Abstract
Predicting the destination of vessels in the maritime industry is a problem that has seen sustained research over the last few years fuelled by an increase in the availability of Automatic Identification System (AIS) data. The problem is inherently difficult due to the nature of the maritime domain. In this paper, we focus on a subset of the maritime industry - the oil transportation business - which complicates the problem of destination prediction further, as the oil transportation market is highly dynamic. We propose a novel model, inspired by research on destination prediction and anomaly detection, for predicting the destination port- and region of oil tankers. In particular, our approach utilises a graph abstraction for aggregation of global oil tanker traffic and feature engineering, and Recurrent Neural Network models for the final port- or region destination prediction. Our experiments show promising results with the final model obtaining an accuracy score of 41% and \(87.1\%\) on a destination port- and region basis respectively. While some related works obtain higher accuracy results - notably \(97\%\) port destination prediction accuracy - the results are not directly comparable, as no related literature found deals with the problem of predicting oil tanker destination on a global scale specifically.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The regions are defined by human experts.
- 2.
Estimated time of arrival is also an important aspect of this scenario. However, this is outside the scope of this paper.
- 3.
Constant features are features that do not change during the duration of a vessel’s port-to-port voyage. For example, the length of a vessel.
- 4.
The bearing rate is the rate of turn of a vessel.
- 5.
Less-frequently visited ports are less interesting as they are a form of outlier in the dataset.
- 6.
References
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: Ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, SIGMOD 1999, pp. 49–60. Association for Computing Machinery, New York (1999). https://doi.org/10.1145/304182.304187
Arora, K.: Contrastive perplexity: A new evaluation metric for sentence level language models. CoRR abs/1601.00248 (2016). http://arxiv.org/abs/1601.00248
BigOceanData: The definitive ais handbook. http://www.marineinsight.com/wp-content/uploads/2016/11/AiS-Whitepaper.pdf
Bodunov, O., Schmidt, F., Martin, A., Brito, A., Fetzer, C.: Grand challenge: Real-time destination and ETA prediction for maritime traffic. CoRR abs/1810.05567 (2018). http://arxiv.org/abs/1810.05567
Capobianco, S., Millefiori, L.M., Forti, N., Braca, P., Willett, P.: Deep learning methods for vessel trajectory prediction based on recurrent neural networks. CoRR abs/2101.02486 (2021). https://arxiv.org/abs/2101.02486
Ghosh, S.: Why the oil tanker business boomed during covid-19 pandemic? (February 2021). https://www.marineinsight.com/know-more/oil-tanker-business-boomed-during-covid-19-pandemic/
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167 (2015). http://arxiv.org/abs/1502.03167
Nguyen, D.D., Le Van, C., Ali, M.I.: Vessel trajectory prediction using sequence-to-sequence models over spatial grid. In: Proceedings of the 12th ACM International Conference on Distributed and Event-Based Systems, DEBS 2018, pp. 258–261. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3210284.3219775
Ram, A., Sunita, J., Jalal, A., Manoj, K.: A density based algorithm for discovering density varied clusters in large spatial databases. Int. J. Comput. Appl. 3 (06 2010). https://doi.org/10.5120/739-1038
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986). https://doi.org/10.1038/323533a0
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929–1958 (2014). http://jmlr.org/papers/v15/srivastava14a.html
Varlamis, I., Tserpes, K., Etemad, M., Júnior, A.S., Matwin, S.: A network abstraction of multi-vessel trajectory data for detecting anomalies. In: Papotti, P. (ed.) Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference, EDBT/ICDT 2019, Lisbon, Portugal, March 26, 2019. CEUR Workshop Proceedings, vol. 2322. CEUR-WS.org (2019). http://ceur-ws.org/Vol-2322/BMDA_5.pdf
Wiseman, S., Rush, A.M.: Sequence-to-sequence learning as beam-search optimization. CoRR abs/1606.02960 (2016). http://arxiv.org/abs/1606.02960
Yuan, Z., Jiang, Y., Li, J., Huang, H.: Hybrid-dnns: Hybrid deep neural networks for mixed inputs. CoRR abs/2005.08419 (2020). https://arxiv.org/abs/2005.08419
Zhang, A., Lipton, Z.C., Li, M., Smola, A.J.: Dive into deep learning. CoRR abs/2106.11342 (2021). https://arxiv.org/abs/2106.11342
Zhang, C., et al.: Ais data driven general vessel destination prediction: a random forest based approach. Transp. Res. Part C: Emerg. Technol. 118, 102729 (2020). https://doi.org/10.1016/j.trc.2020.102729, http://www.sciencedirect.com/science/article/pii/S0968090X20306446
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Magnussen, B.B., Bläser, N., Jensen, R.M., Ylänen, K. (2021). Destination Prediction of Oil Tankers Using Graph Abstractions and Recurrent Neural Networks. In: Mes, M., Lalla-Ruiz, E., Voß, S. (eds) Computational Logistics. ICCL 2021. Lecture Notes in Computer Science(), vol 13004. Springer, Cham. https://doi.org/10.1007/978-3-030-87672-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-87672-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87671-5
Online ISBN: 978-3-030-87672-2
eBook Packages: Computer ScienceComputer Science (R0)