{"id":832,"date":"2021-03-21T18:25:06","date_gmt":"2021-03-21T18:25:06","guid":{"rendered":"https:\/\/localhost\/monitraffic\/?page_id=832"},"modified":"2023-11-14T15:37:26","modified_gmt":"2023-11-14T15:37:26","slug":"task-4","status":"publish","type":"page","link":"https:\/\/monitraffic.tecnico.ulisboa.pt\/?page_id=832","title":{"rendered":"Task 4"},"content":{"rendered":"\t\t
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Task 4<\/h1>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Integrated system for maritime traffic characterisation and monitoring<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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This task has addressed the process of development of a data-based maritime traffic analysis platform for ship trajectory data provided by the Automatic Identification System (AIS). The proposed platform is implemented to simplify and integrate the areas of data processing, data storage and data analysis into a single robust, flexible and expandable base. The platform consists of several modules\/tools developed to attend to the needs of each of the data handling areas. Each module is built with efficient tools and strategies applicable to Big Data handling to allow the future development of the platform. Particular attention is given to the data enrichment capabilities of the platform that include to reconstruction of the sea and weather conditions along the historical ship trajectories using the Copernicus Climate Data.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\tMaritime traffic analysis platform<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

The usage of AIS data in research on maritime traffic topics is heavily dependent on three factors: the reliability, availability, and ease of use of the data. To directly address these issues and offer a solution to them, a platform for maritime traffic analysis is developed. The platform\u2019s main body is built with Python and SQL languages, allowing for exceptionally large amounts of data to be stored and retrieved with ease. Several tools and software libraries that have been developed for data decoding, handling, processing, and visualisation as open-source libraries for Python are adopted in the development of the platform.<\/p>\n\n

The developed maritime traffic analysis platform consists of three main modules which are subdivided into components that are developed to address more specific issues using available tools and software libraries (Figure 1):\n

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  1. Data Processing<\/strong> module unites every component directly related to the decoding and treatment of the raw AIS data. Several different methods of data handling and processing are applied in this module to simplify the development of the two other modules.<\/li>\n \t
  2. Data Storage<\/strong> module handles the interactions with the database, facilitating the storage and retrieval of data from the platform.<\/li>\n \t
  3. Data Analysis<\/strong> module includes several methods of analysis, such as visual, using maps and graphs, or statistical, such as the analysis of historic data of an area.<\/li>\n<\/ol><\/p>\n\n

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    \n \"Modules\n
    \n Figure 1 – Modules of the maritime traffic analysis platform<\/small><\/strong>\n <\/figcaption>\n <\/figure>\n<\/div><\/p>\n\n

    The open-source library Pandas which provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language is used to facilitate data handling. Several operations are carried out using the NumPy library and the images are created using Matplotlib. The PYAIS library is used to decode the raw messages, and the RDP library is used to compress the ship trajectories using the Douglas-Peucker algorithm. The storage is handled with PostgreSQL, an open-source object-relational database. This database has good integration with Python, allowing for a smooth and predictable joint operation between the Python code and the database.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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    The core of the developed platform is to process \u201craw\u201d AIS messages into useful and reliable information for several applications. For this purpose, the following four main steps should be properly taken (Figure 2):\n