Density, Wave Features, Tailored Features.
Abstract:
Sea traffic is fundamental information that needs to be considered while planning vessel operations to enhance navigational safety and operational efficiency. Therefore, several environmental constraints, such as weather and traffic conditions, must be taken into account to minimize delays caused by vessel traffic and improve safety by decreasing collision risks. In this paper, we address the vessel traffic prediction problem, which tackles predicting vessel traffic for ships using several sources of information. We propose a vessel traffic prediction method that processes information obtained from different sources indicating historical traffic and wave conditions for vessels. The proposed method consists of three models processing different types of features and fuses the outputs of these models for the vessel traffic prediction problem. We evaluate the proposed method on real-world historical vessel trajectories and report its performance by providing a comparison with other basel
ines. The experimental results indicate that our proposed method provides promising results for predicting vessel traffic with a mean squared error of 0.325.
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