Hostname: page-component-6766d58669-76mfw Total loading time: 0 Render date: 2026-05-24T15:15:10.734Z Has data issue: false hasContentIssue false
  • English
  • Français

Ship Trajectories Pre-processing Based on AIS Data

Published online by Cambridge University Press:  22 April 2018

Liangbin Zhao ,
Guoyou Shi  and
Jiaxuan Yang
Liangbin Zhao*
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
Guoyou Shi
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
Jiaxuan Yang
Affiliation:
(Navigation College, Dalian Maritime University, Dalian, China)
*
(E-mail: vszlb@126.com)
Get access Rights & Permissions [Opens in a new window]

Abstract

Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.

Keywords

AIS TrajectoriesAIS errorData qualityPre-processing

Information

Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 5 , September 2018 , pp. 1210 - 1230
Copyright
Copyright © The Royal Institute of Navigation 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Aarsæther, K.G. and Moan, T. (2009). Estimating navigation patterns from AIS. The Journal of Navigation, 62(4), 587–607.Google Scholar
Altan, Y.C. and Otay, E.N. (2017). Maritime Traffic Analysis of the Strait of Istanbul based on AIS data. The Journal of Navigation, 70(6), 1367–1382.Google Scholar
Bailey, N., (2005). Training, technology and ais: looking beyond the box. Proceedings of the Seafarers International Research Centre's 4th International Symposium Cardiff University, Cardiff, 108–128.Google Scholar
Banyś, P., Noack, T. and Gewies, S. (2012). Assessment of AIS vessel position report under the aspect of data reliability. Annual of Navigation, 19(1), 5–16.Google Scholar
Breithaupt, S.A., Copping, A., Tagestad, J. and Whiting, J. (2017). Maritime Route Delineation using AIS Data from the Atlantic Coast of the US. The Journal of Navigation, 70(2), 379–394.Google Scholar
Brodie, M.L. (1980). Data quality in information systems. Information & Management, 3(6), 245–258.Google Scholar
Chen, J., Lu, F. and Peng, G. (2015). A quantitative approach for delineating principal fairways of ship passages through a strait. Ocean Engineering, 103(103), 188–197.Google Scholar
De Souza, E.N., Boerder, K., Matwin, S. and Worm, B. (2016). Improving fishing pattern detection from satellite AIS using data mining and machine learning. Plos One, 11(7), e0158248.Google Scholar
Felski, A., Jaskolski, K. and Banyś, P. (2015). Comprehensive assessment of Automatic Identification System (AIS) data application to anti-collision manoeuvring. Journal of Navigation, 68(4), 697–717.Google Scholar
Fiorini, M., Capata, A. and Bloisi, D.D. (2016). AIS Data Visualization for Maritime Spatial Planning (MSP). International Journal of e-Navigation and Maritime Economy, 5, 45–60.Google Scholar
Greidanus, H., Alvarez, M., Eriksen, T. and Gammieri, V. (2016). Completeness and Accuracy of a Wide-Area Maritime Situational Picture based on Automatic Ship Reporting Systems. The Journal of Navigation, 69(1), 156–168.Google Scholar
Harati-Mokhtari, A., Wall, A., Brooks, P. and Wang, J. (2007). Automatic Identification System (AIS): data reliability and human error implications. The Journal of Navigation, 60(3), 373–389.Google Scholar
International Maritime Organization (IMO). (2003). International Convention for the Safety of Life at Sea (SOLAS).Google Scholar
International Telecommunications Union (ITU). (2010). Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band, Recommendation ITU-R M. 1371–4.Google Scholar
Iperen, W.H. (2015). Classifying ship encounters to monitor traffic safety on the North Sea from AIS data. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation, 9(1), 51–58.Google Scholar
Iphar, C., Napoli, A. and Ray, C. (2015). Detection of false AIS messages for the improvement of maritime situational awareness. Oceans'2015. Oct 2015, Washington, DC, United States, 1–7.Google Scholar
Jaskólski, K. (2017). Two-dimensional coordinate estimation for missing Automatic Identification System (AIS) signals based on the discrete Kalman filter algorithm and Universal Transverse Mercator (UTM) projection. 52 Scientific Journals of the Maritime University of Szczecin, 52, 82–89.Google Scholar
Marine Management Organization (MMO). (2013). Spatial trends in shipping activity. Marine Management Organization.Google Scholar
Mazaheri, A., Montewka, J., Kotilainen, P., Sormunen, O.V.E. and Kujala, P. (2015). Assessing grounding frequency using ship traffic and waterway complexity. The Journal of Navigation, 68(1), 89–106.Google Scholar
Mazzarella, F., Vespe, M., Damalas, D. and Osio, G. (2014). Discovering vessel activities at sea using AIS data: Mapping of fishing footprints. International Conference on Information Fusion, Salamanca, Spain, 1–7.Google Scholar
Mou, J.M., Van Der Tak, C. and Ligteringen, H. (2010). Study on collision avoidance in busy waterways by using AIS data. Ocean Engineering, 37(5), 483–490.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013). Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction. Entropy, 15(6), 2218–2245.Google Scholar
Peters, D.J. and Hammond, T.R. (2011). Interpolation between AIS reports: probabilistic inferences over vessel path space. The Journal of Navigation, 64(4), 595–607.Google Scholar
Ristic, B., Scala, B.L., Morelande, M. and Gordon, N. (2008). Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction. IEEE International Conference on Information Fusion, 1–7.Google Scholar
Sang, L.Z., Wall, A., Mao, Z., Yan, X.P. and Wang, J. (2015). A novel method for restoring the trajectory of the inland waterway ship by using AIS data. Ocean Engineering, 110, 183–194.Google Scholar
Shelmerdine, R.L. (2015). Teasing out the detail: how our understanding of marine AIS data can better inform industries, developments, and planning. Marine Policy, 54, 17–25.Google Scholar
Silveira, P.A.M., Teixeira, A.P. and Soares, C.G. (2013). Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal. The Journal of Navigation, 66(6), 879–898.Google Scholar
Tsou, M.C. (2010). Discovering knowledge from AIS database for application in VTS. The Journal of Navigation, 63(3), 449–469.Google Scholar
Vettor, R. and Soares, C.G. (2015). Detection and analysis of the main routes of voluntary observing ships in the North Atlantic. The Journal of Navigation, 68(2), 397–410.Google Scholar
Wang, J., Zhu, C., Zhou, Y. and Zhang, W. (2017). Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering. The Journal of Navigation, 70(6), 1383–1400.Google Scholar
Wawruch, R. (2017). Ability to test shipboard automatic identification system instability and inaccuracy on simulation devices. Scientific Journals of the Maritime University of Szczecin, 52, 128–134.Google Scholar
Wu, L., Xu, Y., Wang, Q., Wang, F. and Xu, Z. (2016). Mapping global shipping density from AIS data. Journal of Navigation, 70(1), 67–81.Google Scholar
Zhang, W., Goerlandt, F., Kujala, P. and Wang, Y. (2016). An advanced method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 124(1), 141–156.Google Scholar
Zhen, R., Jin, Y., Hu, Q., Shao, Z. and Nikitakos, N. (2017). Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier. The Journal of Navigation, 70(3), 648–670.Google Scholar
Zhou, M., Chen, J., Ge, Q. and Huang, X. (2013). AIS data based identification of systematic collision risk for maritime intelligent transport system. IEEE International Conference on Communications, 6158–6162.Google Scholar