main| new issue| archive| editorial board| for the authors| publishing house|
Ðóññêèé
Main page
New issue
Archive of articles
Editorial board
For the authors
Publishing house

 

 


ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 8. Vol. 30. 2024

DOI: 10.17587/it.30.411-416

A. S. Filipchenko, Postgraduate student,
Federal state autonomous educational institution of higher education "Russian university of transport", Moscow

Typology of Big Data Algorithms for Solving Computing Problems in Transport

Current transport problems that use big data to organize the computing process are considered. For each problem under consideration, the algorithms used for processing and analyzing big data are determined. The selected algorithms are divided by type, type, and examples of specific algorithms and methods are given. A theoretical basis is provided for further development of the topic in the field of developing methods for modelling parallel specialized computing systems in transport.
Keywords: Big Data, algorithms, transport, computing systems

P. 411-416

References

  1. Kharkevich A. A. Information and technology, Communist, 1962, vol. 39, no. 17, pp. 93—102 (in Russian).
  2. Materials of the XXIV Congress of the CPSU, Moscow, Politizdat, 1971, 320 p. (in Russian).
  3. The "Express-2" sells tickets, Science and life, 1984, no. 4, p. 10 (in Russian).
  4. Popov B. N., Fedorina E. S. Application of methods of analysis and data processing to information flows of water transport objects, Bulletin of the State University of Marine and River Fleet named after. Admiral S. O. Makarova, 2015, no. 2(30), pp. 220—225, doi: 10.21821/2309-5180-2015-7-2-220-225, EDN TPSXIB (in Russian).
  5. Frolov A. V., Frolova E. S. Big Data and infrastructure for updating navigation data, Operation of marine transport, 2019, no. 4(93), pp. 45-47, doi: 10.34046/aumsuomt93/8, EDN VJIBNO (in Russian).
  6. Khalikov Z. A., Pereslegin S. V., Karpov I. O. Two-position quasi-mirror radar of the sea surface: mechanisms of microwave scattering and the possibility of solving oceanological problems from space, Collection of abstracts of the Fourteenth All-Russian Open Conference " Modern problems of remote sensing of the Earth from space": Electronic collection of abstracts, Moscow, November 14—29, 2016, Moscow, Institute of Space Research of the Russian Academy of Sciences, 2016, p. 55, EDN XSOPSB (in Russian).
  7. Ashimov R. N. Comparative analysis of the search algorithms for association rules Apriori and Eclat, Central Asian Scientific Journal, 2022, no. 2(6), pp. 15—19, EDN CSKZYX (in Russian).
  8. Korneeva E. V., Sidorenko V. G. Analysis of the applica­bility of the term Big Data to an automated transportation operational management system, Science and technology of transport, 2022, no. 1, pp. 70—76, EDN NUGBVS (in Russian).
  9. Urusov A. V. 50 years is just the beginning!, Automation, communications, computer science, 2021, no. 7, pp. 2—4 (in Russian).
  10. Rosenberg E. N., Ozerov A. V., Olshansky A. M. Con­struction of an adaptive planning system for trains using digital data processing methods Data Science and Big Data, Intelligent transport systems: materials of the International scientific — practical conference, Moscow, May 26, 2022, Moscow, Russian University of Transport, 2022, pp. 285—289, EDN KHVPEU (in Russian).
  11. Patent No. 2753989 C1 Russian Federation, IPC B61L 27/00. Device for constructing forecast train schedules based on big data processing methods: No. 2021102780: application. 02/05/2021: publ. 08/25/2021 / M. G. Lysikov, A. V. Ozerov, A. M. Olshansky [etc.]; applicant Joint Stock Company "Research and Design Institute of Information, Automation and Communications in Railway Transport", EDN FBKBCO (in Russian).
  12. Rosenberg E. N., Olshansky A. M., Ozerov A. V., Saf-ronov R. A. On the use of Big Data methods in the field of ensuring functional safety, Reliability, 2022, vol. 22, no. 2, pp. 38—46, doi: 10.21683/1729-2646-2022-22-2-38-46, EDN LJMCPJ (in Russian).
  13. Rosenberg E. N., Lysikov M. G., Ozerov A. V., Olshansky A. M. On the transition to predictive management of transport systems using BigData, Bulletin of the Institute for Problems of Natural Monopolies: Iron Technology expensive, 2018, no. 1(41), pp. 32—37, EDN YNWTHL (in Russian).
  14. Alekseev V. M., Baranov L. A., Kulagin M. A., Sidorenko V. G. Construction of the architecture of an intelligent control system for the city rail transport system, World of Transport, 2021, vol. 19, no. 1(92), pp. 18—46, doi: 10.30932/1992-3252-2021-19-1-18-46, EDN EJAGGL (in Russian).
  15. Vidovic K., Colic P., Vojvodic S., Blavicki A. Methodology for public transport mode detection using telecom big data sets: case study in Croatia, Transportation Research Procedia, 2022, vol. 64, pp. 76—83.
  16. Yin G., Huang Z., Yang L., Ben-Elia E., Xu L., Scheuer B., Liu Y. How to quantify the travel ratio of urban public transport at a high spatial resolution? A novel computational framework with geospatial big data, International Journal of Applied Earth Observation and Geoinformation, 2023, vol. 118.
  17. Shebe H. Autonomous driving. On methods of applying security principles, Reliability, 2019, vol. 19, no. 3(70), pp. 21—33, doi: 10.21683/1729-2646-2019-19-3-21-33, EDN KBROBR (in Russian).

To the contents