Semantic representation of ship behavior based on context autoencoder

MA Jie; HE Mu-rong; JIA Cheng-feng; LI Wen-kai; ZHANG Yu

doi:10.19818/j.cnki.1671-1637.2022.04.026

Volume 22 Issue 4

Aug. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Traffic and Transportation Engineering > 2022 > 22(4): 334-347

Next Previous

MA Jie, HE Mu-rong, JIA Cheng-feng, LI Wen-kai, ZHANG Yu. Semantic representation of ship behavior based on context autoencoder[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 334-347. doi: 10.19818/j.cnki.1671-1637.2022.04.026

Citation:

MA Jie, HE Mu-rong, JIA Cheng-feng, LI Wen-kai, ZHANG Yu. Semantic representation of ship behavior based on context autoencoder[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 334-347. doi: 10.19818/j.cnki.1671-1637.2022.04.026

Citation:

PDF( 13199 KB)

Semantic representation of ship behavior based on context autoencoder

doi: 10.19818/j.cnki.1671-1637.2022.04.026

MA Jie^{1, 2, 3
,},
HE Mu-rong¹,
JIA Cheng-feng¹,
LI Wen-kai¹,
ZHANG Yu^{2, 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

Funds:

National Key Research and Development Program of China 2021YFB3901504

National Natural Science Foundation of China 52271366

National Natural Science Foundation of China 51679182

More Information

Author Bio:
MA Jie(1978-), male, professor, PhD, majie@whut.edu.cn
Received Date: 2022-02-09
Publish Date: 2022-08-25

Abstract

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.
- ship automatic identification system,
- ship behavior,
- autoencoder,
- continuous bag-of-words model,
- semantic representation

FullText(HTML)

References(31)

References

[1]	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) doi: 10.3963/j.issn.1674-4861.2017.03.014
[2]	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) http://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201903008.htm
[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. doi: 10.1016/j.ress.2020.106933
[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. doi: 10.1080/01441647.2019.1649315
[6]	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) doi: 10.12082/dqxxkx.2021.210495
[7]	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) doi: 10.3963/j.issn.1674-4861.2018.01.007
[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. doi: 10.1016/j.oceaneng.2016.05.012
[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. doi: 10.1016/j.oceaneng.2014.11.020
[10]	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) doi: 10.3969/j.issn.1007-7405.2014.04.006
[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. doi: 10.1016/j.oceaneng.2020.107092
[12]	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) doi: 10.3969/j.issn.1000-4653.2012.02.011
[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. doi: 10.1016/j.oceaneng.2016.07.059
[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. doi: 10.1109/TPAMI.2013.50
[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. doi: 10.1016/j.scs.2018.12.025
[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. doi: 10.1109/TITS.2020.2995856
[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]	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]	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) http://www.cnki.com.cn/Article/CJFDTOTAL-JTJS202006002.htm
[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]	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) http://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201702002.htm
[27]	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) http://www.cnki.com.cn/Article/CJFDTOTAL-SHHY202001006.htm
[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.

Relative Articles

Supplements(0)

Cited By

Get Citation

PDF

XML

Article Metrics

Article views (1872) PDF downloads(214)

Semantic representation of ship behavior based on context autoencoder

doi: 10.19818/j.cnki.1671-1637.2022.04.026

Abstract

References

Catalog

Article Metrics

Related

Semantic representation of ship behavior based on context autoencoder

doi: 10.19818/j.cnki.1671-1637.2022.04.026

Abstract

References

Catalog

Article Metrics

Related

Export File

Citation

Format

Content