Volume 40 Issue 6
Dec.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(6): 1-11
HUANG Chen, CHEN Deshan, WU Bing, YAN Xinping. A Real-time Detection of Nautical Traffic Events: A Review and Prospect[J]. Journal of Transport Information and Safety, 2022, 40(6): 1-11. doi: 10.3963/j.jssn.1674-4861.2022.06.001
Citation: HUANG Chen, CHEN Deshan, WU Bing, YAN Xinping. A Real-time Detection of Nautical Traffic Events: A Review and Prospect[J]. Journal of Transport Information and Safety, 2022, 40(6): 1-11. doi: 10.3963/j.jssn.1674-4861.2022.06.001
shu

A Real-time Detection of Nautical Traffic Events: A Review and Prospect

doi: 10.3963/j.jssn.1674-4861.2022.06.001
  • 1.

    School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

  • 2.

    National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China

  • 3.

    Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

  • Received Date: 2022-06-06
    Available Online: 2023-03-27
    Abstract
  • Nautical Traffic Event Detection(NTED) methods mostly rely on offline methods using historical data, which are insufficient for real-time traffic supervision. The studies on abnormal behavior detection and incidents detection of ships are collected and investigated, and the findings are concluded as follows: from the perspective of data, the detection data rely on a single source and the environmental information is usually missing; from the perspective of methodologies, classical models that are based on statistical methods, risk assessments, etc., have high efficiency but low accuracy; while, the machine-learning based methods, such as neural networks, image recognition, etc., have high accuracy but low efficiency; and the combination of multi-source data fusion and multi-technology have become new trends.Three key technologies for the real-time NTED are summarized: ① maritime big data technologies, which process ships and environment data efficiently and standardize multi-source heterogeneous data structures, which reduces the false alarm caused by the single data source; ②dynamic behavior modeling, which uses knowledge graph or other technologies to integrate nautical contextual information, and uses deep learning, semantic association, graph neural network or other methods to develop different models for dynamic ship behaviors in different nautical context, which improves the accuracy of the NTED; ③the real-time analysis and visualization techniques combined with parallel systems, which can transfer information between the virtual and real systems, analyze the simulated results, and display the detection process which facilitates human-computer interactions in the supervision. A Nautical Traffic Event Parallel Detection System(NTEPDS) is proposed, which includes three functional modules: ①the data acquisition; ②the backend service; ③the client application. The NTEPDS can receive real-time navigation data, analyze and predict real-time traffic status, dynamically detect and report traffic events and display the simulation results. Finally, the prospects of the real-time NTED are concluded from three aspects: data fusion, traffic state perception, and traffic virtuality-reality mapping, which reveals the development directions of real-time NTED at the practical level.

     

    • FullText(HTML)
  • loading
    • References(51)
  • [1]
    中华人民共和国交通运输部. 国家水上交通安全监管和救助系统布局规划[EB/OL]. 北京: 中华人民共和国交通运输部, 2007. [2007-7-24]. https://xxgk.mot.gov.cn/2020/jigou/zhghs/202006/t20200630_3320035.html.

    Ministry of Transport of the People's Republic of China. Layout planning of national water traffic safety supervision and rescue system[EB/OL]. Beijing: Ministry of Transport of the People's Republic of China, 2007. [2007-7-24]. https://xxgk.mot.gov.cn/2020/jigou/zhghs/202006/t20200630_3320035.html. (in Chinese)
    [2]
    初秀民, 李祎承, 余玉欢. 长江中下游航道通过能力计算方法[J]. 交通运输系统工程与信息, 2014, 14(2): 213-219. doi: 10.3969/j.issn.1009-6744.2014.02.033

    CHU X M, LI Y C, YU Y H. Calculation method for traffic capacity in the midstream-downstream of Yangtze river[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(2): 213-219. (in Chinese) doi: 10.3969/j.issn.1009-6744.2014.02.033
    [3]
    RIVEIRO M, PALLOTTA G, VESPE M. Maritime anomaly detection: A review[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018, 8(5): e1266.
    [4]
    孟智勇, 姚聃, 白兰强, 等. 基于实地灾害调研和雷达观测对"东方之星"倾覆地点附近强风的估计[J]. 科学通报, 2016, 61(7): 797-798. https://www.cnki.com.cn/Article/CJFDTOTAL-KXTB201607018.htm

    MENG Z Y, YAO D, BAI L Q, et al. Wind estimation around the shipwreck of Oriental Star based on field damage surveys and radar observations[J]. Sci Bull, 2016, 61(7): 330-337. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KXTB201607018.htm
    [5]
    唐皇, 尹勇, 神和龙. 海船异常行为检测综述[J]. 重庆交通大学学报(自然科学版), 2019, 38(9): 109-115. doi: 10.3969/j.issn.1674-0696.2019.09.18

    TANG H, YIN Y, SHEN H L. Survey of abnormal behavior of marine vessels[J]. Journal of Chongqing Jiaotong University(Natural Science), 2019, 38(9): 109-115. (in Chinese) doi: 10.3969/j.issn.1674-0696.2019.09.18
    [6]
    陈影玉, 杨神化, 索永峰. 船舶行为异常检测研究进展[J]. 交通信息与安全, 2020, 38(5): 1-11. doi: 10.3963/j.jssn.1674-4861.2020.05.001

    CHEN Y Y, YANG S H, SUO Y F. Research progress of ship behavior anomaly detection[J]. Journal of Transport Information and Safety, 2020, 38(5): 1-11. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.05.001
    [7]
    LONG Z J, LEE S K. Analysis of marine accident and its current status in South Korea[C]. 29thInternational Conference on Ocean, Offshore and Arctic Engineering, Shanghai: ASME, 2010.
    [8]
    HAN X, ARMENAKIS C, JADIDI M. Modeling vessel behaviours by clustering AIS data using optimized DBSCAN[J]. Sustainability, 2021, 13(15): 8162. doi: 10.3390/su13158162
    [9]
    RONG H, TEIXEIRA A P, SOARES C G. Data mining approach to shipping route characterization and anomaly detection based on AIS data[J]. Ocean Engineering, 2020(198): 106936.
    [10]
    RONG H, TEIXEIRA A P, SOARES C G. Maritime traffic probabilistic prediction based on ship motion pattern extraction[J]. Reliability Engineering & System Safety, 2022(217): 108061.
    [11]
    RONG H, TEIXEIRA A P, SOARES C G. Ship trajectory uncertainty prediction based on a Gaussian process model[J]. Ocean Engineering, 2019(182): 499-511.
    [12]
    PALLOTTA G, VESPE M, BRYAN K. Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction[J]. Entropy, 2013, 15(6): 2218-2245.
    [13]
    王正星, 朱玉柱, 王春瑶. 大雾天气海上船舶交通异常挖掘识别方法[J]. 舰船科学技术, 2021, 43(2): 49-51. doi: 10.3404/j.issn.1672-7649.2021.02.010

    WANG Z X, ZHU Y Z, WANG C Y. Analysis on identification of traffic abnormalities in maritime vessels by heavy fog[J]. Ship Science and Technology, 2021, 43(2): 49-51. (in Chinese) doi: 10.3404/j.issn.1672-7649.2021.02.010
    [14]
    向怀坤, 李伟龙, 谢秉磊. 粒子群优化神经网络的交通事件检测算法研究[J]. 计算机测量与控制, 2016, 24(2): 171-174. https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201602049.htm

    XIANG H K, LI W L, XIE B L. Research on traffic incident detection algorithm based on particle swarm optimizer neural network[J]. Computer Measurement & Control, 2016, 24(2): 171-174. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JZCK201602049.htm
    [15]
    刘庆华, 丁文涛, 涂娟娟, 等. 优化BP_AdaBoost算法及其交通事件检测[J]. 同济大学学报(自然科学版), 2015, 43(12): 1829-1833. doi: 10.11908/j.issn.0253-374x.2015.12.010

    LIU Q H, DING W T, TU J J, et al. Improved BP_AdaBoost algorithm and its application in traffic incident detection[J]. Journal of Tongji University(Natural Science Edition), 2015, 43(12): 1829-1833. (in Chinese) doi: 10.11908/j.issn.0253-374x.2015.12.010
    [16]
    MA X, TAO Z, WANG Y, et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J]. Transportation Research Part C: Emerging Technologies, 2015(54): 187-197.
    [17]
    马文耀, 吴兆麟, 李伟峰. 船舶异常行为的一致性检测算法[J]. 交通运输工程学报, 2017, 17(5): 149-158. doi: 10.3969/j.issn.1671-1637.2017.05.014

    MA W Y, WU Z L, LI W F. Conformal detection algorithm of anomalous behaviors of vessel[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 149-158. (in Chinese) doi: 10.3969/j.issn.1671-1637.2017.05.014
    [18]
    KOWALSKA K, PEEL L. Maritime anomaly detection using Gaussian process active learning[C]. 15th International Conference on Information Fusion, Singapore: IEEE, 2012.
    [19]
    BOTTS C H. A novel metric for detecting anomalous ship behavior using a variation of the DBSCAN clustering algorithm[J]. SN Computer Science, 2021, 2(5): 1-16.
    [20]
    ZHANG T, ZHAO S, CHENG B, et al. Detection of AIS closing behavior and MMSI spoofing behavior of ships based on spatiotemporal data[J]. Remote Sensing, 2020, 12(4): 702. doi: 10.3390/rs12040702
    [21]
    D'AFFLISIO E, BRACA P, WILLETT P. Malicious AIS spoofing and abnormal stealth deviations: A comprehensive statistical framework for maritime anomaly detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(4): 2093-2108. doi: 10.1109/TAES.2021.3083466
    [22]
    ZHANG Z, SUO Y, YANG S, et al. Detection of complex abnormal ship behavior based on event stream[C]. 2020 Chinese Automation Congress(CAC), Shanghai: IEEE, 2020.
    [23]
    SHAHIR H Y, GLASSER U, SHAHIR A Y, et al. Maritime situation analysis framework: Vessel interaction classification and anomaly detection[C]. 2015 IEEE International Conference on Big Data, Santa Clara: IEEE, 2015.
    [24]
    CHEN P, HUANG Y, MOU J, et al. Probabilistic risk analysis for ship-ship collision: State-of-the-art[J]. Safety science, 2019(117): 108-122.
    [25]
    ZHANG W, KOPCA C, TANG J, et al. A systematic approach for collision risk analysis based on AIS data[J]. The Journal of Navigation, 2017, 70(5): 1117-1132. doi: 10.1017/S0373463317000212
    [26]
    WENG J, XUE S. Ship collision frequency estimation in port fairways: A case study[J]. The Journal of Navigation, 2015, 68(3): 602-618. doi: 10.1017/S0373463314000885
    [27]
    HUANG Y, VAN G P. Time-varying risk measurement for ship collision prevention[J]. Risk Analysis, 2020, 40(1): 24-42. doi: 10.1111/risa.13293
    [28]
    XI Y T, YANG Z L, FANG Q G, et al. A new hybrid approach to human error probability quantification-applications in maritime operations[J]. Ocean Engineering, 2017(138): 45-54.
    [29]
    SOTIRALIS P, VENTIKOS N P, HAMANN R, et al. Incorporation of human factors into ship collision risk models focusing on human centred design aspects[J]. Reliability Engineering & System Safety, 2016(156): 210-227.
    [30]
    LUO M, SHIN S H. Half-century research developments in maritime accidents: Future directions[J]. Accident Analysis & Prevention, 2019(123): 448-460.
    [31]
    WANG L, WANG J, SHI M, et al. Critical risk factors in ship fire accidents[J]. Maritime Policy & Management, 2021, 48(6): 895-913.
    [32]
    付姗姗, 宋倩, 庄慧, 等. 基于Tripod-Beta模型的船舶火灾事故风险分析[J]. 安全与环境学报, 2020, 20(1): 9-19. https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202001002.htm

    FU S S, SONG Q, ZHUANG H, et al. Overview risk analysis of the ship fire accidents via a Tripod-Beta model[J]. Journal of Safety and Environment, 2020, 20(1): 9-19. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-AQHJ202001002.htm
    [33]
    李先锋, 徐森, 花义明. 基于OpenCV计算机视觉的海事视频船舶火灾烟雾检测技术[J]. 舰船科学技术, 2021, 43(22): 202-204. doi: 10.3404/j.issn.1672-7649.2021.11A.068

    LI X F, XU S, HUA Y M. Research on marine video ship fire smoke detectiontechnology based on OpenCV computer vision[J]. Ship Science and Technology, 2021, 43(22): 202-204. (in Chinese) doi: 10.3404/j.issn.1672-7649.2021.11A.068
    [34]
    何光华. LNG运输船火灾检测与报警系统设计[J]. 船海工程, 2021, 50(1): 45-48.

    HE G H. Design of fire detection and alarm system for LNG carrier[J]. Ship & Ocean Engineering, 2021, 50(1): 45-48. (in Chinese)
    [35]
    周泊龙, 宋英磊, 俞孟蕻. 基于图像处理的舰船火灾烟雾检测技术研究[J]. 舰船科学技术, 2016, 38(9): 111-115. https://www.cnki.com.cn/Article/CJFDTOTAL-JCKX201609025.htm

    ZHOU B L, SONG Y L, YU M H. Research on warship fire smoke detection technology based on image processing[J]. Ship Science and Technology, 2016, 38(9): 111-115. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JCKX201609025.htm
    [36]
    MAZAHERI A, MONTEWKA J, KUJALA P. Modeling the risk of ship grounding—a literature review from a risk management perspective[J]. WMU Journal of Maritime Affairs, 2014, 13(2): 269-297. doi: 10.1007/s13437-013-0056-3
    [37]
    ACANFORA M, KRATA P, MONTEWKA J, et al. Towards a method for detecting large roll motions suitable for oceangoing ships[J]. Applied Ocean Research, 2018(79): 49-61.
    [38]
    于卫红, 付飘云, 任月, 等. 基于PMI与BTM的船舶事故原因文本挖掘[J]. 交通信息与安全, 2021, 39(1): 35-44. doi: 10.3963/j.jssn.1674-4861.2021.01.0005

    YU W H, FU P Y, REN Y, et al. Text mining for causes of ship accidents based on PMI and BTM[J]. Journal of Transport Information and Safety, 2021, 39(1): 35-44. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.01.0005
    [39]
    LIU X, CAI H, ZHONG R, et al. Learning traffic as images for incident detection using convolutional neural networks[J]. IEEE Access, 2020(8): 7916-7924.
    [40]
    ZHANG D, ZHANG Y, ZHANG C. Data mining approach for automatic ship-route design for coastal seas using AIS trajectory clustering analysis[J]. Ocean Engineering, 2021(236): 109535.
    [41]
    KOGA S. Major challenges and solutions for utilizing big data in the maritime industry[D]. Malmö: World Maritime University, 2015.
    [42]
    YANG D, WU L, WANG S, et al. How big data enriches maritime research-a critical review of automatic identification system(AIS)data applications[J]. Transport Reviews, 2019, 39(6): 755-773.
    [43]
    International Maritime Organization(IMO). Report to the maritime safety committee[R]. London: The Maritime Safety Committee, 2014.
    [44]
    ZHANG T C, TIAN X, SUN X H, et al. Overview of research on knowledge graph embedding technology[J/OL]. Journal of Software, 2021[2021-11-15]. http://www.jos.org.cn/1000-9825/6429.htm.
    [45]
    JI S, PAN S, CAMBRIA E, et al. A survey on knowledge graphs: Representation, acquisition, and applications[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(2): 494-514.
    [46]
    文元桥, 张义萌, 黄亮, 等. 基于语义的船舶行为动态推理机制[J]. 中国航海, 2019, 42(3): 34-39+50. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201903008.htm

    WEN Y Q, ZHANG Y M, HUANG L, et al. Mechanism of ship behavior dynamic reasoning based on semantics[J]. Navigation of China, 2019, 42(3): 34-39+50. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201903008.htm
    [47]
    ZHU J, HAN X, DENG H, et al. KST-GCN: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 1-11.
    [48]
    THOMAS J J. Illuminating the path: The research and development agenda for visual analytics[M]. Washington, D. C., USA: IEEE Computer Society, 2005.
    [49]
    WANG F Y. The emergence of intelligent enterprises: From CPS to CPSS[J]. IEEE Intelligent Systems, 2010, 25(4): 85-88.
    [50]
    杨林瑶, 陈思远, 王晓, 等. 数字孪生与平行系统: 发展现状、对比及展望[J]. 自动化学报, 2019, 45(11): 2001-2031. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201911001.htm

    YANG L Y, CHEN S Y, WANG X, et al. Digital twins and parallel systems: State of the art, comparisons and prospect[J]. Acta Automatica Sinica, 2019, 45(11): 2001-2031. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201911001.htm
    [51]
    严新平, 李晨, 刘佳仑, 等. 新一代航运系统体系架构与关键技术研究[J]. 交通运输系统工程与信息, 2021, 21(5): 22-29+76. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202105004.htm

    YAN X P, LI C, LIU J L, et al. Architecture and key technologies for new generation of waterborne transportation system[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 22-29+76. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT202105004.htm
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1013) PDF downloads(70) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return