Abstract
The Automatic Identification System (AIS) is used to identify and locate active maritime vessels. Datasets of AIS messages recorded over time make it possible to model ship movements and analyze traffic events. Here, the maritime traffic is modeled using a potential fields method, enabling the extraction of traffic patterns and anomaly detection. A software tool named STRAND, implementing the modeling method, displays real-world ship behavior patterns, and is shown to generate traffic rules spontaneously. STRAND aids maritime situational awareness by displaying patterns of common behaviors and highlighting suspicious events, i.e., abstracting informative content from the raw AIS data and presenting it to the user. In this it can support decisions regarding, e.g., itinerary planning, routing, rescue operations, or even legislative traffic regulation. This study in particular focuses on identification and analysis of traffic rules discovered based on the computed traffic models. The case study demonstrates and compares results from three different areas, and corresponding traffic rules identified in course of the result analysis. The ability to capture distinctive, repetitive traffic behaviors in a quantitative, automatized manner may enhance detection and provide additional information about sailing practices.
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© 2015 Springer International Publishing Switzerland
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Osekowska, E., Carlsson, B. (2015). Learning Maritime Traffic Rules Using Potential Fields. In: Corman, F., Voß, S., Negenborn, R. (eds) Computational Logistics. ICCL 2015. Lecture Notes in Computer Science(), vol 9335. Springer, Cham. https://doi.org/10.1007/978-3-319-24264-4_21
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DOI: https://doi.org/10.1007/978-3-319-24264-4_21
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