Unsupervised Maritime Traffic Graph Learning with Mean-Reverting Stochastic Processes

Inspired by the fair regularity of the motion of ships, we present a method to derive a representation of the commercial maritime traffic in the form of a graph, whose nodes represent way-point areas, or regions of likely direction changes, and whose edges represent navigational legs with constant cruise velocity. The proposed method is based on the representation of a ship's velocity with an Ornstein-Uhlenbeck process and on the detection of changes of its long-run mean to identify navigational way-points. In order to assess the graph representativeness of the traffic, two performance metrics are introduced, leading to distinct graph construction criteria. Finally, the proposed method is validated against real-world Automatic Identification System data collected in a large area.

[1]  Bengt Carlsson,et al.  Maritime vessel traffic modeling in the context of concept drift , 2017 .

[2]  Lokukaluge P. Perera,et al.  Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction , 2012, IEEE Transactions on Intelligent Transportation Systems.

[3]  Bradley J. Rhodes,et al.  Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness , 2007, 2007 10th International Conference on Information Fusion.

[4]  Lars Niklasson,et al.  Trajectory clustering for coastal surveillance , 2007, 2007 10th International Conference on Information Fusion.

[5]  Paolo Braca,et al.  Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Michele Vespe,et al.  Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction , 2013, Entropy.

[7]  Allen M. Waxman,et al.  Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness , 2006, 2006 9th International Conference on Information Fusion.

[8]  Paolo Braca,et al.  Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph Representation , 2018, IEEE Transactions on Aerospace and Electronic Systems.

[9]  M. Vespe,et al.  Unsupervised learning of maritime traffic patterns for anomaly detection , 2012 .

[10]  Bengt Carlsson,et al.  Potential Fields in Modeling Transport over Water , 2015 .

[11]  Giuliana Pallotta,et al.  Maritime Traffic Networks: From Historical Positioning Data to Unsupervised Maritime Traffic Monitoring , 2018, IEEE Transactions on Intelligent Transportation Systems.

[12]  Shwu-Jing Chang,et al.  Vessel route analysis by in situ measurements A pre-requisite for Maritime Spatial Planning , 2012, 2012 12th International Conference on ITS Telecommunications.

[13]  Paolo Braca,et al.  Consistent Estimation of Randomly Sampled Ornstein–Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction , 2016, IEEE Signal Processing Letters.

[14]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[15]  Paolo Braca,et al.  Performance Assessment of Vessel Dynamic Models for Long-Term Prediction Using Heterogeneous Data , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[16]  E. S. Page CONTINUOUS INSPECTION SCHEMES , 1954 .

[17]  Gian Luca Foresti,et al.  Fusion of trajectory clusters for situation assessment , 2006, 2006 9th International Conference on Information Fusion.