Maritime pattern extraction and route reconstruction from incomplete AIS data
Tóm tắt
Effective barge scheduling in the logistic domain requires advanced information on the availability of the port terminals and the maritime traffic in their vicinity. To enable a long-term prediction of vessel arrival times, we investigate how to use the publicly available automatic identification system (AIS) data to identify maritime patterns and transform them into a directed graph that can be used to estimate the potential trajectories and destination points. To tackle this problem, we use a genetic algorithm (GA) to cluster vessel position data. Then, we show how to enhance the process to allow fast computation of incremental data coming from the sensors, including the importance of adding a quad tree structure for data preprocessing. Focusing on a real case implementation, characterized by partially incomplete and noisy AIS data, we show how the algorithm can handle routes intersecting the regions with missing data and the repercussions this has on the route graph. Finally, postprocessing is explained that handles graph pruning and filtering. We validate the results produced by the GA by comparing resulting patterns with known inland water routes for two Dutch provinces followed by the simulation using synthetic data to highlight the strengths and weaknesses of this approach.
Tài liệu tham khảo
Dobrkovic, A., Iacob, M.E., Van Hillegersberg, J.: Maritime pattern extraction from AIS data using a genetic algorithm. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 642–651. IEEE (2016)
Dobrkovic, A., Iacob, M.E., van Hillegersberg, J., Mes, M., Glandrup, M.: Towards an approach for long term AIS-based prediction of vessel arrival times. In: Logistics and Supply Chain Innovation, pp. 281–294. Springer (2016)
Dobrkovic, A., Iacob, M.E., van Hillegersberg, J.: Using machine learning for unsupervised maritime waypoint discovery from streaming AIS data. In: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, p. 16. ACM (2015)
Pallotta, G., Vespe, M., Bryan, K.: Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction. Entropy 15(6), 2218–2245 (2013)
Lampe, O.D., Kehrer, J., Hauser, H.: Visual analysis of multivariate movement data using interactive difference views. In: VMV, pp. 315-322 (2010)
Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf.Syst. 24(3), 45–77 (2007)
Oonk, M.: Smart logistics corridors and the benefits of intelligent transport systems. In: Transport Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment (2014)
van Riessen, B., Negenborn, R.R., Dekker, R.: Synchromodal container transportation: An overview of current topics and research opportunities. In: Computational Logistics, pp. 386-397. Springer (2015)
Lu, M., Borbon-Galvez, Y.: Advanced logistics and supply chain management for intelligent and sustainable transport. In: 19th ITS World Congress (2012)
Lei, P.-R., Su, J., Peng, W.-C., Han, W.-Y., Chang, C.-P.: A framework of moving behavior modeling in the maritime surveillance. J. Chung Cheng Inst. Technol. 40(2), 33–42 (2011)
Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 330–339. ACM (2007)
Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., Andrienko, G.: Visually driven analysis of movement data by progressive clustering. Inf. V. 7(3–4), 225–239 (2008)
Handl, J., Knowles, J.: An evolutionary approach to multiobjective clustering. IEEE Trans. Evolut. Comput. 11(1), 56–76 (2007)
Soares Júnior, A., Moreno, B.N., Times, V.C., Matwin, S., Cabral, L.D.A.F.: GRASP-UTS: an algorithm for unsupervised trajectory segmentation. Int. J. Geogr. Inf. Sci. 29(1), 46–68 (2015)
Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 593–604. ACM (2007)
Li, Z., Lee, J.-G., Li, X., Han, J.: Incremental clustering for trajectories. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1–4, 2010, Proceedings, Part II, pp. 32–46. Springer, Berlin Heidelberg (2010)
Finkel, R.A., Bentley, J.L.: Quad trees a data structure for retrieval on composite keys. Acta Inf. 4(1), 1–9 (1974)
Waterrecreatie Nederland: BRTN 2008–2013. http://waterrecreatienederland.nl/themas-projecten/landelijk-routenetwerk/brtn-2008-2013/ (2016). Accessed 29 Jan 2016
Andrienko, G., Andrienko, N., Rinzivillo, S., Nanni, M., Pedreschi, D., Giannotti, F.: Interactive visual clustering of large collections of trajectories. In: IEEE Symposium on Visual Analytics Science and Technology, 2009, VAST 2009, pp. 3-10. IEEE (2009)
Xiao, Z., Ponnambalam, L., Fu, X., Zhang, W.: Maritime traffic probabilistic forecasting based on vessels’ waterway patterns and motion behaviors. IEEE Trans. Intell. Transp. Syst. 18(11), 3122–3134 (2017)
Perera, L.P., Soares, C.G.: Ocean vessel trajectory estimation and prediction based on Extended Kalman filter. In: Proceedings of 2nd International Conference on Adaptive and Self-adaptive Systems and Applications, pp. 14–20 (2010)
Ristic, B., La Scala, B., Morelande, M., Gordon, N.: Statistical analysis of motion patterns in AIS data: anomaly detection and motion prediction. In: 2008 11th International Conference on Information Fusion, pp. 1–7. IEEE (2008)