船舶航行交通事件实时检测技术研究现状与展望

黄琛; 陈德山; 吴兵; 严新平

doi:10.3963/j.jssn.1674-4861.2022.06.001

船舶航行交通事件实时检测技术研究现状与展望

doi: 10.3963/j.jssn.1674-4861.2022.06.001

黄琛^{1, 2,},
陈德山^{2, 3, ,},
吴兵^{2, 3},
严新平^{1, 2, 3}

1.
武汉理工大学交通与物流工程学院武汉 430063
2.
武汉理工大学国家水运安全工程技术研究中心武汉 430063
3.
武汉理工大学智能交通系统研究中心武汉 430063

基金项目:

国家重点研发计划项目 2021YFC3001504

国家自然科学基金重点国际(地区)合作项目 51920105014

国家自然科学基金面上项目 52272424

详细信息

作者简介:
黄琛(1998—), 硕士研究生.研究方向: 船舶交通事件检测.E-mail: 306145@whut.edu.cn

通讯作者:
陈德山(1986—), 博士, 副研究员.研究方向: 交通智能感知.E-mail: dschen@whut.edu.cn

中图分类号: U692.5+1
计量
- 文章访问数: 968
- HTML全文浏览量: 370
- PDF下载量: 69
- 被引次数: 0
出版历程
- 收稿日期: 2022-06-06
- 网络出版日期: 2023-03-27

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

HUANG Chen^{1, 2
,},
CHEN Deshan^{2, 3
, ,},
WU Bing^{2, 3},
YAN Xinping^{1, 2, 3}

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

摘要

摘要: 船舶航行交通事件检测依赖基于历史数据的离线检测方法, 检测模型适用性差, 难以满足监管人员的实时监测需求。通过分析船舶异常行为检测、航行事故检测等现有交通事件检测技术, 可以发现: 在数据层面, 监测数据来源单一、环境信息缺失; 在方法层面, 基于统计、风险评估等经典模型的事件监测方法效率高但准确性低, 基于神经网络、图像识别等机器学习的检测方法准确性高但效率低; 多源数据融合、多项技术结合的交通事件检测方法成为实时检测方法的发展趋势。在此基础上, 梳理了实时船舶航行交通事件检测的3项关键技术: (1)海事大数据技术: 高效处理船舶运动数据和航行环境数据, 统一多源异构数据结构标准, 降低数据源单一造成的事件误报率; (2)船舶行为动态建模技术: 利用知识图谱等技术融合船舶航行情境信息, 在不同船舶运动环境下利用深度学习、语义关联、图神经网络等方法构建不同的船舶行为模型, 提高检测准确性; (3)实时分析和可视化技术: 结合平行系统进行虚实系统间信息传递, 定性分析检测结果, 实时显示检测全过程, 提升监管过程中的人机交互效率。然后, 提出了包括数据采集、后台服务和客户端应用3个功能模块的交通事件平行检测系统; 该系统具备实时接收并处理船舶航行数据、分析并预测交通状态、动态检测并预警交通事件和仿真结果展示等功能。从数据融合、交通状态感知和交通虚实映射3个方面, 展望了面向海事监测实务的实时检测技术发展方向。
- 水路交通 /
- 船舶航行交通事件 /
- 实时检测 /
- 平行检测系统 /
- 异常行为 /
- 船舶事故
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.
- waterborne traffic /
- ship navigation traffic events /
- real-time detect /
- parallel detect system /
- abnormal behavior /
- ship accident

HTML全文

图 1 船舶航行交通事件分类

Figure 1. Ship navigation traffic event classification

下载: 全尺寸图片幻灯片

图 2 船舶异常行为检测方法

Figure 2. Ship abnormal behavior detection methods

下载: 全尺寸图片幻灯片

图 3 船舶事故检测方法

Figure 3. Ship accident detection methods

下载: 全尺寸图片幻灯片

图 4 船舶航行交通事件检测关键技术

Figure 4. Key technologies of ship navigation traffic event detection

下载: 全尺寸图片幻灯片

图 5 平行检测系统架构

Figure 5. The architecture of parallel detection system

下载: 全尺寸图片幻灯片

参考文献(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]. 15^th 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