Adaptive Traffic Management Software Development and Evaluation of Its Effectiveness
https://doi.org/10.31854/1813-324X-2026-12-3-129-138
EDN: XYELRO
Abstract
The relevance of this research is due to the fact that, on the one hand, the development of artificial intelligence and information technologies in general opens up new prospects for the implementation of innovative traffic management methods and algorithms in road regulation processes, and on the other hand, most existing solutions are not adaptive to changes in the intensity of vehicle traffic and are not designed to operate effectively under peak loads.
The purpose of this work is to select the boundary parameters of the developed algorithm and analyze its effectiveness in comparison with other methods of traffic control.
The paper uses methods of queuing theory and graph theory for the traffic model, and also applies a multi-agent approach and reinforcement learning for the algorithm of traffic light phases control, simulation modeling is used to evaluate the system efficiency.
Results. In the course of solving the scientific problem, the functional requirements of the developed software were formed. A series of experiments was conducted on the simulation of the traffic management process under various scenarios. A simulation model of a road section in the Nevsky District of St. Petersburg was implemented. A number of experiments were conducted on the simulation of traffic situations and the optimization of the duration of traffic light phases, as well as the identification of the boundary parameters for the effective functioning of the system. A comparative analysis of traffic management methods was conducted.
The scientific novelty of the work is determined by the author's approach to combining methods of road traffic management and creating an algorithm that identifies the most suitable methods for the current traffic situation.
The practical significance of the developed solution lies in the fact that it is more effective than other algorithms considered in the article and can be used for managing road traffic and analyzing the efficiency of the road network.
About the Authors
D. A. Pelikh
The Bonch-Bruevich Saint Petersburg State University of Telecommunications
Russian Federation
Russian Federation
N. V. Shatalova
Solomenko Institute of Transport Problems of the Russian Academy of Sciences
Russian Federation
Russian Federation
References
1. Malygin I.G. Problems and Perspectives of Using Intellectual Transport Safety Systems in the Megacities. Proceedings of the All-Russian Scientific and Practical Conference on Technologies of Building Cognitive Transport Systems, 30–31 May 2018, St. Petersburg, Russian Federation. St. Petersburg: N.S. Solomenko Institute of Transport Problems of the Russian Academy of Sciences Publ.; 2018. p.15–20. (in Russ.) EDN:XRWWTJ
2. Shatalova N.V., Oreshkina A.D. Technological principles of intelligent transport systems. Proceedings of the IVth International Scientific and Practical Conference on the NTI Avtonet "Promising Transport Technologies", 8 July 2025, Yekaterinburg, Russian Federation. Moscow: Moscow Polytechnic University Publ.; 2025. p.159–164. (in Russ.) EDN:LBQFJM
3. Bondarenko I.B., Pelikh D.A. Design the Architecture of a Multi-Agent System in Solving Specific Urban Management Tasks. Scientific and Technical Volga Region Bulletin. 2025;2:48–51. (in Russ.) EDN:DRPBFF
4. Konovalova T.V., Nadiryan S.L., Izyumsky A.A., Kotsurba S.V. Elements of Road Infrastructure and Their Impact on Road Safety. International Journal of Advanced Studies: Transport and Information Technologies. 2022;12(2):49–68. (in Russ.) DOI:10.12731/2227-930X-2022-12-2-49-68. EDN:OKEKTL
5. Ziarmand A.N., Khakhanov V.I. Models and Methods of Transport Monitoring and Control. Radioelectronics & Informatics. 2016;3:64–80. (in Russ.)
6. Elkin D.M., Vyatkin V.V. Towards IoT in Traffic Control: Review of Existing Methods of Road Traffic Regulation. Izvestiya SFedU. Engineering Sciences. 2019;5(207):100–113. (in Russ.) DOI:10.23683/2311-3103-2019-5-100-113. EDN:XZWTPC
7. Gorodetskii V.I., Karsaev O.V., Samoilov V.V., Serebryakov S.V. Development Tools for Open Agent Networks. Journal of Computer and Systems Sciences International. 2008;47(3):429–446. DOI:10.1134/S1064230708030131. EDN:LLHPLJ
8. Rodzina L.S. Applied Multi-Agent Systems. Programming on the JADE Platform. Saarbrucken: LAP LAMBERT Academic Publishing GmbH&Co; 2011. 174 p. (in Russ.)
9. Masloboev A.V. Software Agents Interaction Models and Algorithms in the Virtual Business Environment of Innovation Development. Vestnik of MSTU. Scientific Journal of Murmansk State Technical University. 2009;12(2):224–234. (in Russ.) EDN:KPYRAD
10. Dushkin R.V. Multi-Agent Systems for Cooperative ITS. Trends and Management. 2021;1:42–50. (in Russ.) DOI:10.7256/2454-0730.2021.1.34169. EDN:ZNESUF
11. Shepelev V.D., Almetova Z.V., Moor A.D., Bersteneva V.I. Performance Optimization of Adaptive Traffic Lights Using Machine Vision. Bulletin of South Ural State University, Series “Economics and Management”. 2020;14(1):189–196. (in Russ.) DOI:10.14529/em200119. EDN:YWEJSZ
12. Rasulmukhamedov M.M., Tashmetov K.Sh. Optimization of traffic flow control at intersections using neural network. Intellectual Technologies on Transport. 2023;S1:92–96. (in Russ.) EDN:YBZSPQ
13. Povaliaev N.D., Krylatov A.Yu., Shatalova N.V. Methods of cluster analysis of road networks for bottleneck detection and traffic optimization. T-Comm. 2025;19(7):34–40. (in Russ.) DOI:10.36724/2072-8735-2025-19-7-34-40. EDN:AALGBX
14. Akopov A.S., Zaripov E.A., Melnikov A.M. Adaptive Control of Transportation Infrastructure in an Urban Environment Using a Real-Coded Genetic Algorithm. Business Informatics. 2024;18(2):48–66. DOI:10.17323/2587-814X.2024.2.48.66. EDN:XSXDWU
15. Haupt R.L., Haupt S.E. Practical genetic algorithms. Wiley; 2004.
16. Arkhangel’sky A. Modeling the Car Motion in a Transport Flow on a Computer. Automation and modeling in design and management. 2020;1(7):26–31. (in Russ.) DOI:10.30987/2658-6436-2020-1-26-31. EDN:EBPRMN
Review
For citations:
Pelikh D.A., Shatalova N.V. Adaptive Traffic Management Software Development and Evaluation of Its Effectiveness. Proceedings of Telecommunication Universities. 2026;12(3):129-138. (In Russ.) https://doi.org/10.31854/1813-324X-2026-12-3-129-138. EDN: XYELRO
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