ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 4. Vol. 30. 2024

DOI: 10.17587/it.30.183-189

E. A. Zaripov, Researcher, Postgraduate, A. M. Melnikov, Researcher, Postgraduate, A. S. Akopov, Chief Researcher, Dr. Tech. Sciences, Prof., Professor of the Russian Academy of Sciences,
Central Economics and mathematics Institute of the Russian Academy of Sciences, Moscow, Russian Federation, MIREA — Russian Technological University, Moscow, Russian Federation

Simulation Modeling and Optimization of Traffic Flows in Local Sections of the Street Road Network Using AnyLogic

An approach to simulation modeling and optimization of traffic flows in local sections of the street road network using AnyLogic and heuristic algorithms is presented. A feature of this approach is the use of agent-based (ABM) and discrete-event simulation methods supported in the AnyLogic system. An original swarm optimization algorithm (PSO) is proposed, modified for the problem of optimizing road traffic on a real section of the road (near the Yugo-Zapadnaya metro station in Moscow). Numerical experiments were performed to confirm the possibility of optimizing the duration of traffic light phases in order to minimize the average travel time of vehicles on a simulated section of the street road network.
Keywords: intelligent transport systems, particle swarm algorithm, traffic flow simulation, road traffic, AnyLogic

Acknowledgements: The research was supported by a grant from the Russian Science Foundation (project no. 23-11-00080).

P. 183-189

References

1. Beklaryan A. L., Beklaryan L. A., Akopov A. Simulation model of an intelligent transport system of a "smart city" with adaptive control of traffic lights based on fuzzy clustering, Business Informatics, 2023, vol. 17, no. 3, pp. 70—86.
2. Akopov A. S., Beklaryan L. A., Beklaryan A. L. Optimising Characteristics of an Intelligent Transport System Using a Real-Coded Genetic Algorithm Based on Adaptive Mutation, Informacionnye Tehnologii, 2023, vol. 29, no. 3, pp. 115—125.
3. Akopov A. S., Beklaryan L. A., Beklaryan A. L., Be-lousov F. A. Simulation of Motion of an Ensemble of Unmanned Ground Vehicles Using FLAME GPU, Informacionnye Tehnologii, 2021, vol. 27, no. 7, pp. 369—379.
4. Akopov A. S., Beklaryan L. A., Khachatryan N. K., Beklaryan A. L., Kuznetsova E. V. Multi-Agent Control System for Unmanned Ground Vehicles, Informacionnye Tehnologii, 2020, vol. 26, no. 6, pp. 342—353.
5. Veremchuk N. S. Elements of simulation modeling in issues of traffic optimization, Vestnik kibernetiki, 2022, vol. 4, no. 48, pp. 23—28.
6. Zaripov E. A., Petrunev E. A. Development of a Neural Network for modeling the behavior of the Educational Process, Artificial societies, 2023, vol. 18, no. 1, available at: https://artsoc.jes.su/s207751800024453-7-1/.
7. Melnikov A. M. Agent-based modeling of the sleep-wake cycle, Artificial societies, 2023, vol. 18, no. 2, available at: https:// artsoc.jes.su/s207751800024523-4-1/.
8. Metropolis N., Ulam S. The Monte Carlo Method, Journal of the American Statistical Association, 1949, vol. 44, no. 247, pp. 335—341.
9. Thomopoulos N. T. Essentials of Monte Carlo Simulation: Statistical Methods for Building Simulation Models, New York, Springer, 2013.
10. Okun E. V., Gruber T. A. Application of numerical Monte Carlo methods in simulation modeling, Young researcher of the Don, 2021 vol. 4, no. 31, pp. 8—11.
11. Coello Coello C. A., Lechuga M. S. BOPSO: a proposal for multiple objective particle swarm optimization, in Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), Honolulu, HI, USA, 2002, vol. 2, pp. 1051—1056.
12. Xiaohui H., Eberhart R. Multiobjective optimization using dynamic neighborhood particle swarm optimization, In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600), Honolulu, HI, USA, 2002, vol. 2, pp. 1677—1681.
13. Yen G. G., Leong W. F. Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization, IEEE Transactions on Systems, Man, and Cybernetics — Part A: Systems and Humans, 2009, vol. 39, no. 4, pp. 890—911.
14. Karpenko A. P., Seliverstov E. Y. Review of particle swarm methods for the global optimization problem (particle swarm optimization), Science and education: scientific publication of MSTU, N. E. Bauman, 2009, no. 3, pp. 2.
15. Borshchev A., Grigoryev I. The Big Book of Simulation Modeling Multimethod Modeling with AnyLogic 8, available at: https://www.anylogic.com/resources/books/big-book-of-simulation-modeling/ (retrieved 09/28/2023).

To the contents