Stopping behavior recognition and classification of ship based on trajectory characteristics

HUANG Liang; ZHANG Zhi-hao; WEN Yuan-qiao; ZHU Man; HUANG Ya-min

doi:10.19818/j.cnki.1671-1637.2021.05.016

Volume 21 Issue 5

Nov. 2021

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Article Navigation > Journal of Traffic and Transportation Engineering > 2021 > 21(5): 189-198

HUANG Liang, ZHANG Zhi-hao, WEN Yuan-qiao, ZHU Man, HUANG Ya-min. Stopping behavior recognition and classification of ship based on trajectory characteristics[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 189-198. doi: 10.19818/j.cnki.1671-1637.2021.05.016

Citation:

HUANG Liang, ZHANG Zhi-hao, WEN Yuan-qiao, ZHU Man, HUANG Ya-min. Stopping behavior recognition and classification of ship based on trajectory characteristics[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 189-198. doi: 10.19818/j.cnki.1671-1637.2021.05.016

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Stopping behavior recognition and classification of ship based on trajectory characteristics

doi: 10.19818/j.cnki.1671-1637.2021.05.016

HUANG Liang^{1, 2
,},
ZHANG Zhi-hao^{1, 2},
WEN Yuan-qiao^{1, 2},
ZHU Man^{1, 2},
HUANG Ya-min^{1, 2}

1.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China
2.
National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, Hubei, China

Funds:

National Key Research and Development Program of China 2018YFC1407405

National Natural Science Foundation of China 41801375

National Natural Science Foundation of China 51679180

National Natural Science Foundation of China 51709218

More Information

Author Bio:
HUANG Liang(1986-), male, associate professor, PhD, leung.huang@whut.edu.cn
Received Date: 2021-04-15
Available Online: 2021-11-13
Publish Date: 2021-10-01

Abstract

Abstract

To estimate stopping activities of ships from massive trajectory data accurately, a two-stage strategy was established to extract stop points from ship trajectories, and an automatic characteristic-based ship stopping behavior recognition and classification method was also proposed. By taking the distance, time and number of points as the constraint conditions, a rule model was constructed to detect the candidate stop trajectories from the raw trajectories. The isolation forest algorithm was applied for the abnormal outliers detection and elimination. A set of highly clustered ship stop trajectories was extracted. Based on the spatio-temporal characteristics of ship berthing and anchoring. Three indices, including the repetition rate of trajectory point, mean distance between neighboring points, and distance between the farthest point pair, were defined to establish a new trajectory similarity measurement model. Then, the distribution characteristics and aggregation degree of ship stop trajectory points were quantitatively evaluated, and the K-nearest neighbor algorithm was then used to automatically classify the berthing and anchoring behaviors of ships. The proposed method was applied to the ship trajectory data collected from three different waters. The classification results of ship stopping behaviors were obtained accurately. The differences in spatio-temporal characteristics of ship anchoring and berthing were verified. The accuracies of recognition and classification of ship stopping behaviors were assessed with the help of manually annotated results. Research results indicate that the repetition rate of trajectory points for ship berthing is more than 80%. The distance between the furthest point pair and the mean distance between neighboring points are 6-11 and 1-2 m, respectively. The repetition rate of trajectory points for ship anchoring is less than 10%. The distance between the furthest point pair and the mean distance between neighboring points are 150-250 and 8-10 m, respectively. Thus, the three spatio-temporal characteristics, including the repetition rate of trajectory point, mean distance between neighboring points, and distance between the farthest point pair have a significant ability to distinguish the ship berthing and anchoring. The recognition and classification accuracy of the proposed method reaches up to 98%. Therefore, its effectiveness is fully proved. With the help of the proposed model, the spatial positions of existing docks and anchorages can be updated. Abnormal ship stops outside the regular waters or abnormal ship stops for prolonged periods inside the regular waters can be recognized automatically. The stopping distribution in ports can be monitored, and the popular docks and anchorages in different times and seasons can be known. In this way, the port planning layout and traffic organization can be optimized. 3 tabs, 7 figs, 31 refs.
- traffic information engineering,
- ship behavior analysis,
- stop point extraction,
- isolation forest,
- K-nearest neighbor classification,
- trajectory mining

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References(31)

References

[1]	陈金海, 陆锋, 彭国均. 海洋运输船舶轨迹分析研究进展[J]. 中国航海, 2012, 35(3): 53-57. doi: 10.3969/j.issn.1000-4653.2012.03.012 CHEN Jin-hai, LU Feng, PENG Guo-jun. The progress of research in maritime vessel trajectory analysis[J]. Navigation of China, 2012, 35(3): 53-57. (in Chinese) doi: 10.3969/j.issn.1000-4653.2012.03.012
[2]	黄亮, 刘益, 文元桥, 等. 内河渡船异常行为识别[J]. 大连海事大学学报, 2017, 43(1): 8-13. https://www.cnki.com.cn/Article/CJFDTOTAL-DLHS201701002.htm HUANG Liang, LIU Yi, WEN Yuan-qiao, et al. Abnormal behavior recognition of inland river ferryboat[J]. Journal of Dalian Maritime University, 2017, 43(1): 8-13. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLHS201701002.htm
[3]	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
[4]	SHENG Pan, YIN Jing-bo. Extracting shipping route patterns by trajectory clustering model based on automatic identification system data[J]. Sustainability, 2018, 10(7): 2327. doi: 10.3390/su10072327
[5]	GAO Miao, SHI Guo-you, LI Shuang. 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. doi: 10.3390/s18124211
[6]	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
[7]	SHENG Kai, LIU Zhong, ZHOU De-chao, et al. Research on ship classification based on trajectory features[J]. Journal of Navigation, 2018, 71(1): 100-116. doi: 10.1017/S0373463317000546
[8]	HÖRTEBORN A, RINGSBERG J W, SVANBERG M, et al. A revisit of the definition of the ship domain based on AIS analysis[J]. Journal of Navigation, 2019, 72(3): 777-794. doi: 10.1017/S0373463318000978
[9]	GAO Miao, SHI Guo-you. Ship spatiotemporal key feature point online extraction based on AIS multi-sensor data using an improved sliding window algorithm[J]. Sensors, 2019, 19(12): 2706. doi: 10.3390/s19122706
[10]	ZHANG Shu-kai, SHI Guo-you, LIU Zheng-jiang, et al. Data-driven based automatic maritime routing from massive AIS trajectories in the face of disparity[J]. Ocean Engineering, 2018, 155: 240-250. doi: 10.1016/j.oceaneng.2018.02.060
[11]	KIM J S, JEONG J S. Extraction of reference seaway through machine learning of ship navigational data and trajectory[J]. International Journal of Fuzzy Logic and Intelligent Systems, 2017, 17(2): 82-90. doi: 10.5391/IJFIS.2017.17.2.82
[12]	ZHOU Yang, DAAMEN W, VELLINGA T, et al. Ship classification based on ship behavior clustering from AIS data[J]. Ocean Engineering, 2019, 175: 176-187. doi: 10.1016/j.oceaneng.2019.02.005
[13]	JIN Liang, LUO Zheng-yi, GAO Shu. Visual analytics approach to vessel behaviour analysis[J]. The Journal of Navigation, 2018, 71(5): 1195-1209. doi: 10.1017/S0373463318000085
[14]	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 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) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201903008.htm
[15]	ALESSANDRINI A, MAZZARELLA F, VESPE M. Estimated time of arrival using historical vessel tracking data[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(1): 7-15.
[16]	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, 8(3): 107. doi: 10.3390/ijgi8030107
[17]	HUANG Ya-min, CHEN Lin-ying, VAN GELDER P H A J M. Generalized velocity obstacle algorithm for preventing ship collisions at sea[J]. Ocean Engineering, 2019, 173: 142-156. doi: 10.1016/j.oceaneng.2018.12.053
[18]	WANG Jiang, ZHU Cheng, ZHOU Yun, et al. Vessel spatio-temporal knowledge discovery with AIS trajectories using co-clustering[J]. Journal of Navigation, 2017, 70(6): 1383-1400. doi: 10.1017/S0373463317000406
[19]	PATROUMPAS K, ALEVIZOS E, ARTIKIS A, et al. Online event recognition from moving vessel trajectories[J]. GeoInformatica, 2017, 21(2): 389-427. doi: 10.1007/s10707-016-0266-x
[20]	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.
[21]	丁兆颖, 姚迪, 吴琳, 等. 一种基于改进的DBSCAN的面向海量船舶位置数据码头挖掘算法[J]. 计算机工程与科学, 2015, 37(11): 2061-2067. doi: 10.3969/j.issn.1007-130X.2015.11.011 DING Zhao-ying, YAO Di, WU Lin, et al. A dock mining algorithm for massive vessel location data based on improved DBSCAN[J]. Computer Engineering and Science, 2015, 37(11): 2061-2067. (in Chinese) doi: 10.3969/j.issn.1007-130X.2015.11.011
[22]	叶仁道, 姜玲, 张瑜. 大数据背景下全球船舶停泊点的数据挖掘分析[J]. 杭州电子科技大学学报(社会科学版), 2018, 14(1): 13-17. https://www.cnki.com.cn/Article/CJFDTOTAL-HZDS201801003.htm YE Ren-dao, JIANG Ling, ZHANG Yu. A data mining analysis of global moorages under big data background[J]. Journal of Hangzhou Dianzi University (Social Sciences), 2018, 14(1): 13-17. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HZDS201801003.htm
[23]	郑海林, 胡勤友, 杨春, 等. 上海外高桥港区停泊船聚类分析与异常检测[J]. 地球信息科学学报, 2018, 20(5): 640-646. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX201805012.htm ZHENG Hai-lin, HU Qin-you, YANG Chun, et al. Clustering analysis and anomaly detection of berthing ships at Waigaoqiao Harbour District of Shanghai[J]. Journal of Geo-information Science, 2018, 20(5): 640-646. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX201805012.htm
[24]	郑振涛, 赵卓峰, 王桂玲, 等. 面向港口停留区域识别的船舶停留轨迹提取方法[J]. 计算机应用, 2019, 39(1): 113-117. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201901022.htm ZHENG Zhen-tao, ZHAO Zhuo-feng, WANG Gui-ling, et al. Ship trajectory extraction method for port parking area identification[J]. Journal of Computer Applications, 2019, 39(1): 113-117. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201901022.htm
[25]	LIU F T, TING K M, ZHOU Zhi-hua. Isolation-based anomaly detection[J]. ACM Transactions on Knowledge Discovery from Data, 2012, 6(1): 1-39.
[26]	向琛. 基于孤立森林算法的船舶异常行为集成检测[D]. 大连: 大连海事大学, 2020. XIANG Chen. Integrated detection of ship abnormal behavior based on isolation forest algorithm[D]. Dalian: Dalian Maritime University, 2020. (in Chinese)
[27]	陈家义, 李福武, 何小阳. AIS系统在大型船舶锚泊半径及船间距的应用[J]. 舰船科学技术, 2017, 39(5): 67-69. https://www.cnki.com.cn/Article/CJFDTOTAL-JCKX201710024.htm CHEN Jia-yi, LI Fu-wu, HE Xiao-yang. The application of AIS system in the large ship's mooring radius and ship spacing[J]. Ship Science and Technology, 2017, 39(5): 67-69. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JCKX201710024.htm
[28]	刘磊, 初秀民, 蒋仲廉, 等. 基于KNN的船舶轨迹分类算法[J]. 大连海事大学学报, 2018, 44(3): 15-21. https://www.cnki.com.cn/Article/CJFDTOTAL-DLHS201803005.htm LIU Lei, CHU Xiu-min, JIANG Zhong-lian, et al. Ship trajectory classification algorithm based on KNN[J]. Journal of Dalian Maritime University, 2018, 44(3): 15-21. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLHS201803005.htm
[29]	LUO Xiang-long, LI Dan-yang, YANG Yu, et al. Spatiotemporal traffic flow prediction with KNN and LSTM[J]. Journal of Advanced Transportation, 2019, 2019: 4145353.
[30]	霍豪, 沈金星, 郑长江. 基于KNN算法的公交到站时间预测[J]. 交通运输工程与信息学报, 2020, 18(4): 76-82, 102. https://www.cnki.com.cn/Article/CJFDTOTAL-JTGC202004010.htm HUO Hao, SHEN Jin-xing, ZHENG Chang-jiang. Bus arrival time prediction based on KNN algorithm[J]. Journal of Transportation Engineering and Information, 2020, 18(4): 76-82, 102. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JTGC202004010.htm
[31]	吕荣. 基于大数据处理技术的AIS应用研究[J]. 海军工程大学学报, 2017, 29(4): 98-102, 112. https://www.cnki.com.cn/Article/CJFDTOTAL-HJGX201704020.htm LYU Rong. Applications for AIS by bigdata processing technology[J]. Journal of Naval University of Engineering, 2017, 29(4): 98-102, 112. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HJGX201704020.htm

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Stopping behavior recognition and classification of ship based on trajectory characteristics

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