[1]任成杰,陈怀新,谢卫.基于GRU自编码器的船舶航线提取[J].智能系统学报,2022,17(6):1201-1208.[doi:10.11992/tis.202107006]
REN Chengjie,CHEN Huaixin,XIE Wei.Ship route extraction based on GRU auto-encoder[J].CAAI Transactions on Intelligent Systems,2022,17(6):1201-1208.[doi:10.11992/tis.202107006]
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REN Chengjie,CHEN Huaixin,XIE Wei.Ship route extraction based on GRU auto-encoder[J].CAAI Transactions on Intelligent Systems,2022,17(6):1201-1208.[doi:10.11992/tis.202107006]
基于GRU自编码器的船舶航线提取
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第6期
页码:
1201-1208
栏目:
学术论文—智能系统
出版日期:
2022-11-05
- Title:
- Ship route extraction based on GRU auto-encoder
- Keywords:
- route extraction; automatic identification system; data mining; gate recurrent unit auto-encoder; deep feature; decoding inversion; DBSCAN algorithm; trajectory clustering
- 分类号:
- TP18
- 文献标志码:
- 2022-10-26
- 摘要:
- 船舶自动识别系统(automatic identification system, AIS)数据中蕴含着大量的船舶行为相关信息，从中提取出有效的航线，在海事监管、船只勘查等方面具有广泛应用。本文提出一种基于GRU自编码器(gate recuurent unit auto-encoder，GRU-AE)的船舶航线提取方法，首先采用GRU编码器将原始轨迹数据编码为统一格式的深度特征信息，其次利用DBSCAN (density-based spatial clustering of applications with noise)算法对深度特征信息进行聚类，最后将深度特征类簇中心通过解码器反演生成相应的船舶航线，从而实现在海量AIS数据中挖掘船舶轨迹规律。以波士顿港口为例，分析一年时间内10万多条AIS的船舶航行数据，实验表明本方法可对不同长度轨迹数据进行聚类及其航线提取，并可支撑船舶轨迹异常检测、路径规划、位置预测等研究，具有较好的应用适应性。
- Abstract:
- The automatic identification system (AIS) data consists of a large amount of information associated with shipping behavior. A wide range of applications in maritime supervision and ship surveys can be made use of by extracting useful ship routes from AIS data. The gate recurrent unit auto-encoder (GRU-AE) is the foundation of a method for ship route extraction that is suggested. First, the GRU encoder is employed to encode the original trajectory data into deep feature information in a unified form; then, deep feature information is clustered using the DBSCAN algorithm; and finally, the deep feature cluster center is inverted through the decoder to produce the corresponding ship route, to achieve the mining of the ship trajectory pattern in the massive AIS data. Taking the Port of Boston as an example, more than 100,000 AIS ship navigation data in one year are examined. Experiments reveal that this technique can cluster and extract route data of various lengths and can support the research of ship trajectory abnormality identification, path planning, position prediction, etc., revealing good application adaptability.
- 参考文献/References:
-
[1] 徐良坤, 任律珍, 周世波. 船舶AIS轨迹聚类方法研究进展综述[J]. 广州航海学院学报, 2019, 27(2): 7–12,47
XU Liangkun, REN Lvzhen, ZHOU Shibo. Research progress on clustering AIS trajectory of vessels[J]. Journal of Guangzhou Maritime University, 2019, 27(2): 7–12,47
[2] 马俊, 董良雄, 李军. 一种基于K-means改进蚁群算法的船舶航线设计方法[J]. 中国修船, 2020, 33(3): 38–41
MA Jun, DONG Liangxiong, LI Jun. Method for designing ship routes based on improved ant colony algorithm of K-means[J]. China shiprepair, 2020, 33(3): 38–41
[3] 陈晓, 戴冉, 赵延鹏, 等. 基于鱼群算法的船舶避浅航线设计[J]. 中国航海, 2019, 42(3): 95–99,120
CHEN Xiao, DAI Ran, ZHAO Yanpeng, et al. Ship route planning to avoid shallow waters with artificial fish swarm algorithm[J]. Navigation of China, 2019, 42(3): 95–99,120
[4] HE Yankang, ZHANG Di, ZHANG Jinfen, et al. Ship route planning using historical trajectories derived from AIS data[J]. TransNav: the international journal on marine navigation and safety of sea transportation, 2019, 13(1): 69–76.
[5] 陈立家, 黄立文, 崔梅. 基于改进蚁群算法的船舶多约束最优航线设计[J]. 上海海事大学学报, 2017, 38(4): 11–15
CHEN Lijia, HUANG Liwen, CUI Mei. Optimal ship route planning with multiple constraints based on improved ant colony algorithm[J]. Journal of Shanghai Maritime University, 2017, 38(4): 11–15
[6] GRINYAK V M, SHULENINA A V, IVANENKO Y S. Ship routes planning based on traffic clustering[J]. Journal of physics:conference series, 2021, 1864(1): 012080.
[7] 韩志豪, 汪益兵, 张宇, 等. 基于深度强化学习的船舶航线自动规划[J]. 中国航海, 2021, 44(1): 100–105
HAN Zhihao, WANG Yibing, ZHANG Yu, et al. Automatic ship route planning based on deep reinforcement learning[J]. Navigation of China, 2021, 44(1): 100–105
[8] 龚铭凡, 徐海祥, 冯辉, 等. 基于改进蚁群算法的智能船舶路径规划[J]. 武汉理工大学学报(交通科学与工程版), 2020, 44(6): 1072–1076
GONG Mingfan, XU Haixiang, FENG Hui, et al. Intelligent ship path planning based on improved ant colony algorithm[J]. Journal of Wuhan University of Technology (transportation science & engineering edition), 2020, 44(6): 1072–1076
[9] 姚肖肖, 胡勤友, 杨春. 基于蚁群算法与海量AIS数据的船舶航线规划[J]. 交通信息与安全, 2019, 37(3): 79–85
YAO Xiaoxiao, HU Qinyou, YANG Chun. Route planning of vessels with ant colony algorithm and massive AIS data[J]. Journal of transport information and safety, 2019, 37(3): 79–85
[10] 杨令省. 船舶航线规划中的精确控制算法研究[J]. 舰船科学技术, 2017, 39(8): 85–87
YANG Lingxing. Research on precise control algorithm in ship route planning[J]. Ship science and technology, 2017, 39(8): 85–87
[11] 王加胜, 刘永学, 杨昆, 等. 基于空间聚类分析的南海主要航线提取方法[J]. 交通运输工程学报, 2016, 16(6): 91–98
WANG Jiasheng, LIU Yongxue, YANG Kun, et al. Extraction method of main routes in South China Sea based on spatial cluster analysis[J]. Journal of traffic and transportation engineering, 2016, 16(6): 91–98
[12] HALPERN B S, WALBRIDGE S, SELKOE K A, et al. A global map of human impact on marine ecosystems[J]. Science, 2008, 319(5865): 948–952.
[13] LIN Dong. Spatial analysis of ship routes for maritime security and safe navigation[D]. Halifax: Dalhousie University, 2008.
[14] WANG Jiasheng, LI Manchun, LIU Yongxue, et al. Safety assessment of shipping routes in the South China Sea based on the fuzzy analytic hierarchy process[J]. Safety science, 2014, 62: 46–57.
[15] YAO Di, ZHANG Chao, ZHU Zhihua, et al. Trajectory clustering via deep representation learning[C]//2017 International Joint Conference on Neural Networks (IJCNN). Anchorage: IEEE, 2017: 3880-3887.
[16] LEE J G, HAN Jiawei, WHANG K Y. Trajectory clustering: a partition-and-group framework[C]//SIGMOD’07: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2007: 593-604.
[17] 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(12): 2218–2245.
[18] PALLOTTA G, VESPE M, BRYAN, K. Traffic route extraction and anomaly detection (TREAD): vessel pattern knowledge discovery and exploitation for maritime situational awareness[J]. Ref, 2012, 37(4): 37–44.
[19] PALLOTTA G, VESPE M, BRYAN K. Traffic knowledge discovery from AIS data[C]//Proceedings of the 16th International Conference on Information Fusion. Istanbul: IEEE, 2013: 1996-2003.
[20] DE VRIES G, SOMEREN M. Unsupervised ship trajectory modeling and prediction using compression and clustering[C]//Proceedings of the 18th Annual Belgian-Dutch Conference on Machine Learning. Tilburg: [s.n.], 2009: 7-12.
[21] WANG Lianhui, CHEN Pengfei, CHEN Linying, et al. Ship AIS trajectory clustering: an HDBSCAN-based approach[J]. Journal of marine science and engineering, 2021, 9(6): 566.
[22] COELHO DA SILVA T L, ZEITOUNI K, DE MACêDO J A F. Online clustering of trajectory data stream[C]//2016 17th IEEE International Conference on Mobile Data Management. Porto: IEEE, 2016: 112-121.
[23] CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[EB/OL]. (2014-09-03)[2021-07-06].https://arxiv.org/abs/1406.1078.
[24] CHO K, VAN MERRI?NBOER B, BAHDANAU D, et al. On the properties of neural machine translation: Encoder-decoder approaches[EB/OL]. (2014-10-07)[2021-07-06].https://arxiv.org/abs/1409.1259.
[25] CHUNG J, GULCEHRE C, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL]. (2014-12-11)[2021-07-06].https://arxiv.org/abs/1412.3555.
备注/Memo
收稿日期:2021-07-05。
基金项目:四川省重点研发项目(2020YFG0193).
作者简介:任成杰，硕士研究生，主要研究方向为深度学习、数据挖掘;陈怀新，教授，博士，主要研究方向为信息融合、视频图像处理、机器学习与智能系统等。主持四川省重点基金项目2项。发表学术论文40余篇;谢卫，高级工程师，主要研究方向为信息融合与数据挖掘
通讯作者:陈怀新.E-mail:huaixinchen@uestc.edu.cn
更新日期/Last Update:
1900-01-01