DocumentCode :
1809665
Title :
Traffic knowledge discovery from AIS data
Author :
Pallotta, Giuliana ; Vespe, Michele ; Bryan, Karna
Author_Institution :
Centre for Maritime Res. & Experimentation (CMRE), NATO Sci. & Technol. Organ. (STO), La Spezia, Italy
fYear :
2013
fDate :
9-12 July 2013
Firstpage :
1996
Lastpage :
2003
Abstract :
Maritime Situational Awareness (i.e., an effective understanding of activities in and impacting the maritime environment) can be significantly improved by knowledge discovery of maritime traffic patterns. The recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers provides a rich source of cooperative vessel movement information. This vast amount of information can not be fully utilized by human operators and poses new storage and computational challenges. A compact representation of this rapidly increasing amount of information gives operational utility to data which would otherwise be ignored. This paper proposes an unsupervised and incremental learning approach to extract the historical traffic patterns from AIS data. The presented methodology called Traffic Route Extraction for Anomaly Detection (TREAD) effectively processes raw AIS data to infer different levels of contextual information, spanning from the identification of ports and off-shore platforms to spatial and temporal distributions of traffic routes. Furthermore, the accurate understanding of the historical traffic enables the classification and prediction of vessel behaviours as well as the detection of low-likelihood behaviours, or anomalies. The ultimate goal is to provide operators with a configurable knowledge framework supporting day by day decision making and general awareness of vessel pattern of life activity. The methodology is demonstrated via a real-world case study, which can be used as a reference data set for further analysis.
Keywords :
data mining; marine engineering; traffic information systems; unsupervised learning; TREAD; automatic identification system; configurable knowledge framework; historical traffic patterns; incremental learning approach; low-likelihood behaviours; maritime situational awareness; maritime traffic patterns; operational utility; raw AIS data; traffic knowledge discovery; traffic route extraction for anomaly detection; unsupervised learning approach; Data mining; Entropy; Knowledge discovery; Receivers; Surveillance; Trajectory; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2013 16th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-605-86311-1-3
Type :
conf
Filename :
6641250
Link To Document :
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