MULTI-AGENT INTELLIGENT SYSTEM FOR CONTROL OF PARKING SPACES IN CITY INFRASTRUCTURE
Abstract
With the growing number of cars and limited space, many cities are realizing the importance of implementing intelligent parking systems to improve urban mobility and convenience for drivers. The level of implementation of intelligent parking based on various technological solutions is growing, but to achieve maximum efficiency, it is necessary to continue to develop technologies, integrate them with other systems and take into account the needs of users. The purpose of the study is to develop a multi-agent intelligent system for monitoring and managing parking space reservations in the city parking network. The architecture of a multi-agent intelligent parking management system has been developed, which provides automatic access control to parking spaces taking into account the wishes of parking lot owners, driver orders, the traffic situation in the city and safety requirements. The main element of the developed system is parking, which is represented by a set of parking spaces equipped with automated parking space management systems (parking attendants), a communication system and data collection tools (surveillance camera and weather stations). Parking spaces and parking attendants are managed by an intelligent control system based on multiagent neurocognitive architectures. A prototype of a hardware and software complex of a multi-agent intelligent parking space management system has been developed in the form of a client-server architecture. The server is responsible for collecting, processing, storing data and managing automated parking attendants. Two types of clients are connected to the server - a mobile application of the administrator and the driver. The administrator has the ability to manage parking (set fixed prices or use server recommendations, book parking spaces for employees) and view statistics (current load, parking statistics, data on accepted payments, parking work forecast, recommendations). The driver has the ability to view the status of parking in the area of interest (number of free spaces, waiting time for a free space, cost, recommendations for the most convenient parking) and book a parking space with the ability to pay online
References
1. Vera-Gómez J.A. et al. An intelligent parking management system for urban areas // Sensors. – 2016.
– Vol. 16, No. 6. – P. 931. – https://doi.org/10.3390/s16060931.
2. Ahad A., Khan Z.R., Ahmad S.A. Intelligent parking system // World Journal of Engineering and Technology.
– 2016. – Vol. 4, No. 2. – P. 160-167.
3. Elfaki A.O. et al. A smart real-time parking control and monitoring system // Sensors. – 2023. – Vol. 23,
No. 24. – P. 9741. – https://doi.org/10.3390/s23249741.
4. Shin J.H., Jun H.B., Kim J.G. Dynamic control of intelligent parking guidance using neural network
predictive control // Computers & Industrial Engineering. – 2018. – Vol. 120. – P. 15-30.
5. Liu J., Wu J., Sun L. Control method of urban intelligent parking guidance system based on Internet of
Things // Computer Communications. – 2020. – Vol. 153. – P. 279-285.
6. Canli H., Toklu S. Deep learning-based mobile application design for smart parking // IEEE Access.
– 2021. – Vol. 9. – P. 61171-61183.
7. Aditya A. et al. An IoT assisted intelligent parking system (IPS) for smart cities // Procedia Computer
Science. – 2023. – Vol. 218. – P. 1045-1054.
8. Ma Z. et al. Intelligent Parking Control Method Based on Multi-Source Sensory Information Fusion
and End-to-End Deep Learning // Applied Sciences. – 2023. – Vol. 13, No. 8. – P. 5003.
9. Dev N.M. et al. Parking Space Detector // Grenze International Journal of Engineering & Technology
(GIJET). – 2023. – Vol. 9, No. 1.
10. Карпов А.C. Системы интеллектуального управления парковочными пространствами // Вестник
науки. – 2024. – Т. 4, № 6 (75). – С. 1412-1419.
11. Панина В.С., Амеличев Г.Э., Белов Ю.С. Интеллектуальная парковочная система как часть ин-
теллектуальной транспортной системы // E-Scio. – 2022. – № 1 (64). – С. 445-452.
12. Singh T. et al. Artificial intelligence-enabled smart parking system // International Conference on
Electrical and Electronics Engineering. – Singapore: Springer Nature Singapore, 2023. – P. 419-436.
13. Alymani M. et al. Enabling smart parking for smart cities using Internet of Things (IoT) and machine
learning // PeerJ Computer Science. – 2025. – Vol. 11. – P. e2544.
14. Alsheikhy A.A. et al. An intelligent smart parking system using convolutional neural network //Journal
of Sensors. – 2022. – Vol. 2022, No. 1. – P. 7571716.
15. Панина В.С., Амеличев Г.А., Белов Ю.С. Интеллектуальная парковочная система на основе свер-
точных нейронных сетей // Научное обозрение. Технические науки. – 2022. – № 1. – С. 29-33.
– URL: https://science-engineering.ru/ru/article/view?id=1382 (дата обращения: 26.02.2025).
16. Rashidi M.R. et al. IoT-Enabled Smart Parking System Using Artificial Intelligence and Optical Character
Recognition // 2024 6th International Conference on Communications, Signal Processing, and
their Applications (ICCSPA). – IEEE, 2024. – P. 1-6.
17. Пшенокова И.А., Бжихатлов К.Ч., Унагасов А.А., Абазоков М.А. Мультиагентный алгоритм
сбора данных с метеостанции для прогнозирования урожайности и состояния посевов // Извес-
тия ЮФУ. Технические науки. – 2022. – № 1 (225). – С. 91-101.
18. Nagoev, Z., Bzhikhatlov, K., Pshenokova, I., Unagasov, A. Algorithms and Software for Simulation of
Intelligent Systems of Autonomous Robots Based on Multi-agent Neurocognitive Architectures / In:
Ronzhin A., Savage J., Meshcheryakov R. (eds) // Interactive Collaborative Robotics. ICR 2024. Lecture
Notes in Computer Science. – 2024. – Vol. 14898. – Springer, Cham. – DOI:
https://doi.org/10.1007/978-3-031-71360-6_29.
19. Нагоев З.В. Интеллектика, или мышление в живых и искусственных системах. – Нальчик: Изд-
во КБНЦ РАН, 2013. – 211 с.
20. Пшенокова И.А., Нагоева О.В., Апшев А.З., Энес А.З. Формирование динамических причинно-
следственных зависимостей при управлении поведением интеллектуального агента на основе
формализма мультиагентных нейрокогнитивных архитектур // Известия Кабардино-
Балкарского научного центра РАН. – 2022. – № 5 (109). – С. 73-80. – DOI: 10.35330/1991-6639-
2022-5-109-73-80.
21. Bzhikhatlov K., Nagoeva O., Anchokov M., Makoeva D. Methods and Algorithms (Modeling of Reasoning)
to Synthesize Intellectual Behavior of Autonomous Mobile Robots and Program Complexes
Based on Received Reasoning Models / In: Samsonovich, A.V., Liu, T. (eds). Biologically Inspired
Cognitive Architectures 2024. BICA 2024. Studies in Computational Intelligence. – 2024. – Vol. 477.
– Springer, Cham. – DOI: https://doi.org/10.1007/978-3-031-76516-2_7.
