COMPUTER ENGINEERING, INFORMATION SYSTEMS AND TECHNOLOGIES

Intellectual Information Technology for Adaptive Control of Traffic Flows in Urban Environments

Authors

V. Yu. Varnava ORCID 0009-0002-7922-3639
Vinnytsia National Technical University ROR
O. V. Silagin ORCID 0009-0006-0089-4800
Vinnytsia National Technical University ROR
DOI https://doi.org/10.31649/1997-9266-2026-187-4-34-47

Keywords

intelligent information technology traffic flows traffic flow management urban intersectio ontological modeling ontological knowledge base fuzzy logic fuzzy inference semantic data interpretation control action traffic light regulation

Abstract

The paper considers the development of an intelligent information technology for traffic flow management in urban areas, aimed at improving the efficiency of traffic regulation at city intersections. The relevance of the study is determined by the increasing intensity of traffic flows, the growing complexity of road infrastructure, and the need for adaptive responses to changes in traffic conditions. Traditional approaches to traffic light control often do not provide sufficient flexibility, since they are usually based on fixed control modes or a limited set of numerical traffic parameters. Therefore, the proposed technology is aimed at extending the functional capabilities of traffic management systems by combining ontological modeling and fuzzy logic.

The proposed intelligent information technology is based on a modular principle and provides a complete cycle of traffic information processing: from collecting data from the sensors, video cameras, GPS devices, and traffic light controllers to preprocessing, semantic interpretation, fuzzy inference, and generation of control actions. The ontological knowledge base is used for the formal representation of the transport system structure, including traffic flows, intersections, traffic directions, traffic light objects, sensors, traffic parameters, and possible control actions. As a result, numerical data are not considered separately, but are interpreted within the meaningful context of the current traffic situation.

The fuzzy module is designed to assess the state of traffic flows under conditions of uncertainty, incompleteness, and ambiguity of input information. It makes it possible to take into account intermediate traffic states that are difficult to describe using strict logical rules or fixed threshold values. The result of the technology is the formation of adaptive control decisions, including changing the duration of traffic light signals, adjusting the phases of the traffic light cycle, and giving priority to specific traffic directions. The proposed approach increases the adaptability of traffic management compared with the existing technologies, since it takes into account not only the numerical parameters of traffic flows, but also the semantic meaning of the current road situation.

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How to Cite

[1]
“Intellectual Information Technology for Adaptive Control of Traffic Flows in Urban Environments”, Вісник ВПІ, no. 4, pp. 34–47, Oct. 2026, doi: 10.31649/1997-9266-2026-187-4-34-47.

Author Biographies

V. Yu. Varnava, Vinnytsia National Technical University

Post-Graduate Student of the Chair of Computer Sciences

O. V. Silagin, Vinnytsia National Technical University

 Cand. Sc. (Eng.), Associate Professor of the Chair of Computer Sciences

References

[1] CIVITAS, “Intelligent Transport Systems and Traffic Management in Urban Areas,” CIVITAS Policy Note, 2015. [Online]. Available: https://civitas.eu/content/civitas-policy-note-intelligent-transport-systems-and-traffic-management-urban-areas-0 .
[2] V. Gandrybida, D. Bondarenko, and V. Sevastyanov, “Advancements in Automated Traffic Management Using Fuzzy Logic: Prospects and Challenges,” Information Technologies and Computer Engineering, vol. 21, no. 3, pp. 86-95, 2024. https://doi.org/10.63341/vitce/3.2024.86 .
[3] В. Варнава, і O. Сілагін, «Інтеграція онтологічних моделей і нечіткої логіки для керування транспортними потоками у містах,» Наука і техніка сьогодні, no. 4(58), pp. 2980-2994, 2026. https://doi.org/10.52058/2786-6025-2026-4(58)-2980-2994.
[4] S. Fernandez, R. Hadfi, T. Ito, I. Marsa-Maestre, and J. R. Velasco, “Ontology-Based Architecture for Intelligent Transportation Systems Using a Traffic Sensor Network,” Sensors, vol. 16, no. 8, article 1287, Aug. 2016. https://doi.org/10.3390/s16081287 .
[5] T. D. Chala, and L. T. Kóczy, “Agent-Based Intelligent Fuzzy Traffic Signal Control System for Multiple Road Intersection Systems,” Mathematics, vol. 13, no. 1, article 124, Jan. 2025. https://doi.org/10.3390/math13010124 .
[6] I. Tunc, A. Y. Yesilyurt, and M. T. Soylemez, “Different Fuzzy Logic Control Strategies for Traffic Signal Timing Control with State Inputs,” IFAC-PapersOnLine, vol. 54, no. 2, pp. 265-270, 2021. https://doi.org/10.1016/j.ifacol.2021.06.032 .
[7] Y. Wu, “Enhancing Urban Traffic Flow Through Fuzzy Logic-Based Signal Light Control Optimization,” International Journal of e-Collaboration, vol. 20, no. 1, pp. 1-13, Nov. 2024. https://doi.org/10.4018/IJeC.358746 .
[8] V. Varnava, and O. Silagin, “Application of Fuzzy Logic to Manage Traffic Flows in Cities,” Вісник Черкаського державного технологічного університету, т. 30, №. 3, с. 61-69, 2025. https://doi.org/10.62660/bcstu/3.2025.61 .
[9] W3C, “OWL 2 Web Ontology Language Document Overview,” W3C Recommendation, Dec. 2012. [Online]. Available: https://www.w3.org/TR/owl2-overview/ .
[10] W3C, “SPARQL 1.1 Query Language,” W3C Recommendation, Mar. 2013. [Online]. Available: https://www.w3.org/TR/sparql11-query/ .
[11] В. Ю. Варнава, О. В. Сілагін, «Онтологічне моделювання бази знань для керування транспортними потоками у містах,» у Матеріали LV Всеукраїнської науково-технічної конференції факультету інтелектуальних інформаційних технологій та автоматизації, секція Комп’ютерних наук, Вінниця, Україна, 2026. [Online]. Available: https://conferences.vntu.edu.ua/index.php/all-fksa/all-fksa-2026/paper/view/28282 .
[12] B. Glimm, I. Horrocks, B. Motik, G. Stoilos, and Z. Wang, “HermiT: An OWL 2 Reasoner,” Journal of Automated Reasoning, vol. 53, no. 3, pp. 245-269, 2014. https://doi.org/10.1007/s10817-014-9305-1 .
[13] E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, and Y. Katz, “Pellet: A practical OWL-DL reasoner,” Web Semantics: Science, Services and Agents on the World Wide Web, vol. 5, no. 2, pp. 51-53, 2007. https://doi.org/10.1016/j.websem.2007.03.004.

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