Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction

Abstract

Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection) was developed for different levels of intermittency (i.e., sensor coverage and performance), persistence (i.e., time lag between subsequent observations) and data sources (i.e., ground-based and space-based receivers).

Radiolocation and tracking of automatic identification system signals for maritime situational awareness

“…where v P is the maximum likelihood estimate and TDoA R is an estimate of the TDoA covariance given in (6).…”

“…the European SafeSeaNet or the Mediterranean AIS Regional Exchange System -MAREΣ), and satellite receiver constellations [5], the system progressively proved effective for maritime surveillance and traffic monitoring, enabling far-reaching applications such as traffic knowledge discovery, route prediction and anomaly detection. The latter can target particular lowlikelihood motion trajectories ( [6], [7]), alerts such as sailing in restricted areas, abrupt changes of direction (an extensive overview of this rules is presented in [8]) or anomalies related to wrong AIS message information either unintentional or deliberate such as spoofing. As an example, false GNSS tracking information can be produced to simulate specific trajectories [9] or false AIS messages can be generated and transmitted at VHF as recently demonstrated in [10].…”

Maritime pattern extraction and route reconstruction from incomplete AIS data

“…Pallotta et al [4] define the TREAD methodology that uses incremental DBSCAN to isolate turning points, and connects them afterward to get shipping lanes. Lei et al [10] developed the TMP algorithm that also uses DBSCAN to discover "hot regions" using them into a modified probabilistic suffix tree to obtain the probability distribution for discrete events occurring in a sequence, thus discovering moving behavior.…”

“…To obtain current positions of the vessels we use publicly available Automatic Identification System (AIS) data and zoom in on two specific regions in the Netherlands. The research done by [3] and [4] shows that the basis for pattern identification is waypoint extraction, and, although standard clustering algorithms, such as DBSCAN, can be used for this task, the problem of varying traffic density requires a different approach. In [3], we have shown that a genetic algorithm (GA) can be a viable alternative to other machine learning approaches, and although it is resource intensive, it can deliver accurate results once good criteria for the GA fitness function have been found.…”

Detecting Vessels Carrying Migrants Using Machine Learning

“…(IMO, 2016). Despite being conceived for collision avoidance, AIS is nowadays a cornerstone of maritime surveillance, and is currently used by the scientific community to extract patterns, predict routes and detect anomalies (e.g Pallotta et al 2013) …”