7 research outputs found
Detection of Abnormal Vessel Behaviours Based on AIS Data Features Using HDBSCAN+
Achieving maritime security is challenging due to the vastness and complexity of the domain. Monitoring all Achieving maritime security is challenging due to the vastness and complexity of the domain. Monitoringall vessels that use this medium is humanly impossible but is needed for law enforcement. This paper proposes amachine learning solution based on HDBSCAN+ to classify the movements of vessels into ‘normal’ or ‘abnormal’.This classification reduces the number of vessels that have to be monitored by law enforcement agencies to amanageable size. To date, AIS is the primary source of information that can represent vessel movements andenable the detection of maritime anomalies. The proposed model uses latitude, longitude, type of vessel, courseand speed as features of the AIS data for analysis. The performance of the proposed model is validated against the marine incidents reported by Information Fusion Centre-Indian Ocean Region (IFC-IOR). The proposed model has successfully detected the incidents reported by IFC-IOR
COVID-19 Impact on Global Maritime Mobility
To prevent the outbreak of the Coronavirus disease (COVID-19), many countries
around the world went into lockdown and imposed unprecedented containment
measures. These restrictions progressively produced changes to social behavior
and global mobility patterns, evidently disrupting social and economic
activities. Here, using maritime traffic data collected via a global network of
AIS receivers, we analyze the effects that the COVID-19 pandemic and
containment measures had on the shipping industry, which accounts alone for
more than 80% of the world trade. We rely on multiple data-driven maritime
mobility indexes to quantitatively assess ship mobility in a given unit of
time. The mobility analysis here presented has a worldwide extent and is based
on the computation of: CNM of all ships reporting their position and
navigational status via AIS, number of active and idle ships, and fleet average
speed. To highlight significant changes in shipping routes and operational
patterns, we also compute and compare global and local density maps. We compare
2020 mobility levels to those of previous years assuming that an unchanged
growth rate would have been achieved, if not for COVID-19. Following the
outbreak, we find an unprecedented drop in maritime mobility, across all
categories of commercial shipping. With few exceptions, a generally reduced
activity is observable from March to June, when the most severe restrictions
were in force. We quantify a variation of mobility between -5.62% and -13.77%
for container ships, between +2.28% and -3.32% for dry bulk, between -0.22% and
-9.27% for wet bulk, and between -19.57% and -42.77% for passenger traffic.
This study is unprecedented for the uniqueness and completeness of the employed
dataset, which comprises a trillion AIS messages broadcast worldwide by 50000
ships, a figure that closely parallels the documented size of the world
merchant fleet
Intent-informed state estimation for tracking guided targets
This paper proposes a state estimation and prediction for tracking guided targets using intent information. A conditionally Markov process is used to describe the destination-oriented target motion, and the collision intent is incorporated through the zero-effort-miss guidance information. The expected arrival time necessary for the conditionally Markov model is determined through the collision geometry and destination motion. Finally, the Kalman filter technique is used to estimate and predict the target state. Numerical simulations demonstrate that the proposed approach can improve state estimation accuracy in both static and dynamic destination cases
Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks
Data-driven methods open up unprecedented possibilities for maritime
surveillance using Automatic Identification System (AIS) data. In this work, we
explore deep learning strategies using historical AIS observations to address
the problem of predicting future vessel trajectories with a prediction horizon
of several hours. We propose novel sequence-to-sequence vessel trajectory
prediction models based on encoder-decoder recurrent neural networks (RNNs)
that are trained on historical trajectory data to predict future trajectory
samples given previous observations. The proposed architecture combines Long
Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data
and generate future predictions with different intermediate aggregation layers
to capture space-time dependencies in sequential data. Experimental results on
vessel trajectories from an AIS dataset made freely available by the Danish
Maritime Authority show the effectiveness of deep-learning methods for
trajectory prediction based on sequence-to-sequence neural networks, which
achieve better performance than baseline approaches based on linear regression
or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation
of results shows: i) the superiority of attention pooling over static pooling
for the specific application, and ii) the remarkable performance improvement
that can be obtained with labeled trajectories, i.e., when predictions are
conditioned on a low-level context representation encoded from the sequence of
past observations, as well as on additional inputs (e.g., port of departure or
arrival) about the vessel's high-level intention, which may be available from
AIS.Comment: Accepted for publications in IEEE Transactions on Aerospace and
Electronic Systems, 17 pages, 9 figure
Multiple Ornstein-Uhlenbeck Processes for Maritime Traffic Graph Representation
We propose an unsupervised procedure to automatically extract a graph-based model of commercial maritime traffic routes from historical Automatic Identification System (AIS) data. In the proposed representation, the main elements of maritime traffic patterns, such as maneuvering regions and sea-lanes, are represented, respectively, with graph vertices and edges. Vessel motion dynamics are defined by multiple Ornstein- Uhlenbeck (OU) processes with different long-run mean parameters, which in our approach can be estimated with a change detection procedure based on Page's test, aimed to reveal the spatial points representative of velocity changes. A density-based clustering algorithm (DBSCAN) is then applied to aggregate the detected changes into groups of similar elements and reject outliers. To validate the proposed graph-based representation of the maritime traffic, two performance criteria are tested against a real-world trajectory data set collected off the Iberian Coast and the English Channel. Results show the effectiveness of the proposed approach, which is suitable to be integrated at any level of a JDL system