• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.212-219
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• 2023

Vessel Traffic Service (VTS) area presents a complex traffic pattern due to ships entering or leaving the port to utilize port facilities, as well as ships passing through the coastal area. To ensure safe and efficient management of maritime traffic, VTS operators continuously monitor and control vessels in real time. However, during periods of high traffic congestion, the workload of VTS operators increases, which can result in delayed or inadequate VTS services. Therefore, it would be beneficial to predict traffic congestion and congested areas to enable more efficient traffic control. Currently, such prediction relies on the experience of VTS operators. In this paper, we defined vessel traffic congestion from the perspective of a VTS operator. We proposed a method to generate traffic networks using historical navigational data and predict traffic congestion and congested areas. Experiments were performed to compare prediction results with real maritime data (Daesan port VTS) and examine whether the proposed method could support VTS operators.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.138-140
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• 2011

해상교통관제시스템의 발달로 과거에 비하여 해상교통의 안전과 항만의 효율성은 크게 향상되었다. 이러한 관제설비는 유기적으로 상호 연동 공유되어야 관제사에게 정보를 효과적으로 제공해 줄 수 있다. 관제장비의 증가는 여러 정보를 수집할 수 있다는 면에서는 긍정적이만 점차 다양해지는 장비로 관제사의 업무량도 증가하기 마련이다. 따라서 여러 관제장비를 상호 연동하고 통합하고 그러한 정보를 활용할 수 있는 시스템이 필요할 것이다. 본 연구에서는 관제사의 업무를 효과적으로 지원해 줄 수 있고 정보를 지속적으로 기록 관리하여 이 정보를 활용할 수 있는 선박안전종합관리시스템의 개발방안을 모색하여 보았다.

• Journal of Navigation and Port Research
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• v.42 no.4
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• pp.277-282
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• 2018

Recently, the Vessel Traffic Service (VTS) coverage has expanded to include coastal areas following the increased attention on vessel traffic safety. However, it has increased the workload on the VTS operators. In some cases, when the traffic volume increases sharply during the rush hour, the VTS operator may not be aware of the risks. Therefore, in this paper, we proposed a new method to recognize ship movement anomalies automatically to support the VTS operator's decision-making. The proposed method generated traffic pattern model without any category information using the unsupervised learning algorithm.. The anomaly score can be calculated by classification and comparison of the trained model. Finally, we reviewed the experimental results using a ship-handling simulator and the actual trajectory data to verify the feasibility of the proposed method.