Abstract
Abstract. The maritime industry has become a major part of globalization. Political and economic actors are meeting challenges regarding shipping and people transport. The Automatic Identification System (AIS) records and broadcasts the location of numerous vessels and delivers a huge amount of information that can be used to analyze fluxes and behaviors. However, the exploitation of these numerous messages requires tools based on Big Data principles.Acknowledgement of origin, destination, travel duration and distance of each vessel can help transporters to manage their fleet and ports to analyze fluxes and focus their investigations on some containers based on their previous locations. Thanks to the historical AIS messages provided by the Danish Maritime Authority and ARLAS PROC/ML, an open source and scalable processing platform based on Apache SPARK, we are able to apply our pipeline of processes and extract this information from millions of AIS messages. We use a Hidden Markov Model (HMM) to identify when a vessel is still or moving and we create “courses”, embodying the travel of the vessel. Then we derive the travel indicators. The visualization of results is made possible by ARLAS Exploration, an open source and scalable tool to explore geolocated data. This carto-centered application allows users to navigate into the huge amount of enriched data and helps to take benefits of these new origin and destination indicators. This tool can also be used to help in the creation of Machine Learning algorithms in order to deal with many maritime transportation challenges.
Highlights
Maritime activity has significantly increased over the last two centuries
We use a dataset composed of Automatic Identification System (AIS) messages provided by the Danish Maritime Authority
The results are visible with ARLAS Exploration, an open-source solution (ARLAS Development Team, 2021a) we have developed to visualize and explore interactively such amounts of geographical data
Summary
Maritime activity has significantly increased over the last two centuries. The management of the goods and people transportation has become an important challenge for political and economic actors. 90% of transportation of goods is globally carried out by more than 80,000 service vessels. 90% of transportation of goods is globally carried out by more than 80,000 service vessels To manage these fluxes and improve the vessel's safety, the Automatic Identification System (AIS) records and broadcasts their location in almost real time. It delivers more than 520 million messages per day from more than 180,000. It provides a reliable source of information for understanding maritime activity, but it requires powerful tools for handling such voluminous data. We use a dataset composed of AIS messages provided by the Danish Maritime Authority This extraction follows several steps, adapted to the volume of data thanks to ARLAS PROC/ML, an open source and scalable processing platform. We will see how this valuable information can be extracted from such amounts of AIS messages thanks to these tools
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