Visualising maritime vessel open data for better situational awareness in ice conditions
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The document discusses the visualization of maritime vessel open data to enhance situational awareness in ice conditions due to the high risk of collisions. It presents a proof-of-concept architecture that utilizes machine learning and open data to predict and prevent collisions, aiming to improve operational efficiency and safety in maritime traffic. The approach emphasizes the importance of real-time analytics and the application of open science principles to enrich data sources and support better decision-making in icy conditions.
Visualising maritime vessel open data for better situational awareness in ice conditions
- 1. www.hamk.fi Visualising maritime vessel open data for better situational awareness in ice conditions Jari Jussila † HAMK Timo Lehtonen, Jari Laitinen SOLITA Markus Makkonen, Lauri Frank JYU
- 2. www.hamk.fi Motivation for studying maritime traffic in ice conditions 1) In Northern Baltic Sea 21 ship-to-ship collisions occured during 2007-2013 3) A maritime vessel on a collision course in ice conditions has approximately only 3-4 minutes time to react to the abnormal speed changes of the vessel in front in order to avoid a collision 2) Ship collisions are expensive and cause disturbances in the supply chain
- 3. www.hamk.fi We claim that collisions can be predicted with open data – and could be avoided by early warning Source: MarineTraffic: www.marinetraffic.com Source: National Centers for Environmental Information Weather and Climate Toolkit: https://www.ncdc.noaa.gov/
- 4. www.hamk.fi Proof-of-concept architecture for Ice Machine Learning Marine open data from Finnish waterways: http://digitraffic.liikennevirasto.fi/en/marine-traffic/ Ice conditions open data from Baltic Sea: https://en.ilmatieteenlaitos.fi/ice-conditions
- 5. www.hamk.fi Vessels getting stuck on ice Vessel speed over ground (sog) 0-12 Ice thickness 0-1.4 A1 = vessel at port A2 = speed falls to zero, possibly planned stop to wait for icebreaker assistance A3 = small change on route A4 = two sudden stops (getting stuck)
- 6. www.hamk.fi A journey of three vessels ending up to collision A1 = Icebreaker Sisu (indicated by blue color) A2 = Merchant vessel Aura, being towed by Sisu (indicated by orange color) A3 = Merchant vessel BBC Virginia (indicated by green color) Critical speed pattern
- 7. www.hamk.fi Discussions and conclusions • We introduced a solution that can provide real-time or near-real-time descriptive, diagnostic and predictive analytics for improving maritime vessel situational awareness in ice conditions • We applied a triple open science principle: open data, open source (https://github.com/timole/iceml/), and open access • The introduced IceML proof of concept demonstrates that by enriching and combining open data sources more valuable applications can be developed • The introduced data science approach could also be used to improve the quality of AIS data, e.g. user inputted vs data-driven estimated time of arrival • Better situational awareness can lead to more optimal use of ice breakers or, e.g., for companies to take proactive business actions in the supply chain to mitigate the losses resulting from a ship arriving late