Trajectory Data Restoring: A Way of Visual Analysis of Vessel Identity Base on OPTICS

Authors

  • Jinyu Lei National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China; Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China; College of Mathematics and Data Science, Minjiang University (MJU), Fuzhou, China
  • Xiumin Chu Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China; College of Physics and Electronic Information Engineering, Minjiang University (MJU), Fuzhou, China
  • Wei He Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China; College of Physics and Electronic Information Engineering, Minjiang University (MJU), Fuzhou, China; Engineering Research Center of Fujian University for Marine Intelligent Ship Equipment, Minjiang University, Fuzhou, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2028

Keywords:

Automatic identification system, waterway transportation, visual analysis, OPTICS clustering

Abstract

Automatic identification system (AIS) data is a significant analysis and decision-making basis for maritime situational awareness. Because of particular navigation environment and the vulnerability of AIS equipment onboard, results in the phenomenon that numerous vessels share the same Maritime Mobile Service Identity (MMSI) in the AIS data collected in ocean and inland waterway. This kind of mixed trajectory information dramatically affects the judgement of the maritime manager and supervisors. In this paper, the visual analytics combined with the algorithm named Ordering Points to Identify the Clustering Structure (OPTICS) is adopted to realize the separation of vessels sharing same MMSI, which can help analysts to recognize the vessel trajectory information and assess the risk of marine traffic correctly. Firstly, this paper illustrates the application of OPTICS clustering method based on space-time distance in AIS trajectory separation. Secondly, the display and interaction of trajectory information of Vessels sharing the same MMSI in OpenStreetMap map were introduced. Then visual analysis method is applied to optimize the parameters of the algorithm and display the trajectory separation effect corresponding to different settings. In final, various practical situations are discussed, and the empirical test shows that it is feasible in AIS chaos trajectory separation.

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Author Biographies

Jinyu Lei, National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China; Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China; College of Mathematics and Data Science, Minjiang University (MJU), Fuzhou, China

Jinyu Lei received his Ph.D. in Transportation Engineering from Wuhan University of Technology, China. Currently he is a lecture with Minjiang University, China. His research interests include visual analytics, maritime situation awareness, and artificial intelligence with its application in transportation safety.

Xiumin Chu, Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China; College of Physics and Electronic Information Engineering, Minjiang University (MJU), Fuzhou, China

Xiumin Chu received his Ph.D. in Automotive Engineering from Jilin University, China. Currently he is a professor with National Engineering Research Center of Water Transport Safety, Wuhan university of technology, China. His research interests include safety control in waterway transportation, information collection and processing in traffic engineering, and intelligent waterway transportation.

Wei He, Fujian Engineering Research Center of Safety Control for Ship Intelligent Navigation, Minjiang University, Fuzhou, China; College of Physics and Electronic Information Engineering, Minjiang University (MJU), Fuzhou, China; Engineering Research Center of Fujian University for Marine Intelligent Ship Equipment, Minjiang University, Fuzhou, China

Wei He received his Ph.D. in Transportation Engineering from Wuhan University of Technology, China. Currently he is an associate professor with Minjiang University, China. His research interests include data analysis and mining, traffic control and management, and artificial intelligence with its application in transportation safety.

References

Li M, Mou J, Liu RR, et al. Relational Model of Accidents and Vessel Traffic using AIS Data and GIS: A Case Study of the Western Port of Shenzhen City [J]. Journal of Marine Science and Engineering, 2019, 7(6): 163.

Scheepens R, Hurter C, Van De Wetering H, et al. Visualization, selection, and analysis of traffic flows [J]. IEEE transactions on visualization and computer graphics, 2015, 22(1): 379–388.

He W, Zhong C, Sotelo MA, et al. Shortterm vessel traffic flow forecasting by using an improved Kalman model [J]. Cluster Computing, 2017: 1–10.

Claramunt C, Ray C, Salmon L, et al. Maritime data integration and analysis: recent progress and research challenges [J]. Advances in Database Technology-EDBT, 2017, 2017: 192–197.

Chen C, Wu Q, Gao S. Quality assessment model for shipping data sources of the Yangtze River [C]//2017 4th International Conference on Transportation Information and Safety (ICTIS). IEEE, 2017: 355–361.

Harati-Mokhtari A, Wall A, Brooks P, et al. Automatic Identification System (AIS): data reliability and human error implications [J]. The Journal of Navigation, 2007, 60(3): 373–389.

Kimbra, Cutlip. “Spoofing: One Identity Shared by Multiple Vessels.” Global Fishing Watch. Web. 25 July. 2015.

Wei Zhaokun. The vessels trajectory clustering and its applications based on AIS [D]. Dalian Maritime University, 2015

Gao Qiang, Zhang Feng-Li, Wang Rui-Jin. Trajectory Big Data: A Review of Key Technologies in Data Pro-cessing [J]. Journal of Software, 2017, 28(4): 959–992.

Liu L, Liu X, Chu X, et al. Coverage effectiveness analysis of AIS base station: a case study in Yangtze River [C]//2017 4th International Conference on Transportation Information and Safety (ICTIS). IEEE, 2017: 178–183.

Kraus P, Mohrdieck C, Schwenker F. Ship classification based on trajectory data with machine learning methods [C]//2018 19th International Radar Symposium (IRS). IEEE, 2018: 1–10.

Mazzarella F, Alessandrini A, Greidanus H, et al. Data fusion for wide-area maritime surveillance [C]//Workshop on Moving objects at Sea. 2013.

Kodiyan NJ. Detection and correction of mover identity problems in movement datasets [D]. The Technical University of Munich. 2018.

Zhao L, Shi G, Yang J. Ship trajectories pre-processing based on AIS data [J]. The Journal of Navigation, 2018, 71(5): 1210–1230.

Zhu Jiao, Liu Jingxian, 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.

Zheng Zhentao, Zhao Zhuofeng, Wang Guiling. Ship trajectory extraction method for port stop area identification [J]. Journal of Computer Applications, 2017, 28(4): 959–992.

Yang Shuliang, Bi Shuoben, Athanase Nkunzimana, et al. Spatial clustering method for taxi passenger trajectory. Computer Engineering and Applications, 2018, 54(14): 249–255.

Lei Jinyu, Chu Xiumin, He Wei, et al. Visual Analytic System of Vessel Traffic in Bridge Waterway [J]. Journal of Shanghai Jiao Tong University, 2017, 51(7): 840–845.

He Zhao-cheng, Zhou Ya-qiang, Yu Zhi. Regional traffic state evaluation method based on data visualization [J]. Journal of Traffic and Transportation Engineering, 2016, 16(1): 133–140.

Andrienko G, Andrienko N, Fuchs G. Understanding movement data quality [J]. Journal of location Based services, 2016, 10(1): 31–46.

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Published

2021-03-16

Issue

Section

Advanced Practice in Web Engineering