Automatic Identification System (AIS) is an automatic tracking system used on ships and by vessel traffic services. The AIS system was first introduced in 2003 and was developed to improve maritime safety conditions. However, the development in the field of database technology and cloud services in the past decade has also cleared the road for exciting commercial applications in financial forecasting and planning.
Ships send signals to each other and from ship to shore at regular intervals. An AIS transceiver sends a signal every 2 to 10 seconds. This signal includes a unique identifier of the ship, the rate of turn, speed over ground, position, type of ship, dimensions, draught and destination. The International Maritime Organization’s International Convention for the Safety of Life at Sea (SOLAS) requires AIS to be fitted aboard international voyaging ships with 300 or more gross tonnage, and all passenger ships regardless of size.
When collected and properly stored in a database, AIS unlocks large amounts of data, which is a huge source of information once it has been made suitable for statistical use. Ones processed and merged with other sources of information, the data can be used to analyze vessel movements and help give a better account of how the maritime sector operates. For example, the data can be used to analyze what effect lower fuel prices have on traffic through the Suez Canal, or how the lifting of Iran’s trade embargo has impacted traffic in the Persian Gulf. The maritime industry has always been relatively conservative and while it is considered a low-data industry, it is currently taking serious steps into the digital age.
There are many interesting commercial applications and possible use cases for AIS data. For example, AIS information will show which ship type ties up where, and how long it is berthed in each port. This information can be used to make business cases about future port developments. Tracks of fishery vessels can be followed for compliance analytics. Dredgers can be analyzed and dredger production can be estimated if the vessel movement data is combined with seabed soil condition information. Fuel efficiency of container vessels can be studied. Utilization of fleets can be monitored. Operations of crew transfer vessels around offshore wind farms can be optimized. Potential customers for repair shipyards can be traced, tug operations can be analyzed. Traffic in locks and ports can be forecasted. AIS data and analytics is used for the development of antifouling coatings on shipping vessels as well as to provide real-time reporting on movement of commodities like coal, iron ore, oil, LPG, LNG and chemicals for trading and shipping companies. CO2 emissions from ships can be analyzed. The data is furthermore likely suitable to serve as an economic indicator. Based on patterns in the transportation and destination of certain goods, international comparisons can be drawn about economic activity.
Storing and querying AIS data
For AIS analytics purposes, large amounts of data need to be collected and specialist knowledge is required to store, manage and query this data. Data alone has little value. Traditional relational database management systems have low performance when processing these volumes. Cloud based solutions like AWS Redshift, Google BigQuery or Elastic are required to query the data. Often, datasets need to be enriched with other data. For example information about vessel characteristics, weather data, terminal data, seabed information and more.
In addition, a data structure needs to be set up in which data is stored into multiple parts. For example, one is mainly for the ship type data query and another is for the region query. To increase the query performance, indexing techniques should be used to linearize the data. Finally, algorithms need to be designed to clean and analyze the data and translate the raw source into valuable insights, tables and graphs that will form the basis for data driven business cases and investment models.
The maritime industry is moving into a digital era that will be driven by a constant stream of data between industry stakeholders. AIS is playing a major role by providing global visibility and greater access to data for analysis, helping with forecasting and improved decision making. The industry is recognizing that remote access monitoring, forecasting and data analytics are not only significantly improving and optimizing operations and ship management, they are also helping to make the global maritime and offshore sector more transparent and more efficient. For commercial parties there are exciting opportunities to use AIS data for advanced modelling and financial forecasting and planning.
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