A Method for Enhancing the Traffic Situation Awareness of Vessel Traffic Service Operators by Identifying High Risk Ships in Complex Navigation Conditions
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/2/379 |
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| author | Lei Zhang Jiahao Ge Floris Goerlandt Lei Du Tuowei Chen Tingting Gu Langxiong Gan Xiaobin Li |
| author_facet | Lei Zhang Jiahao Ge Floris Goerlandt Lei Du Tuowei Chen Tingting Gu Langxiong Gan Xiaobin Li |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ccd50d9a29f2403eb31728b8d757e8f5 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-ccd50d9a29f2403eb31728b8d757e8f52025-08-20T02:03:39ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-02-0113237910.3390/jmse13020379A Method for Enhancing the Traffic Situation Awareness of Vessel Traffic Service Operators by Identifying High Risk Ships in Complex Navigation ConditionsLei Zhang0Jiahao Ge1Floris Goerlandt2Lei Du3Tuowei Chen4Tingting Gu5Langxiong Gan6Xiaobin Li7School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430062, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430062, ChinaDepartment of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, NS B3H 4R2, CanadaSchool of Navigation, Wuhan University of Technology, Wuhan 430062, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430062, ChinaLaboratory and Equipment Management Service, Wuhan University of Technology, Wuhan 430062, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430062, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430062, ChinaAs 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.https://www.mdpi.com/2077-1312/13/2/379vessel traffic serviceWOA–K-meansship collisioncomplex networkrisk analysis |
| spellingShingle | Lei Zhang Jiahao Ge Floris Goerlandt Lei Du Tuowei Chen Tingting Gu Langxiong Gan Xiaobin Li A Method for Enhancing the Traffic Situation Awareness of Vessel Traffic Service Operators by Identifying High Risk Ships in Complex Navigation Conditions Journal of Marine Science and Engineering vessel traffic service WOA–K-means ship collision complex network risk analysis |
| title | A Method for Enhancing the Traffic Situation Awareness of Vessel Traffic Service Operators by Identifying High Risk Ships in Complex Navigation Conditions |
| title_full | A Method for Enhancing the Traffic Situation Awareness of Vessel Traffic Service Operators by Identifying High Risk Ships in Complex Navigation Conditions |
| title_fullStr | A Method for Enhancing the Traffic Situation Awareness of Vessel Traffic Service Operators by Identifying High Risk Ships in Complex Navigation Conditions |
| title_full_unstemmed | A Method for Enhancing the Traffic Situation Awareness of Vessel Traffic Service Operators by Identifying High Risk Ships in Complex Navigation Conditions |
| title_short | A Method for Enhancing the Traffic Situation Awareness of Vessel Traffic Service Operators by Identifying High Risk Ships in Complex Navigation Conditions |
| title_sort | method for enhancing the traffic situation awareness of vessel traffic service operators by identifying high risk ships in complex navigation conditions |
| topic | vessel traffic service WOA–K-means ship collision complex network risk analysis |
| url | https://www.mdpi.com/2077-1312/13/2/379 |
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