Features of planning air traffic using weather maps constructed with application of the Big Data technologies

Authors: Vlasov A.I., Novikov P.V., Rivkin A.M. Published: 23.12.2015
Published in issue: #6(105)/2015  
DOI: 10.18698/0236-3933-2015-6-46-62

Category: Informatics, Computer Engineering and Control  
Keywords: 4D path, air traffic, weather, GRIB, Big Data

This article discusses both the problems of efficient use of the Russian airspace and the ways to solve the problems. The methods ofquality assurance while planning the aircraft movements are described in detail. It is shown that the effective usage of the available resources requires determination of the aircraft’s position during all phases of the flight, i.e. its 4D flight trajectory, with precise accuracy. This can be achieved by processing the information about the external factors affecting the aircraft, which is provided by weather forecast. However, the weather data required for processing is too big to be evaluated by conventional means within the reasonable time. To solve this problem, some modern technologies such as Big Data are used.


[1] Air Code of the Russian Federation. Moscow, Omega-L Publ., 2005. 64 p.

[2] Aeronautical Information Publication of the Russian Federation. Moscow, CAICA 2008.

[3] Novikov P.V. Algorithm precision calculations meteodobavki wind speed for the flight portion of the air assets. Nauka i obrazovanie. MGTU im. N.E. Baumana. [Science and Education of the Bauman MSTU. Electronic Journal], 2012, no. 11, pp. 5-7. Available at: http://elibrary.ru/item.asp7idM7105440

[4] World Meteorological Organization. Technical Regulations: Sat key documents. No. 2, vol. 2. Meteorological Service for International Air Navigation. World Meteorological Organization. Geneva, WMO Secretariat, 2007. 180 p.

[5] Aircraft Operations. International Civil Aviation Organization - ICAO, 2006. 386 p.

[6] Manual on Codes. Vol. 1.2. International Codes. World Meteorological Organization. Geneva, WMO Secretariat, 2008, pp. 25-192.

[7] Rivkin A.M. Model aircraft flight at flight level. Nauka i obrazovanie. MGTU im. N.E. Baumana [Science and Education of the Bauman MSTU. Electronic Journal], 2011, no. 11, pp. 15-15.

[8] Rivkin A.M. Krossplatformennyy konvertor GRIB formata meteodannykh dlya sistemy upravleniya poletami po eshelonam [Cross-platform format converter GRIB weather data for flight control system by tiers]. Moscow, MGTU im. N.E. Baumana Publ., 2012. 97 p.

[9] Vlasov A.I. Spatial model of an assessment of evolution of methods of visual design of difficult systems. Datchiki i sistemy [Sensors and systems], 2013, no. 9 (172), pp. 10-28 (in Russ.).

[10] Chernyak L. Big Data - a new theory and practice. Otkrytye sistemy. SUBD [Open Systems. DBMS], 2011, no. 10. Available at: http://www.osp.ru/os/2011/10/13010990/ (accessed 01.12.2013).

[11] Dana Blankenhorn Shared nothing coming to open source. ZDNet (27 February 2006). Available at: http://www.zdnet.com/blog/open-source/shared-nothing-coming-to-open-source/580 (accessed 01.12.2013).

[12] The Case for Shared Nothing Architecture by Michael Stonebraker [Originally published in Database Engineering, vol. 9, по. 1 (1986)], pp. 1-5.

[13] Gartner Says Solving ’Big Data’ Challenge Involves More Than Just Managing Volumes of Data. Available at: http://www.gartner.com/newsroom/id/1731916 (accessed 01.12.2013).

[14] Chernyak L. Troubles DBMS. Otkrytye sistemy [Open Systems], 2012, no. 2. Available at: http://www.osp.ru/os/2012/02/13014107/ (accessed 01.12.2013).

[15] Olenin O. NoSQL: back to the future. Otkrytye sistemy [Open Systems], 2012, no. 2. Available at: http://www.osp.ru/os/2012/02/13012856/ (accessed 01.12.2013).

[16] Chernyak L. MapReduce - future database. Otkrytye sistemy. SUBD [Open Systems. DBMS], 2009, no. 02. Available at: http://www.osp.ru/os/2009/02/7322603/ (accessed 01.12.2013).

[17] Dean J., Ghemawat S. MapReduce: Simplified data processing on large clusters. Proceedings of the Sixth Conference on Operating System Design and Implementation. Berkeley, CA, 2004, pp. 137-149.

[18] White T. Hadoop. Detailed guidance - Hadoop: The Definitive Guide. St. Petersburg, Peter, 2013, pp. 19-435.

[19] Chernyak L. MapReduce - Hadoop against Big Data. Otkrytye sistemy. SUBD [Open Systems. DBmS], 2010, no. 07. Available at: http://www.osp.ru/os/2010/07/13004186/ (accessed 01.12.2013).

[20] Vlasov A.I., Tsyganov I.G. Adaptive filtration of information streams in corporate systems on the basis of the mechanism of vote of users. Informatsionnye tekhnologii [Information technologies], 2004, no. 9, p. 12 (in Russ.).

[21] Preimesberger Ch. Hadoop, Yahoo, ’Big Data’ Brighten BI Future. EWeek (15 August 2011), pp. 1-6.