|Table of Contents|

Semantic representation of ship behavior based on context autoencoder(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2022年04期
Page:
334-347
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Semantic representation of ship behavior based on context autoencoder
Author(s):
MA Jie1; 2; 3;  HE Mu-rong1;  JIA Cheng-feng1;  LI Wen-kai1;  ZHANG Yu2; 3; 4
(1. School of Navigation, Wuhan University of Technology, Wuhan 430063, Hubei, China; 2. Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, Hubei, China; 3. National Engineering Research Center for Water Transportation Safety, Wuhan University of Technology, Wuhan 430063, Hubei, China; 4. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, Hubei, China)
Keywords:
ship automatic identification system;  ship behavior;  autoencoder;  continuous bag-of-words model;  semantic representation
PACS:
U675
DOI:
10.19818/j.cnki.1671-1637.2022.04.026
Abstract:
Considering the temporal correlation of ship behavior, a semantic representation model based on the context autoencoder(SRCAE)was proposed for ship behavior. The behavioral feature parameters, such as the longitude, latitude, speed, as well as the course, were extracted to establish the behavioral feature sequence. The behavioral feature sequence was divided into the center ship behavior and context ship behavior via the continuous bag-of-words(CBOW)model. The deep autoencoder(AE)networks were utilized to construct the semantic representation model of context ship behavior, and the encoded center ship behavior obtained from the model was output as the representation vector. The clustering algorithm was employed to establish the ship behavior dictionary. The South Passage Intersection Water of the Yangtze Estuary was selected as the research object, and the data from the automatic identification system(AIS)for ships were employed for verification of the proposed model and method. Analysis results show that the context relationships between ship behaviors can be effectively represented by the proposed SRCAE model, and the representation error of the SRCAE model is lower than that of the traditional AE model and long short-term memory autoencoder(LSTMAE)model. Three clustering algorithms, namely, k-means, Gaussian mixture model(GMM), and kernel k-means, were used to extract the ship behavior dictionary. Compared with the original data, the representation vectors generated by the SRCAE model are easier to distinguish different ship behavior patterns, among which the effect of k-means is the best, and the Silhouette coefficient(SC), Calinski-Harabasz index(CHI), and Davies-Bouldin index(DBI)of k-means reach 0.384, 18.308, and 0.531, respectively. A total of 30 types of composite behaviors are generated, such as steering acceleration, steering deceleration, straight-ahead acceleration, straight-ahead deceleration, and so on and the combination relationships of ship behavior words under different behavior patterns are effectively extracted. 2 tabs, 11 figs, 31 refs.

References:

[1] 朱 姣,刘敬贤,陈 笑,等.基于轨迹的内河船舶行为模式挖掘[J].交通信息与安全,2017,35(3):107-116,132.
ZHU Jiao, LIU Jing-xian, CHEN Xiao, et al. Behavior pattern mining of inland vessels based on trajectories[J]. Journal of Transport Information and Safety, 2017, 35(3): 107-116, 132.(in Chinese)
[2] 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.
WEN Yuan-qiao, ZHANG Yi-meng, HUANG Liang, et al. Mechanism of ship behavior dynamic reasoning based on semantics[J]. Navigation of China, 2019, 42(3): 34-39, 50.(in Chinese)
[3] TU En-mei, ZHANG Guang-hao, RACHMAWATI L, et al.
Exploiting AIS data for intelligent maritime navigation: a comprehensive survey from data to methodology[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 19(5): 1559-1582.
[4] LEI D, FLORIS G, PENTTI K. Review and analysis of
methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data[J]. Reliability Engineering and System Safety, 2020, 200: 106933.
[5] YANG Dong, WU Ling-xiao, WANG Shuai-an, 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.
[6] 甄 荣,邵哲平,潘家财.基于AIS数据的船舶行为特征挖掘与预测:研究进展与展望[J].地球信息科学学报,2021,23(12):2111-2127.
ZHEN Rong, SHAO Zhe-ping, PAN Jia-cai. Advance in character mining and prediction of ship behavior based on AIS data[J]. Journal of Geo-Information Science, 2021, 23(12): 2111-2127.(in Chinese)
[7] 陶 阳,毛 喆,盛 萍,等.基于船舶行为的武汉长江大桥水域货船通航规律研究[J].交通信息与安全,2018,36(1):49-56.
TAO Yang, MAO Zhe, SHENG Ping, et al. A study of regularity of navigation patterns of cargo ships at the waterways near Wuhan Yangtze River Bridge based on ship maneuvering behavior[J]. Journal of Transport Information and Safety, 2018, 36(1): 49-56.(in Chinese)
[8] WU Xing, MEHTA A L, ZALOOM V A, et al. Analysis of waterway transportation in Southeast Texas waterway based on AIS data[J]. Ocean Engineering, 2016, 121: 196-209.
[9] XIAO Fang-liang, LIGTERINGEN H, VAN-GULIJK C, et al. Comparison study on AIS data of ship traffic behavior[J]. Ocean Engineering, 2015, 95: 84-93.
[10] 甄 荣,邵哲平,潘家财,等.基于 AIS 信息的航道内船舶速度分布统计分析[J].集美大学学报(自然科学版),2014,19(4):274-278.
ZHEN Rong, SHAO Zhe-ping, PAN Jia-cai, et al. Statistical analysis of distribution of ship speed within the fairway based on AIS data[J]. Journal of Jimei University(Natural Science), 2014, 19(4): 274-278.(in Chinese)
[11] HUANG Liang, WEN Yuan-qiao, GUO Wei, et al. Mobility pattern analysis of ship trajectories based on semantic transformation and topic model[J]. Ocean Engineering, 2020, 201: 107092.
[12] 朱飞祥,张英俊,高宗江.基于数据挖掘的船舶行为研究[J].中国航海,2012,35(2):50-54.
ZHU Fei-xiang, ZHANG Ying-jun, GAO Zong-jiang. Research on ship behaviors based on data mining[J]. Navigation of China, 2012, 35(2): 50-54.(in Chinese)
[13] ZHANG Wei-bin, GOERLANDT F, KUJALA P, et al. An advanced method for detecting possible near miss ship collisions from AIS data[J]. Ocean Engineering, 2016, 124: 141-156.
[14] BENGIO Y, COURVILLE A, VINCENT P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.
[15] WANG Sheng, BAO Zhi-feng, CULPEPPER J S, et al. A survey on trajectory data management, analytics, and learning[J]. ACM Computing Surveys(CSUR), 2021, 53(2): 1-36.
[16] LI Li, LI Xin, YANG Yuan, et al. Indoor tracking trajectory data similarity analysis with a deep convolutional autoencoder[J]. Sustainable Cities and Society, 2019, 45: 588-595.
[17] WANG Wei, XIA Feng, NIE Han-song, et al. Vehicle
trajectory clustering based on dynamic representation learning of internet of vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(6): 3567-3576.
[18] ZHOU Yang, DAAMEN W, VELLINGA T, et al. Ship
classification based on ship behavior clustering from AIS data[J]. Ocean Engineering, 2019, 175(1): 176-187.
[19] YAO Di, ZHANG Chao, ZHU Zhi-hua, et al. Trajectory
clustering via deep representation learning[C]∥IEEE. 2017 International Joint Conference on Neural Networks(IJCNN). New York: IEEE, 2017: 3880-3887.
[20] ZHANG Rui, XIE Peng, JIANG Huang-bo, et al. Clustering noisy trajectories via robust deep attention auto-encoders[C]∥IEEE. 2019 20th IEEE International Conference on Mobile Data Management(MDM). New York: IEEE, 2019: 63-71.
[21] 谢 鹏.基于深度学习的轨迹数据中移动模式发现方法研究[D].武汉:武汉理工大学,2019.
XIE Peng. Research on moving pattern discovery method in trajectory data based on deep learning[D]. Wuhan: Wuhan University of Technology, 2019.(in Chinese)
[22] 常吉亮,谢 磊,赵建伟,等. 基于VAE-LSTM模型的航迹异常检测算法[J].交通信息与安全,2020,38(6):1-8.
CHANG Ji-liang, XIE Lei, ZHAO Jian-wei, et al. An anomaly detection algorithm for ship trajectory data based on VAE-LSTM model[J]. Journal of Transport Information and Safety, 2020, 38(6): 1-8.(in Chinese)
[23] WEN Yuan-qiao, ZHANG Yi-meng, HUANG Liang, et al. Semantic modelling of ship behavior in harbor based on ontology and dynamic Bayesian network[J]. ISPRS International Journal of Geo-Information, 2019, DOI:10.3390/ijgi8030107.
[24] PARENT C, SPACCAPIETRA S, RENSO C, et al. Semantic trajectories modeling and analysis[J]. ACM Computing Surveys(CSUR), 2013, 45(4): 1-32.
[25] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient
estimation of word representations in vector space[J]. arXiv, 2013, Bibcode: 2013arXiv1301.3781M.
[26] 甄 荣,金永兴,胡勤友,等.基于AIS信息和BP神经网络的船舶航行行为预测[J].中国航海,2017,40(2):6-10.
ZHEN Rong, JIN Yong-xing, HU Qin-you, et al. Vessel behavior prediction based on AIS data and BP neural network[J]. Navigation of China, 2017, 40(2): 6-10.(in Chinese)
[27] 刘 娇,史国友,杨学钱,等.基于DE-SVM的船舶航迹预测模型[J].上海海事大学学报,2020,41(1):34-39,115.
LIU Jiao, SHI Guo-you, YANG Xue-qian, et al. Ship trajectory prediction model based on DE-SVM[J]. Journal of Shanghai Maritime University, 2020, 41(1): 34-39, 115.(in Chinese)
[28] FANG Zhi-xiang, YU Hong-chu, KE Ran-xuan, et al. Automatic identification system-based approach for assessing the near-miss collision risk dynamics of ships in ports[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(2): 534-543.
[29] LIN J, KEOGH E, WEI L, et al. Experiencing SAX: a novel symbolic representation of time series[J]. Data Mining and Knowledge Discovery, 2007, 15(2): 107-144.
[30] KRASNOV F, SEN A. The number of topics optimization: clustering approach[J]. Machine Learning and Knowledge Extraction, 2019, 1(1): 416-426.
[31] WANG Wen-shuo, RAMESH A, ZHU Jia-cheng, et al. Clustering of driving encounter scenarios using connected vehicle trajectories[J]. IEEE Transactions on Intelligent Vehicles, 2020, 5(3): 485-496.

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Last Update: 2022-09-01