Sustainable Parking Space Management Using Machine Learning and Swarm Theory—The SPARK System
Source abstract
Abstract
The utilization of contemporary technology enhances the efficiency of parking resource management, contributing to more liveable and sustainable cities. In response to the growing challenges of urbanization, intelligent parking systems have emerged as a crucial solution for optimizing parking management, reducing traffic congestion, and minimizing pollution. The primary aim of this study is to present the concept of the developed web application that supports finding available parking spaces, embodied in the SPARK system (Smart Parking Assistance and Resource Knowledge). The integration of the YOLOv9 (You Only Look Once) segmentation algorithm with Artificial Bee Colony (ABC) optimization, combined with the use of crowdsourced data and deep learning for image analysis, significantly enhances the SPARK system’s operational efficiency. It enables rapid and precise detection of available parking spaces while ensuring robustness and continuous improvement. The accuracy of detecting available parking spaces in the presented system, estimated at 87.33%, is satisfactory compared to similar studies worldwide.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Parking Systems Research · Traffic control and management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Geodesy and Construction, University of Warmia and Mazury in Olsztyn, 10-720 Olsztyn, Poland
Department of Geomatics Engineering, Faculty of Engineering, Niğde Ömer Halisdemir University, Niğde 51240, Türkiye
Institute of Spatial Management and Geography, University of Warmia and Mazury in Olsztyn, 10-720 Olsztyn, Poland
References (87)
Show source-provided references
1. Gary, M.R., and Johnson, D.S. (1979). Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman.
2. The High Cost of Free Parking10.1177/0739456X9701700102 ↗
3. Krishnamurthi, R., Kumar, A., Gopinathan, D., Nayyar, A., and Qureshi, B. (2020). An Overview of IOT Sensor Data Processing, Fusion, and Analysis Techniques. Sensors, 20.10.3390/s20216076 ↗
4. Corrado, A., Barba, S., Carozzo, I., and Nardi, S. (2023). Smart Passenger Center: Real-Time Optimization of Urban Public Transport. The International FLAIRS Conference Proceedings, Florida Artificial Intelligence Research Society.10.32473/flairs.36.133300 ↗
5. Kumar, G., Basri, S., Imam, A.A., Khowaja, S.A., Capretz, L.F., and Balogun, A.O. (2021). Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review. Appl. Sci., 11.10.3390/app11178275 ↗
6. Rodriguez, A., Gutierrez, A., Rivera, L., and Ramirez, L. (2015). RWA: Comparison of Genetic Algorithms and Simulated Annealing in Dynamic Traffic. Advanced Computer and Communication Engineering Technology, Proceedings of the 1st International Conference on Communication and Computer Engineering, Rajshahi, Bangladesh, 26–27 November 2015, Springer.10.1007/978-3-319-07674-4_1 ↗
7. Deep Reinforcement Learning Approach Towards A Smart Parking Architecture10.1007/s10586-022-03599-y ↗
8. Shoeibi, N., and Shoeibi, N. (2020). Future of smart parking: Automated valet parking using deep Q-learning. Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions, Springer.10.1007/978-3-030-23946-6_20 ↗
9. Cicirelli, F., Guerrieri, A., Mastroianni, C., Spezzano, G., and Vinci, A. (2019). Swarm Intelligence and IoT-Based Smart Cities: A Review. The Internet of Things for Smart Urban Ecosystems, Springer International Publishing.10.1007/978-3-319-96550-5 ↗
10. Mikusova, M., Abdunazarov, J., and Zukowska, J. (2019). Modelling of The Movement of Designed Vehicles on Parking Space for Designing Parking. Development of Transport by Telematics, Proceedings of the 19th International Conference on Transport System Telematics, TST 2019, Jaworze, Poland, 27 February–2 March 2019, Springer.10.1007/978-3-030-27547-1_15 ↗
11. Analysis of the current situation and a proposal to solve the parking problem in the center of the city of Banja Luka by introducing new street parking spaces
12. Paris’s parking policy for healthier cities10.1016/S2542-5196(24)00089-5 ↗
13. Miao, H., Guan, H., Han, Y., and Wang, H. (2024). Research of park and ride parking spaces tiered pricing methodology based on subway ride distance. Appl. Sci., 14.10.3390/app14093550 ↗
14. Smart Cities, Smarter Parking: Developing and Implementing Vehicle Parking Management System
15. Research on planning and management of urban parking lot—Taking Hangzhou as an example10.4236/cus.2017.54021 ↗
16. Fateha, M.R., Mukta, M.S.H., Hossain, M.A., Al Islam, M., and Islam, S. (2022, January 17–19). SlotFinder: A Spatio-Temporal Based Car Parking System. Proceedings of the 25th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh.10.1109/ICCIT57492.2022.10055168 ↗
17. Lucchese, C., Callegher, G., Modenese, M., and Dassiè, S. (2022, January 25–29). A Comparison of Spatio-Temporal Prediction Methods: A Parking Availability Case Study. Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, Virtual.10.1145/3477314.3507035 ↗
18. Errousso, H., Filali, Y., Aghbalou, N., Alaoui, E.A.A., and Benhadou, S. (2023, January 4–6). Spatio-Temporal Clustering For Optimal Real-Time Parking Availability Estimation. Proceedings of the International Conference on Smart City Applications, Paris, France.10.1007/978-3-031-53824-7_19 ↗
19. Sairam, B., Agrawal, A., Krishna, G., and Sahu, S.P. (2020, January 11–13). Automated Vehicle Parking Slot Detection System Using Deep Learning. Proceedings of the Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.10.1109/ICCMC48092.2020.ICCMC-000140 ↗
20. Predicting free parking slot availability using hybrid machine learning model
21. An Optimization Model and Genetic-Based Matheuristic for Parking Slot Rent Optimization to Carsharing10.1016/j.retrec.2020.100962 ↗
22. Optimal Placement of Parking of Electric Vehicles in Smart Grids, Considering their Active Capacity10.1016/j.epsr.2023.109238 ↗
23. An Optimization Model for Renting Public Parking Slots to Carsharing Services10.1016/j.trpro.2020.03.064 ↗
24. Designing Intelligent Public Parking Locations for Autonomous Vehicles10.1016/j.eswa.2023.119810 ↗
25. An Improved Vehicle Parking Mechanism to Reduce Parking Space Searching Time Using Firefly Algorithm and Feed Forward Back Propagation Method10.1016/j.procs.2020.03.394 ↗
26. A Beautiful Fleet: Optimal Repositioning in e-Scooter Sharing Systems for Urban Decorum10.1016/j.trpro.2021.01.069 ↗
27. Planning of Parking Enforcement Patrol Considering Drivers’ Parking Payment Behavior10.1016/j.trb.2017.07.001 ↗
28. On-Street Parking Occupancy Inference Based on Payment Transactions10.1109/TITS.2021.3095277 ↗
29. Dynamic Repositioning Problem of Dockless Electric Scooter Sharing Systems10.1080/19427867.2022.2129209 ↗
30. Kittur, A., Chi, E.H., and Suh, B. (2008, January 5–10). Crowdsourcing User Studies with Mechanical Turk. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy.10.1145/1357054.1357127 ↗
31. Ipeirotis, P.G., Provost, F., and Wang, J. (2010, January 25). Quality Management on Amazon Mechanical Turk. Proceedings of the ACM SIGKDD Workshop on Human Computation, Washington, DC, USA.10.1145/1837885.1837906 ↗
32. Enhancing data quality in crowdsourcing systems with trust and reputation management
33. Voigt, P., and Von dem Bussche, A. (2017). The EU General Data Protection Regulation (GDPR), Springer International Publishing AG.10.1007/978-3-319-57959-7 ↗
34. Turkish Grand National Assembly—TBMM (2024, October 19). Law on the Protection of Personal Data, Available online: https://www.mevzuat.gov.tr/mevzuat?MevzuatNo=6698&MevzuatTur=1&MevzuatTertip=5.
35. Anonymity and pseudonymity in cyberspace: A survey on identity management techniques and protocols
36. Addressing cloud computing security issues10.1016/j.future.2010.12.006 ↗
37. Crowdsourcing—Harnessing the masses to advance health and medicine, a systematic review10.1007/s11606-013-2536-8 ↗
38. Quality assurance in crowdsourcing using optimization and machine learning: A survey
39. Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon10.1016/j.cose.2015.07.002 ↗
40. Singh, T., Khan, S.S., and Chadokar, S. (2018, January 28–29). A Review on Automatic Parking Space Occupancy Detection. Proceedings of the International Conference on Advanced Computation and Telecommunication (ICACAT), Bhopal, India.10.1109/ICACAT.2018.8933644 ↗
41. Real-time smart parking systems integration in distributed its for smart cities
42. Chen, M., and Chang, T. (2011, January 6–8). A Parking Guidance and Information System Based on Wireless Sensor Network. Proceedings of the IEEE International Conference on Information and Automation, Shenzhen, China.
43. Wireless magnetic sensor node for vehicle detection with optical wake-up10.1109/JSEN.2010.2103937 ↗
44. A parking occupancy detection algorithm based on AMR sensor10.1109/JSEN.2014.2362122 ↗
45. Automatic vision-based parking slot detection and occupancy classification10.1016/j.eswa.2023.120147 ↗
46. Li, W., Cao, L., Yan, L., Li, C., Feng, X., and Zhao, P. (2020). Vacant parking slot detection in the around view image based on deep learning. Sensors, 20.10.3390/s20072138 ↗
47. Zinelli, A., Musto, L., and Pizzati, F. (2019, January 9–12). A Deep-Learning Approach for Parking Slot Detection on Surround-View Images. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Paris, France.10.1109/IVS.2019.8813777 ↗
48. Deep Learning-Based Parking Occupancy Detection Framework Using ResNet and VGG-1610.1007/s11042-023-15654-w ↗
49. Semantic Segmentation-Based Parking Space Detection with Standalone Around View Monitoring System10.1007/s00138-018-0986-z ↗
50. Evidence Filter of Semantic Segmented Image from Around View Monitor in Automated Parking System10.1109/ACCESS.2019.2927736 ↗
51. Anggawijaya, Y.M., Weng, T.-H., and Herawati, R. (2019, January 29–30). Energy Aware Parking Lot Availability Detection Using Yolo on Tx2. Proceedings of the 3rd International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia.10.1109/ICICoS48119.2019.8982448 ↗
52. Ammar, A., Koubaa, A., Ahmed, M., Saad, A., and Benjdira, B. (2021). Vehicle detection from aerial images using deep learning: A comparative study. Electronics, 10.10.3390/electronics10070820 ↗
53. Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., and Ouni, K. (2019, January 5–7). Car Detection Using Unmanned Aerial Vehicles: Comparison Between Faster R-CNN and YOLOv3. Proceedings of the 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), Muscat, Oman.10.1109/UVS.2019.8658300 ↗
54. Comparison of faster R-CNN and YOLOv5 for overlapping objects recognition10.21123/bsj.2022.7243 ↗
55. Comparison of YOLOV5, YOLOV6, YOLOV7 and YOLOV8 for intelligent video surveillance
56. Optimized real-time parking management framework using deep learning10.1016/j.eswa.2023.119686 ↗
57. Panthakkan, A., Valappil, N., Al-Mansoori, S., and Al-Ahmad, H. (2022, January 5–7). AI Based Automatic Vehicle Detection From Unmanned Aerial Vehicles (Uav) Using YOLOv5 Model. Proceedings of the IEEE 5th International Conference on Image Processing Applications and Systems (IPAS), Genova, Italy.10.1109/IPAS55744.2022.10053056 ↗
58. A method for detecting street parking using dashboard camera videos
59. Automated identification of vehicles in very high-resolution UAV orthomosaics using YOLOv7 deep learning model10.55730/1300-0632.4060 ↗
60. A smart parking system: An IoT based computer vision approach for free parking spot detection using faster R-CNN with YOLOv3 method10.1007/s11277-022-09705-y ↗
61. Zhao, N., Wang, K., Yang, J., Luan, F., Yuan, L., and Zhang, H. (2024). CMCA-YOLO: A study on a real-time object detection model for parking lot surveillance imagery. Electronics, 13.10.3390/electronics13081557 ↗
62. YOLO-CIR: The network based on YOLO and ConvNeXt for infrared object detection10.1016/j.infrared.2023.104703 ↗
63. Tschentscher, M., and Neuhausen, M. (2012, January 26–28). Video-Based Parking Space Detection. Proceedings of the Forum Bauinformatik, Bochum, Germany.
64. UAV Measurements and AI-Driven Algorithms Fusion for Real Estate Good Governance Principles Support
65. Wong, G.S., Goh, K.O.M., Tee, C., and Md. Sabri, A.Q. (2023). Review of vision-based deep learning parking slot detection on surround view images. Sensors, 23.10.3390/s23156869 ↗
66. Automatic parking based on a bird’s eye view vision system10.1155/2014/847406 ↗
67. Deployment optimization in wireless sensor networks using advanced artificial bee colony algorithm10.1007/s12083-024-01771-2 ↗
68. An ensemble optimization strategy for dynamic parking-space allocation10.1109/MITS.2022.3163506 ↗
69. Duan, F., Eslami, M., Khajehzadeh, M., Alkhayer, A.G., and Palani, S. (2024). An improved meta-heuristic method for optimal optimization of electric parking lots in distribution network. Sci. Rep., 14.10.1038/s41598-024-71408-0 ↗
70. A novel chaotic particle swarm optimization algorithm for parking space guidance10.1155/2016/5126808 ↗
71. Automated basement car park design: Impact of design factors on static capacity10.1061/JAEIED.AEENG-1609 ↗
72. Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm10.1002/nem.2228 ↗
73. Ji, Y., Lu, X., Jiang, H., Zhu, X., and Wang, J. (2022). Layout optimization for shared parking spaces considering shared parking walking time and parking fee. Sustainability, 14.10.3390/su14095635 ↗
74. Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization: Technical Report-Tr06, Computer Engineering Department, Engineering Faculty, Erciyes University.
75. Karaboga, D. (2018). Yapay Zekâ Optimizasyon Algoritmaları, Nobel Akademi Yayınları. [6th ed.].
76. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm10.1007/s10898-007-9149-x ↗
77. Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., and Montes, D. (2024, May 29). YOLOv5 SOTA Realtime Instance Segmentation. Available online: https://zenodo.org/records/7347926.
78. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8–14). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_49 ↗
79. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.322 ↗
80. Real-time car parking detection with deep learning in different lighting scenarios
81. Ghilani, C.D. (2010). Adjustment Computations: Spatial Data Analysis, John Wiley & Sons.
82. Ermak, D. (2024, May 29). Orion_cars Dataset. Available online: https://universe.roboflow.com/denis-ermak/orion_cars.
83. Dong, L., and Wang, D. (2022). The Universality in Urban Commuting Across and within Cities. arXiv.
84. Gao, Q.-L., Li, Q.-Q., Zhuang, Y., Yue, Y., Liu, Z.-Z., Li, S.-Q., and Sui, D. (2019). Urban Commuting Dynamics in Response to Public Transit Upgrades: A Big Data Approach. PLoS ONE, 14.10.1371/journal.pone.0223650 ↗
85. A New “Smart Parking” System Infrastructure and Implementation10.1016/j.sbspro.2012.09.842 ↗
86. Car Park System: A Review of Smart Parking System and Its Technology10.3923/itj.2009.101.113 ↗
87. Continuous and Discrete Zhang Dynamics for Real-Time Varying Nonlinear Optimization10.1007/s11075-015-0088-1 ↗