Ontological model for managing waiting time for traffic light signals by road users in a pedestrian crossing zone
https://doi.org/10.21869/2223-1560-2024-28-4-124-137
Abstract
Purpose of research. Development of an ontological model for managing the waiting time for traffic light signals by road users in a pedestrian crossing zone, with the capability to count the number of pedestrians and vehicles at the intersection and regulate the timing intervals of traffic light signals based on their quantity.
Methods. The database for the ontological model is collected using a computer vision system. A cognitive decision-making model is used to determine object boundaries. Object classification is performed using the YOLO algorithm. The counting of pedestrians and vehicles is carried out within a mathematical model for counting detectable objects in an image. The calculation of time for regulating the duration of traffic light signals is achieved through a mathematical model of intelligent traffic light control. The proposed ontological model includes several stages: data collection, image preprocessing, object boundary detection, classification of road users into classes and subclasses, counting the number of pedestrians and vehicles, and calculating the time required to adjust the duration of intelligent traffic light signals.
Results. A specialized software model has been developed, which enables the detection of object classes and the calculation of delay times for traffic light signals to regulate an intelligent traffic light. The state registration certificate for the computer program "Program for Detecting Objects at a Pedestrian Crossing and Determining Traffic Light Signal Delay Times" is numbered 2024662790. Additionally, a patent for the invention "Traffic Light Control Device Based on Fuzzy Logic" (No. 2827781) has been obtained, allowing for the generation of control signals for an intelligent traffic light.
Conclusion. The results of experimental studies have demonstrated the high efficiency of the developed ontological model for managing the waiting time for traffic light signals by road users in a pedestrian crossing zone.
Keywords
ontological model,
intelligent traffic light,
pedestrian crossing control,
contour detection,
object classification,
Canny,
YOLO
Supporting agencies: The work was supported by the State Assignment of the Ministry of Education and Science of the Russian Federation on the topic "Research of algorithms, models and methods for improving the efficiency of complex technical systems" (Agreement No. 0851-2020-0032)
About the Authors
M. V. Bobyr
Southwest State University
Russian Federation
Competing Interests:
Russian Federation
Maksim V. Bobyr, Dr. of Sci. (Engineering), Professor
Computer Engineering Department
305040; 50 Let Oktyabrya str. 94; Kursk
e-mail: fregat_mn@rambler.ru
Researcher ID: G-2604-2013
Competing Interests:
The authors declare the absence of obvious and potential conflicts of interest related to the publication of this article
S. G. Emelyanov
Southwest State University
Russian Federation
Competing Interests:
Russian Federation
Sergey G. Emelyanov, Dr. of Sci. (Engineering), Professor, Rector
305040; 50 Let Oktyabrya str. 94; Kursk
Researcher ID: E-3511-2013
Competing Interests:
The authors declare the absence of obvious and potential conflicts of interest related to the publication of this article
N. I. Khrapova
Southwest State University
Russian Federation
Competing Interests:
Russian Federation
Natalia I. Khrapova, Post-Graduate Student
Software Engineering Department
305040; 50 Let Oktyabrya str. 94; Kursk
Competing Interests:
The authors declare the absence of obvious and potential conflicts of interest related to the publication of this article
References
1. Slobodchikova N. A., Pluta K. V. Pedestrian crossing safety. Vestnik nauki i obrazovaniya Severo-Zapada Rossii = Bulletin of Science and Education of the North-West of Russia. 2019; 5(3): 91-99 (In Russ.).
2. Volkov V. S., Nabatnikova E. A., Lebedev E. G. Some issues of traffic safety at un-regulated pedestrian crossings in the city. Evraziiskii soyuz uchenykh = Eurasian Union of Scientists. 2020; (12-5): 26-32 (In Russ.).
3. Pavlenko P. F. Automatic early warning system for drivers about the presence of pedestrians at a pedestrian crossing (Safe pedestrian crossing). Problemy avtomatiki i upravleniya = Automation and control problems. 2013; (2): 48-53 (In Russ.).
4. Bobyr M.V., Khrapova N. I., Lamonov M. A. Smart Traffic Light Control System Based on Fuzzy Logic. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of the Southwest State University. 2021; 25(4): 162-176 (In Russ.). doi: 10.21869/2223-1560-2021-25-4-162-176.
5. Kuznetsov T. A. Optimization of traffic flow at a controlled intersection using simulation modeling. Politekhnicheskii molodezhnyi zhurnal = Polytechnic Youth Journal. 2022; (7). (In Russ.).
6. Amirov M. S., Amirov S. M. Unified transport system. Moscow: KnoRus; 2023. 178 p. (In Russ.).
7. Strizhko M. A., Chervinsky V. V. Intelligent traffic flow management system at intersections with traffic light regulation. Vestnik Voronezhskogo gosudarstvennogo tekhnicheskogo universiteta = Bulletin of the Voronezh State Technical University. 2024; 20(2): 48-55 (In Russ.).
8. Pegat A. Fuzzy modeling and control. Moscow: Laboratoriya znanii; 2020. 801 p. (In Russ.).
9. Bobyr M. V., Khrapova N. I. Two-level information and analytical control system for intelligent traffic lights. Elektronnye biblioteki = Electronic libraries. 2024; 27(5): 696-717. (In Russ.).
10. Bobyr M. V., Milostnaya N. A., Khrapova N. I. On the approach to detecting pedestrian movement by the method of histograms of directional gradients. Elektronnye biblioteki = Electronic libraries. 2024; 27(4): 429-447. (In Russ.).
11. Bobyr M. V., Khrapova N. I. Information and analytical system for detecting the movement of objects at a pedestrian crossing. Ontologiya proektirovaniya = The ontology of design. 2024; 14(4): 531-541. (In Russ.).
12. Kochegurov A. I., Dubinin D. V., Geringer V. I. Modified Pratt-Yasorsky estimation in a generalized indicator of the quality of contour detection algorithms. Izvestiya Tomskogo politekhnicheskogo universiteta. Inzhiniring georesursov = Proceedings of Tomsk Polytechnic University. Georesource engineering. 2021; 332(9): 168-177. (In Russ.).
13. Geringer V., Dubinin D., Kochegurov A. The results of a complex analysis of the modified Pratt-Yaskorskiy performance metrics based on the two-dimensional markov-renewal-process. Lecture Notes in Computer Science. 2016; 9875: 187-196.
14. Bobyr M. V., Arkhipov A. E., Gorbachev S. V., et al. Fuzzy logic methods in the problem of detecting object boundaries. Informatika i avtomatizatsiya = Informatics and Automation. 2022; 21(2): 376-404. (In Russ.).
Review
For citations:
Bobyr M.V., Emelyanov S.G., Khrapova N.I. Ontological model for managing waiting time for traffic light signals by road users in a pedestrian crossing zone. Proceedings of the Southwest State University. 2024;28(4):124-137. (In Russ.) https://doi.org/10.21869/2223-1560-2024-28-4-124-137
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