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Search Results (5)

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Keywords = vessel traffic service operator (VTSO)

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19 pages, 6079 KiB  
Article
A Method for Enhancing the Traffic Situation Awareness of Vessel Traffic Service Operators by Identifying High Risk Ships in Complex Navigation Conditions
by Lei Zhang, Jiahao Ge, Floris Goerlandt, Lei Du, Tuowei Chen, Tingting Gu, Langxiong Gan and Xiaobin Li
J. Mar. Sci. Eng. 2025, 13(2), 379; https://doi.org/10.3390/jmse13020379 - 19 Feb 2025
Cited by 1 | Viewed by 681
Abstract
As ship traffic volumes increase and navigable waters become more complex, vessel traffic service operators (VTSOs) face growing challenges to effectively monitor marine traffic. To address the heavy reliance on human expertise in current ship supervision, we propose a method for quickly identifying [...] Read more.
As ship traffic volumes increase and navigable waters become more complex, vessel traffic service operators (VTSOs) face growing challenges to effectively monitor marine traffic. To address the heavy reliance on human expertise in current ship supervision, we propose a method for quickly identifying high risk ships to enhance the situational awareness of VTSOs in complex waters. First, the K-means clustering algorithm is improved using the Whale Optimization Algorithm (WOA) to adaptively cluster ships within a waterway, segmenting the traffic in the area into multiple ship clusters. Second, a ship cluster collision risk assessment model is developed to quantify the degree of collision risk for each ship cluster. Finally, a weighted directed complex network is constructed to identify high risk ships within each ship cluster. Experimental simulations show that the proposed WOA–K-means clustering algorithm outperforms other adaptive clustering algorithms in terms of computation speed and accuracy. The developed ship cluster collision risk assessment model can identify high risk ship clusters that require VTSO attention, and the weighted directed complex network model accurately identifies high risk ships. This approach can assist VTSOs in executing a comprehensive and targeted monitoring process encompassing macro, meso, and micro aspects, thus boosting the efficacy of ship oversight, and mitigating traffic hazards. Full article
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25 pages, 7123 KiB  
Article
Vessel Trajectory Prediction at Inner Harbor Based on Deep Learning Using AIS Data
by Gil-Ho Shin and Hyun Yang
J. Mar. Sci. Eng. 2024, 12(10), 1739; https://doi.org/10.3390/jmse12101739 - 2 Oct 2024
Cited by 3 | Viewed by 2731
Abstract
This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy [...] Read more.
This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy of predictions in complex port environments. Recurrent neural network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU models were developed, with LSTM delivering the highest performance. The primary scientific question of this study is how to reliably predict vessel trajectories under varying conditions in inner harbors. The results demonstrate that the proposed method not only improves the precision of predictions but also identifies critical areas where Vessel Traffic Service Operators (VTSOs) can better manage vessel movements. These findings contribute to safer and more efficient vessel traffic management in ports with high traffic density and complex navigational challenges. Full article
(This article belongs to the Special Issue Maritime Artificial Intelligence Convergence Research)
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18 pages, 5665 KiB  
Article
Development of Priority Index for Intelligent Vessel Traffic Monitoring System in Vessel Traffic Service Areas
by Lee-na Lee and Joo-sung Kim
Appl. Sci. 2022, 12(8), 3807; https://doi.org/10.3390/app12083807 - 9 Apr 2022
Cited by 10 | Viewed by 3005
Abstract
Recognizing dangerous situations in advance and determining priority is essential in vessel traffic surveillance. The traffic management priority is determined by the vessel traffic service operator (VTSO) employing the closest point of approach (CPA) and the time to CPA (TCPA) of the targets [...] Read more.
Recognizing dangerous situations in advance and determining priority is essential in vessel traffic surveillance. The traffic management priority is determined by the vessel traffic service operator (VTSO) employing the closest point of approach (CPA) and the time to CPA (TCPA) of the targets considering their current navigational data. Various environmental conditions influence CPA and TCPA, which affects the importance of surveillance. This study aims to support vessel traffic prioritization in the navigation surveillance of VTSO from the observer side. The vessel tracks were clustered based on density, and a priority index of the vessel surveillance was developed in the VTS area by reflecting regional navigation characteristics. Density-based spatial clustering of applications with noise (DBSCAN) was used for data clustering to classify the surveillance area. A fuzzy membership function was constructed based on the CPA and TCPA belonging to each cluster, and a dataset for determining priorities was constructed, yielding 17 clusters, fuzzy rules, and tables, with the priority index extracted for all vessel pairs to visualize the priority. The results indicated prior recognition of all dangerous situations. The proposed method facilitates vessel surveillance priority determination in high-density areas and predicts the risk in advance, thereby contributing to traffic management. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems)
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14 pages, 4606 KiB  
Article
Regional Collision Risk Prediction System at a Collision Area Considering Spatial Pattern
by Ho Namgung and Joo-Sung Kim
J. Mar. Sci. Eng. 2021, 9(12), 1365; https://doi.org/10.3390/jmse9121365 - 2 Dec 2021
Cited by 18 | Viewed by 2809
Abstract
To reduce the risk of collision in territorial sea areas, including trade ports and entry waterways, and to enhance the safety and efficiency of ship passage, the International Maritime Organization requires the governing body of every country to establish and operate a vessel [...] Read more.
To reduce the risk of collision in territorial sea areas, including trade ports and entry waterways, and to enhance the safety and efficiency of ship passage, the International Maritime Organization requires the governing body of every country to establish and operate a vessel traffic service (VTS). However, previous studies on risk prediction models did not consider the locations of near collisions and actual collisions and only employed a combined collision risk index in surveillance sea areas. In this study, we propose a regional collision risk prediction system for a collision area considering spatial patterns using a density-based spatial clustering of applications with noise (DBSCAN). Furthermore, a fuzzy inference system based on a near collision (FIS-NC) and long short-term memory (LSTM) is adopted to help a vessel traffic service operator (VTSO) make timely optimal decisions. In the local spatial pattern stage, the ship trajectory was determined by identifying the actual-collision and near-collision locations simultaneously. Finally, the system was developed by learning a sequence dataset from the extracted trajectory of the ship when a collision occurred. The proposed system can recommend an action faster than the fuzzy inference system based on the near-collision location. Therefore, using the developed system, a VTSO can quickly predict ship collision risk situations and make timely optimal decisions at dangerous surveillance sea areas. Full article
(This article belongs to the Section Ocean Engineering)
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17 pages, 7188 KiB  
Article
Collision Risk Assessment Support System for MASS RO and VTSO Support in Multi-Ship Environment of Vessel Traffic Service Area
by Yunja Yoo and Jin-Suk Lee
J. Mar. Sci. Eng. 2021, 9(10), 1143; https://doi.org/10.3390/jmse9101143 - 18 Oct 2021
Cited by 13 | Viewed by 3595
Abstract
The discussions by the International Maritime Organization for the introduction of a maritime autonomous surface ship (MASS) began in earnest. At the 27th ENAV meeting, the International Association of Marine Aids to Navigation and Lighthouse Authorities proposed the “sharing of a common operating [...] Read more.
The discussions by the International Maritime Organization for the introduction of a maritime autonomous surface ship (MASS) began in earnest. At the 27th ENAV meeting, the International Association of Marine Aids to Navigation and Lighthouse Authorities proposed the “sharing of a common operating picture for situational awareness of the waterway within vessel traffic service (VTS) environment” when developing a system to support MASS operation. Marine accidents caused by collisions on waterways still account for a high percentage of ship accidents that occur at sea, and many studies have investigated the risk of collision between ships. Collision risk assessment was primarily conducted in ship domain-based safety areas. This study evaluates the collision risk using the ship domain derived by the VTS operator (VTSO) and proposes a real-time collision risk assessment support system to improve the situational awareness of VTSOs and MASS remote operators (MASS ROs) regarding near-collision situations occurring in local waters. To evaluate the validity of the proposed system, a risk analysis was performed on near-collision scenarios at Busan Port. The results show that the distance to the closest point of approach (CPA), time to the CPA, and inter-ship distance converged within 0.5 nautical miles, 10 min, and 3 nautical miles, respectively. Full article
(This article belongs to the Section Ocean Engineering)
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