Hostname: page-component-7bb8b95d7b-w7rtg Total loading time: 0 Render date: 2024-09-06T20:49:47.924Z Has data issue: false hasContentIssue false
  • English
  • Français

Vessel Trajectory Prediction Using Historical Automatic Identification System Data

Published online by Cambridge University Press:  26 August 2020

Danial Alizadeh ,
Ali Asghar Alesheikh Open the ORCID record for Ali Asghar Alesheikh [Opens in a new window]  and
Mohammad Sharif Open the ORCID record for Mohammad Sharif [Opens in a new window]
Danial Alizadeh
Affiliation:
(Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran)
Ali Asghar Alesheikh
Affiliation:
(Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran)
Mohammad Sharif*
Affiliation:
(Department of Geography, Faculty of Humanities, University of Hormozgan, Bandar Abbas, Iran)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40·85% for the second model compared with the first model and 23% for the third model compared with the second model.

Keywords

TrajectoryMovement ModelsShip BehaviourAutomatic Identification System
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 1 , January 2021 , pp. 156 - 174
Check for updates
Copyright
Copyright © The Royal Institute of Navigation 2020

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.)

References

REFERENCES

Alsmeyer, G. (2011). Chebyshev's inequality. In: Lovric, M. (ed.). International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer.Google Scholar
Borkowski, P. (2017). The ship movement trajectory prediction algorithm using navigational data fusion. Sensors, 17, 1432.CrossRefGoogle ScholarPubMed
Dalsnes, B. R., Hexeberg, S., Flåten, A. L., Eriksen, B.-O. H. and Brekke, E. F. (2018). The Neighbor Course Distribution Method with Gaussian Mixture Models for AIS-Based Vessel Trajectory Prediction. 2018 21st International Conference on Information Fusion (FUSION). IEEE, pp. 580–587.CrossRefGoogle Scholar
Duca, A. L., Bacciu, C. and Marchetti, A. (2017). A K-Nearest Neighbor Classifier for Ship Route Prediction. OCEANS 2017-Aberdeen. Aberdeen, UK: IEEE, pp. 1–6.Google Scholar
Fujino, I., Claramunt, C. and Boudraa, A.-O. (2018). Extracting Courses of Vessels from AIS Data and Real-Time Warning Against Off-Course. Proceedings of the 2nd International Conference on Big Data Research (ICBDR 2018). Association for Computing Machinery, New York, NY, USA, pp. 62–69.CrossRefGoogle Scholar
Gan, S., Liang, S., Li, K., Deng, J. and Cheng, T. (2016). Ship Trajectory Prediction for Intelligent Traffic Management Using Clustering and ANN. 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, pp. 1–6.CrossRefGoogle Scholar
Gao, M., Shi, G. and Li, S. (2018). Online prediction of ship behavior with automatic identification system sensor data using bidirectional long short-term memory recurrent neural network. Sensors, 18, 4211.CrossRefGoogle ScholarPubMed
Gers, F. A., Schmidhuber, J. and Cummins, F. (1999). Learning to forget: Continual prediction with LSTM. 9th International Conference on Artificial Neural Networks: ICANN '99, Edinburgh, UK, pp. 850–855.CrossRefGoogle Scholar
Graser, A., Schmidt, J. and Widhalm, P. (2018). Predicting trajectories with probabilistic time geography and massive unconstrained movement data.Google Scholar
Graser, A., Schmidt, J., Dragaschnig, M. and Widhalm, P. (2019). Data-driven Trajectory Prediction and Spatial Variability of Prediction Performance in Mari-time Location Based Services. 15th International Conference on Location-Based Services.Google Scholar
Grech, M. R., Horberry, T. and Smith, A. (2002). Human Error in Maritime Operations: Analyses of Accident Reports Using the Leximancer Tool. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Los Angeles, CA: Sage Publications.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, 373–389.CrossRefGoogle Scholar
Hochreiter, S., Bengio, Y., Frasconi, P. and Schmidhuber, J. (2001). Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies. In: Kolen, J. F., & Kremer, S. C. (eds.), A Field Guide to Dynamical Recurrent Neural Networks. New York, USA: IEEE Press.Google Scholar
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735–1780.CrossRefGoogle ScholarPubMed
Kaffash-Charandabi, N., Alesheikh, A. A. and Sharif, M. (2019). A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals' contexts. Environmental Science and Pollution Research, 26, 7525–7539.CrossRefGoogle ScholarPubMed
Karatzoglou, A., Jablonski, A. and Beigl, M. (2018). A Seq2Seq Learning Approach for Modeling Semantic Trajectories and Predicting the Next Location. Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 528–531.CrossRefGoogle Scholar
Kawan, B., Wang, H., Li, G. and Chhantyal, K. (2017). Data-driven modeling of ship motion prediction based on support vector regression. Proceedings of the 58th Conference on Simulation and Modelling (SIMS 58), Reykjavik, Iceland, vol. 138, pp. 350–354.CrossRefGoogle Scholar
Kim, K.-I. and Lee, K. M. (2018). Deep learning-based caution area traffic prediction with automatic identification system sensor data. Sensors, 18, 3172.CrossRefGoogle ScholarPubMed
Mao, S., Tu, E., Zhang, G., Rachmawati, L., Rajabally, E. and Huang, G.-B. (2018). An Automatic Identification System (AIS) Database for Maritime Trajectory Prediction and Data Mining. In: Cao, J., Cambria, E., Lendasse, A., Miche, Y., Vong, C. (eds.), Proceedings of ELM-2016. Proceedings in Adaptation, Learning and Optimization, vol. 9. Springer, Cham, Switzerland.Google Scholar
Marinecadastre. (2019). Available at: www.marinecadastre.gov [Accessed 20 Dec. 2019].Google Scholar
Nishizaki, C., Terayama, M., Okazaki, T. and Shoji, R. (2018). Development of Navigation Support System to Predict New Course of Ship. World Automation Congress (WAC). IEEE.CrossRefGoogle 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, 2218–2245.CrossRefGoogle Scholar
Perera, L. P. and Soares, C. G. (2010). Ocean Vessel Trajectory Estimation and Prediction Based on Extended Kalman Filter. The Second International Conference on Adaptive and Self-Adaptive Systems and Applications, Lisbon, Portugal, pp. 14–20.Google Scholar
Qiao, S.-J., Jin, K., Han, N., Tang, C.-J. and Gesangduoji, G. (2015). Trajectory prediction algorithm based on Gaussian mixture model. Journal of Software, 26, 21–32.Google Scholar
Rodrigue, J.-P., Comtois, C. and Slack, B. (2016). The Geography of Transport Systems. New York, USA: Routledge.CrossRefGoogle Scholar
Series, M. (2010). Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band. Recommendation ITU-R M.1371-5, February 2014, Geneva, Switzerland.Google Scholar
Sharif, M. and Alesheikh, A. A. (2017). Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method. GIScience & Remote Sensing, 54, 426–452.CrossRefGoogle Scholar
Sharif, M. and Alesheikh, A. A. (2018). Context-aware movement analytics: implications, taxonomy, and design framework. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8, e1233.Google Scholar
Sharif, M., Alesheikh, A. A. and Tashayo, B. (2019). CaFIRST: A context-aware hybrid fuzzy inference system for the similarity measure of multivariate trajectories. Journal of Intelligent & Fuzzy Systems, 36(6), 5383–5395.CrossRefGoogle Scholar
Sheng, P. and Yin, J. (2018). Extracting shipping route patterns by trajectory clustering model based on automatic identification system data. Sustainability, |v10, 2327.CrossRefGoogle Scholar
Sidibé, A. and Shu, G. (2017). Study of automatic anomalous behaviour detection techniques for maritime vessels. The Journal of Navigation, 70, 847–858.CrossRefGoogle Scholar
SOLAS (2003). International Convention for the Safety of Life at Sea. London: International Maritime Organization.Google Scholar
Tang, H., Yin, Y. and Shen, H. (2019). A model for vessel trajectory prediction based on long short-term memory neural network. Journal of Marine Engineering & Technology, 1–10, https://doi.org/10.1080/20464177.2019.1665258.CrossRefGoogle Scholar
Valsamis, A., Tserpes, K., Zissis, D., Anagnostopoulos, D. and Varvarigou, T. (2017). Employing traditional machine learning algorithms for big data streams analysis: The case of object trajectory prediction. Journal of Systems and Software, 127, 249–257.CrossRefGoogle Scholar
Wijaya, W. M. and Nakamura, Y. (2013). Predicting Ship Behavior Navigating Through Heavily Trafficked Fairways by Analyzing AIS Data on Apache HBase. 2013 First International Symposium on Computing and Networking. IEEE, pp. 220–226.CrossRefGoogle Scholar
Xiao, Z., Ponnambalam, L., Fu, X. and Zhang, W. (2017). Maritime traffic probabilistic forecasting based on vessels' waterway patterns and motion behaviors. IEEE Transactions on Intelligent Transportation Systems, 18, 3122–3134.CrossRefGoogle Scholar
Zhao, L. and Shi, G. (2019). Maritime anomaly detection using density-based clustering and recurrent neural network. The Journal of Navigation, 72, 894–916.CrossRefGoogle Scholar
Zorbas, N., Zissis, D., Tserpes, K. and Anagnostopoulos, D. (2015) Predicting Object Trajectories from High-Speed Streaming Data. 2015 IEEE Trustcom/BigDataSE/ISPA. IEEE, pp. 229–234.CrossRefGoogle Scholar