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ROUTE OPTIMIZATION IN A VARIABLE ENVIRONMENT

EDN: THOPRN

Abstract

The article explores the methods of forming routes in conditions of changing traffic conditions or a dynamic situation surrounding the vehicle. These conditions are called conditions of increased variability, and such routes are called variable routes. Variable routes arise in a metropolis in conditions of unsteady traffic flows, in conditions of counteraction to movement, in some types of tourist services, when there is an unpredictable interest in visiting an object. A feature of variable routes is their unpredictability and deviation from the original plan, they are characterized by information uncertainty, and decision support information systems are used to form and reshape them. To account for and select alternative routes, an agent-based approach was used in this work.

About the Authors

V. A. Mordvinov
RTU MIREA
Russian Federation

phD., professor



V. T. Matchin
RTU MIREA
Russian Federation

senior lecturer



S. Ya. Prokofiev
RTU MIREA
Russian Federation

master



A. E. Romanchenko
RTU MIREA
Russian Federation

assistent



References

1. Kuzhelev P.D. Principles of Transport Management in a Megacity // Science and Technology of Railways. – 2017. – Vol. 1. – No. 1(1). – Pp. 27–33. EDN: YLOJUB

2. Rogov I.E. Organic and Situational Analysis in Transport Management in a Megacity // Science and Technology of Railways. 2022. Vol. 6. No. 1(21). – Pp. 25–33. EDN: XUDPGE

3. Kozlov A.V. Multipurpose Transport Management in a Megacity // Science and Technologies of Railways. – 2018. – Vol. 2. – 4(8). – Pp. 40-47. EDN: YSUNQL

4. Tsvetkov V.Ya., Shorygin S.M. Dynamic Information Situation for Overcoming Missile Defense // Vestnik MGTU MIREA. - 2014 - No. 3 (4). - Pp. 85-100. EDN: SMTMRL

5. Rogov I. E. The use of multi‑agent systems in managing the transport system of a metropolis // Science and Technologies of Railways. - 2020. Vol. 4. - No. 1(13). - Pp. 26-36. EDN: FOVCCT

6. Shchennikov A.E. Models of Direct Algorithms // Slavyansky Forum. - 2017. - 4(18). - Pp. 103-109. EDN: YOBAYQ

7. Tsvetkov V.Ya., Kozlov A.V. Algorithm of Subsidiary Metaheuristics // Educational Resources and Technologies. - 2022. - No. 4 (41). - Pp. 87-95. EDN: UBFSAX

8. Yandex. Maps: official website. - URL: https://yandex.ru/maps(rev.20.05.23).

9. Aurama mini: official website. - URL: https://mini.aurama.ru/(rev.10.05.23).

10. Google Maps: official website. - URL: https://developers.google.com/maps/documentation?hl=en (accessed on 27.05.23).

11. Poncy: official website. - URL: https://poncy.su/ (accessed on 10.05.23).

12. Kaliberda E. A. et al. “Ant” algorithm in solving the traveling salesman problem //Applied Mathematics and Fundamental Informatics. - 2020. - Vol. 7. - No. 2. - Pp. 10-17. EDN: SKFKZZ

13. Tsvetkov V. Ya. Trade-off transportation problem. Proceedings of the National Academy of Sciences of the Republic of Kazakhstan. Series of Geology and Technical Sciences. 2019. Vol. 3. No. 435. Pp. 109-113. DOI: 10.32014/2019.2518-170X.75 EDN: SGZJAJ 14. Karpova I. P. On a bio‑inspired approach to robot orientation, or a real “ant” algorithm //Management of Large Systems: Collection of Works. – 2022. – No. 96. – Pp. 69–117. EDN: VKEWXL

14. Bogoutdinov B.B., Tsvetkov V.Ya. Application of the complementary resources model in investment activities // Vestnik Mordovskogo Universiteta. – 2014. – Vol. 24. No. 4. – Pp. 103–116. EDN: TDWWIP

15. Rosenberg I.N., Tsvetkov V.Ya. Application of multi-agent systems in intelligent logistics systems. // International Journal of Experimental Education. – 2012. – No. 6. – Pp. 107–109. EDN: RAKERL

16. Pavlov A.I. Geoservice as a direction in computer science and geoinformatics // Slavyansky Forum. -2020. - 2(28). - pp. 7–14. EDN: MJDTUO

17. Tsvetkov V.Ya. Designing Data Structures and Databases — Moscow: Moscow State University of Geodesy and Cartography, 1997. — 90 p. EDN: RQCSER

18. Colorni A., Dorigo M., Maniezzo V. An Investigation of some Properties of an “Ant Algorithm” // Ppsn. — 1992. — Vol. 92. — No. 1992.

19. Dorigo M. et al. Evolving self-organizing behaviors for a swarm-bot //Autonomous Robots. - 2004. - Vol. 17. - No. 2-3. - Pp. 223-245. EDN: BFXXGA

20. Kozlov A. V. Two-algorithmic system for controlling moving objects // Science and Technologies of Railways. - 2020. Vol. 4. - 1(13). - Pp. 37-45. EDN: QDSTGX

21. Tsvetkov V.Ya. Paralinguistic informational units in education// Prospects of Science and Education. – 2013. – 4(4). – P.30-38. EDN: QZYHZR

22. Deneubourg J. L. et al. Error, communication and learning in ant societies //European Journal of Operational Research. – 1987. – Vol. 30. – No. 2. – Pp. 168–172.

23. Goss S. et al. Self-organized shortcuts in the Argentine ant //Naturwissenschaften. – 1989. – Vol. 76. – No. 12. – Pp. 579–581. EDN: HHCLMO

24. Tsvetkov V.Ya. Application of the subsidiarity principle in the information economy // Financial Business. -2012. - No. 6. - Pp. 40-43. EDN: QAGGBF

25. “System Requirements: FURPS+ Classification”: official website. - URL: https://sysana.wordpress.com/2010/09/16/furps/(rev.18.08.23).


Review

For citations:


Mordvinov V.A., Matchin V.T., Prokofiev S.Ya., Romanchenko A.E. ROUTE OPTIMIZATION IN A VARIABLE ENVIRONMENT. Intelligent transport. 2023;(3(27)):18-23. (In Russ.) EDN: THOPRN

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ISSN 3033-6007 (Online)