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2024, 06, v.23 82-89
海上交通态势研究进展
基金项目(Foundation): 山东省自然科学基金“面向复杂通航系统的水上交通态势动态演化机理与监测研究”(项目编号:ZR2021QG022); 国家自然科学基金项目“基于数据场-云模型的感潮河段交通态势动态演化机理与监测预警研究”(项目编号:51909156)
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DOI:
摘要:

海上交通态势研究是船舶安全航行、高效管理的关键。针对海上交通态势研究的进展、趋势及现存问题,利用CiteSpace软件对2000-2023年的776篇相关文献进行了挖掘分析。结果表明,海上交通态势相关文献的发文量总体呈增长趋势,相关领域研究热点主要集中于船舶交通流预测和海上交通态势评估方面,研究前沿为AIS数据挖掘及船舶避碰等海上交通态势安全问题,而对船舶航行行为模式识别、海上交通态势的时空特征演变机理等关键问题还有待进一步研究。

Abstract:

The study of maritime traffic situation is the key to safe navigation and efficient management of ships. According to the progress, trend and existing problems of maritime traffic situation research, 776 relevant literature from 2000 to 2023 were mined and analyzed by CiteSpace software. The results show that the number of published documents related to maritime traffic situation shows an overall increasing trend, and the research hot-spots in relevant fields mainly focus on ship traffic flow prediction and maritime traffic situation assessment. The research frontiers include AIS data mining and ship collision avoidance and other maritime traffic situation safety issues. However, the key problems such as the pattern recognition of ship navigation behavior and the evolution mechanism of temporal and spatial characteristics of maritime traffic situation need to be further studied.

参考文献

[1] ENDSLEY M R. Design and evaluation for situation awareness enhancement[C]//Proceedings of the Human Factors Society annual meeting. Sage CA:Los Angeles,CA:Sage Publications, 1988, 32(2):97-101.

[2]张洪海,吕文颖,万俊强,等.扇区空中交通风险态势网络建模与演化特征[J].交通运输工程学报, 2023, 23(1):222-241.

[3]吴磊,王晓辉,杨新月,等.交通态势识别及状态转换机制研究[J].交通标准化, 2007(2):61-66.

[4]刘彬,黄群龙.高速公路交通态势分析探究[J].公路,2021, 66(7):356-360.

[5]温冬,张萌萌,孙庆文.基于集对分析评价模型的城市交通态势判别[J].数学的实践与认识, 2024, 54(1):105-113.

[6] IMO. Revised guidelines for the onboard operational use of shipborne Automatic Identification Systems(AIS)[EB/OL].(2015-12-14)[2024-06-21].https://wwwcdn.imo.org/localresources/en/KnowledgeCentre/IndexofIMOResolutions/AssemblyDocuments/A.1106(29).pdf.

[7]李正强.水上交通态势评估建模与可视化研究[D].武汉:武汉理工大学, 2015:10-14.

[8]高广旭.港口水域船舶交通流特征提取及交通态势评价研究[D].武汉:武汉理工大学, 2021:31-32.

[9]何静,赵睿,张恒硕.知识图谱的可视化文献计量分析[J].计算机科学, 2024, 51(S1):13-22.

[10]林德明,陈超美,刘则渊.共被引网络中介中心性的Zipf—Pareto分布研究[J].情报学报, 2011, 30(1):76-82.

[11]李杰,陈超美. CiteSpace:科技文本挖掘及可视化[M].北京:首都经济贸易大学出版社, 2016:193-203.

[12]钟伟金,李佳,杨兴菊.共词分析法研究(三)——共词聚类分析法的原理与特点[J].情报杂志, 2008, 27(7):118-120.

[13]钮浩东,黄洪琼.基于FOA优化GRNN的船舶交通流预测模型[J].微型机与应用, 2016, 35(12):81-83.

[14]赵程栋,庄继晖,程晓鸣,等.基于特征注意力机制的RNN-Bi-LSTM船舶轨迹预测[J].广东海洋大学学报,2022, 42(5):102-109.

[15] XU T, ZHANG Q N. Ship traffic flow prediction in wind farms water area based on spatiotemporal dependence[J].Journal of Marine Science and Engineering, 2022, 10(2):295.

[16]权波,杨博辰,胡可奇,等.基于LSTM的船舶航迹预测模型[J].计算机科学, 2018, 45(S2):126-131.

[17] GAO M, SHI G Y, LI S. Online prediction of ship behavior with automatic identification system sensor data using bidirectional long short-term memory recurrent neural network[J]. Sensors, 2018, 18(12):4211.

[18]李洁,林永峰.基于多时间尺度RNN的时序数据预测[J].计算机应用与软件, 2018, 35(7):33-37.

[19] SUO Y F, CHEN W K, CLARAMUNT C, et al. A ship trajectory prediction framework based on a recurrent neural network[J]. Sensors, 2020, 20(18):5133.

[20] ZHANG S, WANG L, ZHU M D, et al. A bi-directional lstm ship trajectory prediction method based on attention mechanism[C]//2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference(IAEAC). IEEE, 2021:1987-1993.

[21]吴鹏程,罗亮.基于RNN-LSTM的船舶运动轨迹预测[J].造船技术, 2021, 49(3):11-16.

[22] WANG X T, LI J, ZHANG T. A machine-learning model for zonal ship flow prediction using ais data:A case study in the south atlantic states region[J]. Journal of Marine Science and Engineering, 2019, 7(12):463.

[23] XU T, ZHANG Q N. Ship traffic flow prediction in wind farms water area based on spatiotemporal dependence[J].Journal of Marine Science and Engineering, 2022, 10(2):295.

[24] MUTHUKUMARAN V, NATARAJAN R, KALADEVI A C, et al. Traffic flow prediction in inland waterways of Assam region using uncertain spatiotemporal correlative features[J]. Acta Geophysica, 2022, 70(6):2979-2990.

[25]高广旭,刘敬贤,刘奕,等.基于矩阵分解的船舶交通流预测方法研究[J].武汉理工大学学报(交通科学与工程版), 2022, 46(1):171-176.

[26]刘敬贤,高广旭,刘奕,等.基于卷积神经网络及长短时记忆网络的短时船舶交通流量预测[J].中国航海, 2022,45(2):56-61+68.

[27] WANG L, LIU Q, DONG S Y, et al. Effectiveness assessment of ship navigation safety countermeasures using fuzzy cognitive maps[J]. Safety science, 2019, 117:352-364.

[28]吴晶. BP-GA算法在船舶碰撞风险评估中的应用[J].舰船科学技术, 2022, 44(7):94-97.

[29] RASMUSSEN F M, GLIBBERY K A K, MELCHILD K, et al. Quantitative assessment of risk to ship traffic in the Fehmarnbelt Fixed Link project[J]. Journal of Polish Safety and Reliability Association, 2012, 3(1):123-134.

[30]靳卫卫,肖强,罗教彬,等.台风期船舶航行海区风险评估和避台规划[J].中国水运, 2022(6):30-32.

[31] HU Y C, PARK G K. Collision risk assessment based on the vulnerability of marine accidents using fuzzy logic[J].International Journal of Naval Architecture and Ocean Engineering, 2020, 12:541-551.

[32]陈信强,史飞翔,王梓创,等.基于模糊逻辑方法的多船会遇安全态势评估[J].广西大学学报(自然科学版),2022, 47(5):1327-1336.

[33] SHI Z Q, ZHEN R, LIU J L. Fuzzy logic-based modeling method for regional multi-ship collision risk assessment considering impacts of ship crossing angle and navigational environment[J]. Ocean Engineering, 2022, 259:111847.

[34]范中洲,严啸,李锦晓.连续弯曲航道的船舶碰撞风险评估方法研究[J].安全与环境学报, 2023, 23(10):3429-3437.

[35]李宾郎,段建丽,柴昱含.基于AIS的船舶航迹数据应用研究[J].长江信息通信, 2021, 34(12):30-33.

[36] WEN Y Q, TAO W, SUI Z Y, et al. Dynamic model-based method for the analysis of ship behavior in marine traffic situation[J]. Ocean Engineering, 2022, 257:111578.

[37]刘钊,齐磊,梁茂晗,等.数据驱动的船舶异常行为识别方法[J].中国航海, 2022, 45(4):1-7.

[38] SUN S, CHEN Y, PIAO Z J, et al. Vessel AIS Trajectory Online Compression Based on Scan-Pick-Move Algorithm Added Sliding Window[J]. IEEE Access, 2020,8:109350-109359.

[39] YANG J X, LIU Y, MA L Q, et al. Maritime traffic flow clustering analysis by density based trajectory clustering with noise[J]. Ocean engineering, 2022, 249:111001.

[40] LEI P R, XIAO L P, WEN Y T, et al. CAPatternMiner:Mining Ship Collision Avoidance Behavior from AIS Trajectory Data[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Boise, 2018:1875-1878.

[41]张照亿,李颖,董双,等.基于船舶领域模型的船舶碰撞危险识别方法[J].中国航海, 2021, 44(2):1-7.

基本信息:

DOI:

中图分类号:U692

引用信息:

[1]周玥,马建文,刘国新等.海上交通态势研究进展[J].武汉船舶职业技术学院学报,2024,23(06):82-89.

基金信息:

山东省自然科学基金“面向复杂通航系统的水上交通态势动态演化机理与监测研究”(项目编号:ZR2021QG022); 国家自然科学基金项目“基于数据场-云模型的感潮河段交通态势动态演化机理与监测预警研究”(项目编号:51909156)

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