{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T06:46:57Z","timestamp":1772261217137,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52331012"],"award-info":[{"award-number":["52331012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52102397"],"award-info":[{"award-number":["52102397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52472347"],"award-info":[{"award-number":["52472347"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KLGLIT2024ZD001"],"award-info":[{"award-number":["KLGLIT2024ZD001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["JXINTROB-2024-201"],"award-info":[{"award-number":["JXINTROB-2024-201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of Chongqing Key Laboratory of Green Logistics Intelligent Technology","award":["52331012"],"award-info":[{"award-number":["52331012"]}]},{"name":"Open Fund of Chongqing Key Laboratory of Green Logistics Intelligent Technology","award":["52102397"],"award-info":[{"award-number":["52102397"]}]},{"name":"Open Fund of Chongqing Key Laboratory of Green Logistics Intelligent Technology","award":["52472347"],"award-info":[{"award-number":["52472347"]}]},{"name":"Open Fund of Chongqing Key Laboratory of Green Logistics Intelligent Technology","award":["KLGLIT2024ZD001"],"award-info":[{"award-number":["KLGLIT2024ZD001"]}]},{"name":"Open Fund of Chongqing Key Laboratory of Green Logistics Intelligent Technology","award":["JXINTROB-2024-201"],"award-info":[{"award-number":["JXINTROB-2024-201"]}]},{"name":"Open Fund of Jiangxi Key Laboratory of Intelligent Robot","award":["52331012"],"award-info":[{"award-number":["52331012"]}]},{"name":"Open Fund of Jiangxi Key Laboratory of Intelligent Robot","award":["52102397"],"award-info":[{"award-number":["52102397"]}]},{"name":"Open Fund of Jiangxi Key Laboratory of Intelligent Robot","award":["52472347"],"award-info":[{"award-number":["52472347"]}]},{"name":"Open Fund of Jiangxi Key Laboratory of Intelligent Robot","award":["KLGLIT2024ZD001"],"award-info":[{"award-number":["KLGLIT2024ZD001"]}]},{"name":"Open Fund of Jiangxi Key Laboratory of Intelligent Robot","award":["JXINTROB-2024-201"],"award-info":[{"award-number":["JXINTROB-2024-201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMSE"],"abstract":"<jats:p>Intelligent ships are a key focus for the future development of maritime transportation, relying on efficient decision-making and autonomous control within complex environments. To enhance the perception, prediction, and decision-making capabilities of these ships, the present study proposes a novel approach for constructing a time-series knowledge graph, utilizing real-time Automatic Identification System (AIS) data analyzed via a sliding window technique. By integrating advanced technologies such as knowledge extraction, representation learning, and semantic fusion, both static and dynamic navigational data are systematically unified within the knowledge graph. The study specifically targets the extraction and modeling of critical events, including variations in ship speed, course changes, vessel encounters, and port entries and exits. To evaluate the urgency of encounters, mathematical algorithms are applied to the Distance to Closest Point of Approach (DCPA) and Time to Closest Point of Approach (TCPA) metrics. Furthermore, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is employed to identify suitable docking berths. Additionally, multi-source meteorological data are integrated with ship dynamic data, providing a more comprehensive representation of the maritime environment. The resulting knowledge system effectively combines ship attributes, navigational status, event relationships, and environmental factors, thereby offering a robust framework for supporting intelligent ship operations.<\/jats:p>","DOI":"10.3390\/jmse12122333","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T10:54:20Z","timestamp":1734605660000},"page":"2333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Maritime Traffic Knowledge Discovery via Knowledge Graph Theory"],"prefix":"10.3390","volume":"12","author":[{"given":"Shibo","family":"Li","sequence":"first","affiliation":[{"name":"Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Jiajun","family":"Xu","sequence":"additional","affiliation":[{"name":"Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8959-5108","authenticated-orcid":false,"given":"Xinqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China"},{"name":"Chongqing Key Laboratory of Green Logistics Intelligent Technology, Chongqing Jiaotong University, Chongqing 400074, China"},{"name":"Jiangxi Key Laboratory of Intelligent Robot, Nanchang 330019, China"}]},{"given":"Yajie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Yiwen","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5055-6347","authenticated-orcid":false,"given":"Octavian","family":"Postolache","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), North Tower, 10th Floor, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal"},{"name":"Department of Information Science and Technology, Iscte\u2014Instituto Universit\u00e1rio de Lisboa, Av. das For\u00e7as Armadas, 1649-026 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109554","DOI":"10.1016\/j.ress.2023.109554","article-title":"Multi-scale collision risk estimation for maritime traffic in complex port waters","volume":"240","author":"Xin","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hwang, T., and Youn, I.H. (2022). Collision Risk Situation Clustering to Design Collision Avoidance Algorithms for Maritime Autonomous Surface Ships. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10101381"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"119368","DOI":"10.1016\/j.oceaneng.2024.119368","article-title":"Ship visual trajectory exploitation via an ensemble instance segmentation framework","volume":"313","author":"Chen","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114205","DOI":"10.1016\/j.oceaneng.2023.114205","article-title":"A real-time multi-ship collision avoidance decision-making system for autonomous ships considering ship motion uncertainty","volume":"278","author":"Zhang","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106482","DOI":"10.1016\/j.ocecoaman.2023.106482","article-title":"Research on ship collision avoidance path planning based on modified potential field ant colony algorithm","volume":"235","author":"Gao","year":"2023","journal-title":"Ocean Coast. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/s12599-020-00661-0","article-title":"Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm","volume":"62","author":"Filipiak","year":"2020","journal-title":"Bus. Inf. Syst. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"47997","DOI":"10.1109\/ACCESS.2022.3172308","article-title":"Ship Navigation Behavior Prediction Based on AIS Data","volume":"10","author":"Liu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_8","first-page":"5961","article-title":"WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory","volume":"59","author":"Kim","year":"2023","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Huang, I.L., Lee, M.C., Nieh, C.Y., and Huang, J.C. (2024). Ship Classification Based on AIS Data and Machine Learning Methods. Electronics, 13.","DOI":"10.3390\/electronics13010098"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106836","DOI":"10.1016\/j.ocecoaman.2023.106836","article-title":"Monitoring and evaluation of ship operation congestion status at container ports based on AIS data","volume":"245","author":"Chen","year":"2023","journal-title":"Ocean Coast. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1109\/TAES.2021.3083466","article-title":"Malicious AIS Spoofing and Abnormal Stealth Deviations: A Comprehensive Statistical Framework for Maritime Anomaly Detection","volume":"57","author":"Braca","year":"2021","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.ssci.2019.09.018","article-title":"Ship collision avoidance methods: State-of-the-art","volume":"121","author":"Huang","year":"2020","journal-title":"Saf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.joes.2021.03.001","article-title":"Ship behavior prediction via trajectory extraction-based clustering for maritime situation awareness","volume":"7","author":"Murray","year":"2022","journal-title":"J. Ocean Eng. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"tdac078","DOI":"10.1093\/tse\/tdac078","article-title":"Event-triggered adaptive finite time trajectory tracking control for an underactuated vessel considering unknown time-varying disturbances","volume":"5","author":"Wang","year":"2023","journal-title":"Transp. Saf. Environ."},{"key":"ref_15","first-page":"894","article-title":"Quantitative calculation method of the collision risk or collision avoidance in ship navigation using the CPA and ship domain","volume":"8","author":"Ha","year":"2021","journal-title":"J. Comput. Des. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"117704","DOI":"10.1016\/j.oceaneng.2024.117704","article-title":"Localized advanced ship predictor for maritime situation awareness with ship close encounter","volume":"306","author":"Wang","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106686","DOI":"10.1016\/j.engappai.2023.106686","article-title":"Orientation-aware ship detection via a rotation feature decoupling supported deep learning approach","volume":"125","author":"Chen","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"28500","DOI":"10.1109\/JSEN.2023.3323322","article-title":"SARGAN: A Novel SAR Image Generation Method for SAR Ship Detection Task","volume":"23","author":"Ju","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3502","DOI":"10.1109\/JSTARS.2024.3352098","article-title":"Ellipse Polar Encoding for Oriented SAR Ship Detection","volume":"17","author":"Liu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11107","DOI":"10.1109\/TITS.2023.3281547","article-title":"A Novel Ship Speed and Heading Estimation Approach Using Radar Sequential Images","volume":"24","author":"Xu","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111630","DOI":"10.1016\/j.measurement.2022.111630","article-title":"A new multi-sensor fusion approach for integrated ship motion perception in inland waterways","volume":"200","author":"Wu","year":"2022","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1007\/s40815-020-00963-1","article-title":"Practical Moving Target Detection in Maritime Environments Using Fuzzy Multi-sensor Data Fusion","volume":"23","author":"Liu","year":"2021","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"21367","DOI":"10.1109\/TITS.2024.3465234","article-title":"STMGF-Net: A Spatiotemporal Multi-Graph Fusion Network for Vessel Trajectory Forecasting in Intelligent Maritime Navigation","volume":"25","author":"Jiang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110799","DOI":"10.1016\/j.knosys.2023.110799","article-title":"Adaptive multi-source data fusion vessel trajectory prediction model for intelligent maritime traffic","volume":"277","author":"Xiao","year":"2023","journal-title":"Knowl. -Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"tdad014","DOI":"10.1093\/tse\/tdad014","article-title":"Examining the characteristics between time and distance gaps of secondary crashes","volume":"6","author":"Liu","year":"2023","journal-title":"Transp. Saf. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"127044","DOI":"10.1016\/j.neucom.2023.127044","article-title":"Relphormer: Relational Graph Transformer for Knowledge Graph Representations","volume":"566","author":"Bi","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13071","DOI":"10.1007\/s10462-023-10465-9","article-title":"Knowledge Graphs: Opportunities and Challenges","volume":"56","author":"Peng","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.knosys.2018.10.008","article-title":"Knowledge graph embedding with concepts","volume":"164","author":"Guan","year":"2019","journal-title":"Knowl. -Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.neucom.2018.08.070","article-title":"Representation learning over multiple knowledge graphs for knowledge graphs alignment","volume":"320","author":"Liu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hu, J.L., Zhou, W.X., Zheng, P.J., and Liu, G.Y. (2024). A Novel Approach for the Analysis of Ship Pollution Accidents Using Knowledge Graph. Sustainability, 16.","DOI":"10.3390\/su16135296"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xie, C.X., Zhang, L.M., and Zhong, Z.G. (2023). A Novel Method for Constructing Spatiotemporal Knowledge Graph for Maritime Ship Activities. Electronics, 12.","DOI":"10.3390\/electronics12153205"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Y.T., Xu, R.Q., Lu, W.P., Mayer, W., Ning, D., Duan, Y.C., Zeng, X., and Feng, Z.W. (2023). Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management. Appl. Sci., 13.","DOI":"10.3390\/app13169393"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/3522586","article-title":"Defining a Knowledge Graph Development Process Through a Systematic Review","volume":"32","author":"Tamasauskaite","year":"2023","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"ref_34","first-page":"26","article-title":"Port mergers: Why not Los Angeles and Long Beach?","volume":"26","author":"Knatz","year":"2018","journal-title":"Res. Transp. Bus. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103570","DOI":"10.1016\/j.tre.2024.103570","article-title":"Data-driven approach for port resilience evaluation","volume":"186","author":"Gu","year":"2024","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20268","DOI":"10.1109\/TITS.2024.3461210","article-title":"AIS Data-Based Hybrid Predictor for Short-Term Ship Trajectory Prediction Considering Uncertainties","volume":"25","author":"Wang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"12740","DOI":"10.1109\/JSEN.2024.3370605","article-title":"Research of AIS Data-Driven Ship Arrival Time at Anchorage Prediction","volume":"24","author":"Guan","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_38","first-page":"2332","article-title":"AISChain: Blockchain-Based AIS Data Platform With Dynamic Bloom Filter Tree","volume":"24","author":"Duan","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2709","DOI":"10.1109\/TDSC.2021.3069428","article-title":"Auth-AIS: Secure, Flexible, and Backward-Compatible Authentication of Vessels AIS Broadcasts","volume":"19","author":"Sciancalepore","year":"2022","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Brcko, T., and Luin, B. (2023). A Decision Support System Using Fuzzy Logic for Collision Avoidance in Multi-Vessel Situations at Sea. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11091819"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9532","DOI":"10.1007\/s11227-023-05826-8","article-title":"Exploring autoregression patterns for automatic vessel type classification","volume":"80","author":"Ferreira","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"125109","DOI":"10.1016\/j.eswa.2024.125109","article-title":"Real-time prediction and detection of contacts between vessels and facilities based on AIS: A multivariate time-series classification approach","volume":"257","author":"Li","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.tranpol.2022.09.010","article-title":"Prospects for improving shipping companies? profit margins by quantifying operational strategies and market focus approach through AIS data","volume":"128","author":"Peng","year":"2022","journal-title":"Transp. Policy"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zheng, J., Yan, D.W., Yan, M., Li, Y., and Zhao, Y.B. (2022). An Unscented Kalman Filter Online Identification Approach for a Nonlinear Ship Motion Model Using a Self-Navigation Test. Machines, 10.","DOI":"10.3390\/machines10050312"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"tdad018","DOI":"10.1093\/tse\/tdad018","article-title":"Research on the navigational risk of liquefied natural gas carriers in an inland river based on entropy: A cloud evaluation model","volume":"6","author":"Liu","year":"2023","journal-title":"Transp. Saf. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"110708","DOI":"10.1016\/j.oceaneng.2022.110708","article-title":"Motion primitives learning of ship-ship interaction patterns in encounter situations","volume":"247","author":"Jia","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111847","DOI":"10.1016\/j.oceaneng.2022.111847","article-title":"Fuzzy logic-based modeling method for regional multi-ship collision risk assessment considering impacts of ship crossing angle and navigational environment","volume":"259","author":"Shi","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.cor.2017.11.005","article-title":"A two-phase heuristic for an in-port ship routing problem with tank allocation","volume":"91","author":"Wang","year":"2018","journal-title":"Comput. Oper. Res."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Del Mondo, G., Peng, P., Gensel, J., Claramunt, C., and Lu, F. (2021). Leveraging Spatio-Temporal Graphs and Knowledge Graphs: Perspectives in the Field of Maritime Transportation. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080541"}],"container-title":["Journal of Marine Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-1312\/12\/12\/2333\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:55:54Z","timestamp":1760115354000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-1312\/12\/12\/2333"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":49,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["jmse12122333"],"URL":"https:\/\/doi.org\/10.3390\/jmse12122333","relation":{},"ISSN":["2077-1312"],"issn-type":[{"value":"2077-1312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,19]]}}}