NAVIGATING THE FUTURE: UNDERSTANDING THE ESSENTIAL COMPONENTS FOR SUCCESSFUL SMART PARKING SOLUTIONS
Keywords:
Intelligent parking management, Intelligent Traffic Management in Smart Cities, Parking datasets, Parking optimization, Smart parking systems, Vehicle detection techniques.
Abstract
In an era when there is increasing talk about the rapid growth in technology, it has become necessary to advance the service requirements of our daily lives, and the first thing we face is traffic jams and time spent searching for safe parking spaces. The smart parking system is a promising solution to face many of the problems that accompany large cities, where lack of work is considered one of the factors that cause pollution and the difficulty of movement due to congestion, which has become one of the main obstacles facing the community. The common way to find a parking space is the traditional way as the driver usually finds a place in the city through his periodic experience, searching, or luck. This process requires time and effort and may result in the worst-case scenario if the driver is driving in a city with high traffic especially at peak hours. Smart parking solutions are characterized by a specific architecture (such as cameras, sensors, communication protocols, and software solutions). But despite the limitations of these components, they constitute a smart parking solution. This paper discusses the most used types of components which guides the commuters to decide on the selection of component types to implement a smart parking solution.
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References
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Abu-Alsaad & H. A. (2023). CNN-Based Smart Parking System. International Journal of Interactive Mobile Technologies, 17(11), 155-170. https://doi.org/10.3991/ijim.v17i11.37033
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Ben-Shoushan, R., & Brook, A. (2023). Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN. Remote Sensing, 15(3). https://doi.org/10.3390/rs15030723
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Gruzd, A., & Hernández-García, Á. (2018). Privacy Concerns and Self-Disclosure in Private and Public Uses of Social Media. Cyberpsychology, Behavior, and Social Networking, 21(7), 418–428. https://doi.org/10.1089/cyber.2017.0709
Zhang, Z., Zhang, C., & Hsu, C. H. (2019). A Survey on Latest Smart Parking Technologies. 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE).
Suresh, M., Kavitha, G., Geetha, M., & Kannan, A. (2019). Smart parking systems and sensors: A survey. Cluster Computing, 22, 15877–15899.
Padmakumara, W., Premaratne, K., & Murthi, M. (2020). Vehicle Counting in High Density Traffic Scenes Incorporating the Vehicle Size Information and Multilevel Classification Scheme. IEEE Transactions on Intelligent Transportation Systems
De Almeida, P., Oliveira, L. S., Silva Jr, E. A., Britto Jr, A. S., & Koerich, A. L. (2015). PKLot's - A robust dataset for parking lot classification. Expert Systems with Applications, 42(11), 4937-4949.
Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., & Vairo, C. (2017). Deep learning for decentralized parking lot occupancy detection. Expert Systems with Applications, 72, 327-334.
König, M., Weissenberger, M., Kleyko, D., & Kleyko, A. (2021). Parking Lot Dataset for Semantic Segmentation and Deep Learning. In 2021 IEEE International Conference on Image Processing (ICIP) (pp. 1239-1243). IEEE.
Shirvani Moghaddam, S., & Shirvani Moghaddam, K. (2023). A threshold-based sorting algorithm for dense wireless sensor systems and communication networks. IET Wireless Sensor Systems, 13(2), 37-47. https://doi.org/10.1049/wss2.12048
Rouf, A., Iwahori, Y., Wu, Q., Wu, H., Yu, X., & Wang, A. Real-time Vehicle Detection, Tracking and Counting System Based on YOLOv7 (Embedded Self Organizing Systems, Issue.
Li, W., Cao, L., Yan, L., Li, C., Feng, X., & Zhao, P. (2020). Vacant parking slot detection in the around view image based on deep learning. Sensors (Switzerland), 20(7). https://doi.org/10.3390/s20072138
Shroud, M. A., Eame, M., Elsaghayer, E., Almabrouk, A., & Nassar, Y. F. International Journal of Electrical Engineering and Sustainability (IJEES) Challenges and Opportunities in Smart Parking Sensor Technologies. 1(3), 44-59. https://ijees.org/index.php/ijees/index
Waqas, M., Iftikhar, U., Safwan, M., Ul Abidin, Z., & Saud, A. (2021). Smart Vehicle Parking Management System using Image Processing. IJCSNS International Journal of Computer Science and Network Security, 21(8). https://doi.org/10.22937/IJCSNS.2021.21.8.21
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Rahman, S., Ramli, M., Arnia, F., Muharar, R., Ikhwan, M., & Munzir, S. (2022). Enhancement of convolutional neural network for urban environment parking space classification. Global Journal of Environmental Science and Management, 8(3), 315-326. https://doi.org/10.22034/gjesm.2022.03.02
Biyik, C., Allam, Z., Pieri, G., Moroni, D., O’fraifer, M., O’connell, E., Olariu, S., & Khalid, M. (2021). Smart parking systems: Reviewing the literature, architecture and ways forward. Smart Cities, 4(2), 623-642. https://doi.org/10.3390/smartcities4020032
Saraswat, P. K., William, S., & Reddy, E. (2021, 2021/10//). A Hybrid Approach for Offline A/B Evaluation for Item Ranking Algorithms in Recommendation Systems. ACM International Conference Proceeding Series,
Yadav, P., Hase, P., & Bansal, M. (2021). Low-Cost Algorithmic Recourse for Users with Uncertain Cost Functions. http://arxiv.org/abs/2111.01235
Visconti, P., Giannoccaro, N. I., & de Fazio, R. (2021). Special issue on electronic systems and energy harvesting methods for automation, mechatronics and automotive. In Energies (Vol. 14): MDPI
Wu, Y., & Zhang, D. (2017). A review of intelligent parking lot management. Wireless Communications and Mobile Computing, 2017, 1-13.
Chen, M., & Wang, J. (2018). An Internet of Things (IoT) system for smart parking management based on RFID, WSN, and cloud computing. Sensors, 18(1), 218.
Mehmood, A., & Leng, S. (2019). Machine learning-based parking space prediction in smart parking systems. Sensors, 19(1), 83.
Maghrebi, M., Khadraoui, D., & Khoukhi, L. (2020). Smart parking systems: A comprehensive survey. IEEE Access, 8, 66761-66783.
Habib, K. M., & Deakin, M. (2018). Smart parking as an enabler of urban resilience. Sustainable Cities and Society, 42, 639-647.
Ananth, P. (2021). A Smart Parking System based on IoT Technologies. https://www.researchgate.net/publication/353996540
Liu, B., Lai, H., Kan, S., & Chan, C. (2023). Camera-Based Smart Parking System Using Perspective Transformation. Smart Cities, 6(2), 1167–1184. https://doi.org/10.3390/smartcities6020056
Sahoo, B. B. P., Shahjad, & Tanwar, P. S. (2021). Real-Time Smart Parking: Challenges and Solution using Machine learning and IoT. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 451-458. https://doi.org/10.32628/cseit217295
Abu-Alsaad & H. A. (2023). CNN-Based Smart Parking System. International Journal of Interactive Mobile Technologies, 17(11), 155-170. https://doi.org/10.3991/ijim.v17i11.37033
Saleem, A. A., Ur, H., Siddiqui, R., Shafique, R., Haider, A., & Javed, M. A. A Review on Smart IOT Based Parking System.
Acharya, D., & Khoshelham, K. Real-time image-based parking occupancy detection using deep learning-A CPU friendly MATLAB tutorial Augmented reality visualisation of subsurface utilities View project Indoor mapping, modeling and localization View project Real-time image-based parking occupancy detection using deep learning-A CPU Friendly MATLAB tutorial. https://github.com/DebadityaRMIT/Parking.
Barriga, J. J., Sulca, J., Luis, J. L., Ulloa, A., Portero, D., Andrade, R., & Guun, S. Y. (2019). Smart parking: A literature review from the technological perspective. In Applied Sciences (Switzerland) (Vol. 9): MDPI AG
Bura, H., Lin, N., Kumar, N., Malekar, S., Nagaraj, S., & Liu, K. (2018, 2018/9//). An edge based smart parking solution using camera networks and deep learning. Proceedings - 2018 IEEE International Conference on Cognitive Computing, ICCC 2018 - Part of the 2018 IEEE World Congress on Services.
de Almeida, P. R. L., Alves, J. H., Parpinelli, R. S., & Barddal, J. P. (2022). A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets. https://doi.org/10.1016/j.eswa.2022.116731
Jakob, M. (2021). ETH Library Parking policies and their impacts on urban networks. https://doi.org/10.3929/ethz-b-000475798
Bassetti, E., Berti, A., Bisante, A., Magnante, A., & Panizzi, E. (2022). Exploiting User Behavior to Predict Parking Availability through Machine Learning. Smart Cities, 5(4), 1243-1266. https://doi.org/10.3390/smartcities5040064
Dincelli, E. Smart Parking Systems and their Impact on the Efficiency of Parking Officers. https://aisel.aisnet.org/amcis2023
Nocilla S., (2023). Embracing the Era of Intelligent User Interface Artificial Intelligence in User Interface/User Experience Design Embracing the Era of Intelligent User Interface Artificial Intellig... Continuous assesment View project Implementing Interactive Educational Technology to Improve Language Barrier and Resettlement View project.
Berawi, M. A. (2023). Smart Cities: Accelerating Sustainable Development Agendas. International Journal of Technology, 14(1), 1-4. https://doi.org/10.14716/ijtech.v14i1.6323
Aditya, A., Anwarul, S., Tanwar, R., & Koneru, S. K. V. (2023). An IoT assisted Intelligent Parking System (IPS) for Smart Cities. Procedia Computer Science, 218, 1045-1054. https://doi.org/10.1016/j.procs.2023.01.084
Ali, J., & Khan, M. F. (2023). A Trust-Based Secure Parking Allocation for IoT-Enabled Sustainable Smart Cities. Sustainability (Switzerland), 15(8). https://doi.org/10.3390/su15086916
Sun, F., Zhao, Z., Lan, M., Xu, Y., Huang, M., & Xu, D. (2021). Abnormal dynamic functional network connectivity of the mirror neuron system network and the mentalizing network in patients with adolescent-onset, first-episode, drug-naïve schizophrenia. Neuroscience Research, 162, 63-70. https://doi.org/10.1016/j.neures.2020.01.003
Mangiaracina, R.; Tumino, A.; Miragliotta, G.; Salvadori, G.; Perego, A. Smart parking management in a smart city: Costs and benefits. In Proceedings of the 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI); Institute of Electrical and Electronics Engineers (IEEE), Bari, Italy, 18–20 September 2017; pp. 27–32.
Ben-Shoushan, R., & Brook, A. (2023). Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN. Remote Sensing, 15(3). https://doi.org/10.3390/rs15030723
de Almeida, P. R. L., Alves, J. H., Oliveira, L. S., Hochuli, A. G., Fröhlich, J. v., & Krauel, R. A. (2023). Vehicle Occurrence-based Parking Space Detection. http://arxiv.org/abs/2306.09940
Gruzd, A., & Hernández-García, Á. (2018). Privacy Concerns and Self-Disclosure in Private and Public Uses of Social Media. Cyberpsychology, Behavior, and Social Networking, 21(7), 418–428. https://doi.org/10.1089/cyber.2017.0709
Zhang, Z., Zhang, C., & Hsu, C. H. (2019). A Survey on Latest Smart Parking Technologies. 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE).
Suresh, M., Kavitha, G., Geetha, M., & Kannan, A. (2019). Smart parking systems and sensors: A survey. Cluster Computing, 22, 15877–15899.
Padmakumara, W., Premaratne, K., & Murthi, M. (2020). Vehicle Counting in High Density Traffic Scenes Incorporating the Vehicle Size Information and Multilevel Classification Scheme. IEEE Transactions on Intelligent Transportation Systems
De Almeida, P., Oliveira, L. S., Silva Jr, E. A., Britto Jr, A. S., & Koerich, A. L. (2015). PKLot's - A robust dataset for parking lot classification. Expert Systems with Applications, 42(11), 4937-4949.
Amato, G., Carrara, F., Falchi, F., Gennaro, C., Meghini, C., & Vairo, C. (2017). Deep learning for decentralized parking lot occupancy detection. Expert Systems with Applications, 72, 327-334.
König, M., Weissenberger, M., Kleyko, D., & Kleyko, A. (2021). Parking Lot Dataset for Semantic Segmentation and Deep Learning. In 2021 IEEE International Conference on Image Processing (ICIP) (pp. 1239-1243). IEEE.
Shirvani Moghaddam, S., & Shirvani Moghaddam, K. (2023). A threshold-based sorting algorithm for dense wireless sensor systems and communication networks. IET Wireless Sensor Systems, 13(2), 37-47. https://doi.org/10.1049/wss2.12048
Rouf, A., Iwahori, Y., Wu, Q., Wu, H., Yu, X., & Wang, A. Real-time Vehicle Detection, Tracking and Counting System Based on YOLOv7 (Embedded Self Organizing Systems, Issue.
Li, W., Cao, L., Yan, L., Li, C., Feng, X., & Zhao, P. (2020). Vacant parking slot detection in the around view image based on deep learning. Sensors (Switzerland), 20(7). https://doi.org/10.3390/s20072138
Shroud, M. A., Eame, M., Elsaghayer, E., Almabrouk, A., & Nassar, Y. F. International Journal of Electrical Engineering and Sustainability (IJEES) Challenges and Opportunities in Smart Parking Sensor Technologies. 1(3), 44-59. https://ijees.org/index.php/ijees/index
Waqas, M., Iftikhar, U., Safwan, M., Ul Abidin, Z., & Saud, A. (2021). Smart Vehicle Parking Management System using Image Processing. IJCSNS International Journal of Computer Science and Network Security, 21(8). https://doi.org/10.22937/IJCSNS.2021.21.8.21
Fahim, A., Hasan, M., & Chowdhury, M. A. (2021). Smart parking systems: comprehensive review based on various aspects. In Heliyon (Vol. 7): Elsevier Ltd.
Rahman, S., Ramli, M., Arnia, F., Muharar, R., Ikhwan, M., & Munzir, S. (2022). Enhancement of convolutional neural network for urban environment parking space classification. Global Journal of Environmental Science and Management, 8(3), 315-326. https://doi.org/10.22034/gjesm.2022.03.02
Biyik, C., Allam, Z., Pieri, G., Moroni, D., O’fraifer, M., O’connell, E., Olariu, S., & Khalid, M. (2021). Smart parking systems: Reviewing the literature, architecture and ways forward. Smart Cities, 4(2), 623-642. https://doi.org/10.3390/smartcities4020032
Saraswat, P. K., William, S., & Reddy, E. (2021, 2021/10//). A Hybrid Approach for Offline A/B Evaluation for Item Ranking Algorithms in Recommendation Systems. ACM International Conference Proceeding Series,
Yadav, P., Hase, P., & Bansal, M. (2021). Low-Cost Algorithmic Recourse for Users with Uncertain Cost Functions. http://arxiv.org/abs/2111.01235
Visconti, P., Giannoccaro, N. I., & de Fazio, R. (2021). Special issue on electronic systems and energy harvesting methods for automation, mechatronics and automotive. In Energies (Vol. 14): MDPI
Published
2023-12-23
How to Cite
AL ABUDY, I. M. A. A., & EL ABBADI , N. K. E. A. (2023). NAVIGATING THE FUTURE: UNDERSTANDING THE ESSENTIAL COMPONENTS FOR SUCCESSFUL SMART PARKING SOLUTIONS . Journal of Duhok University, 26(2), 283-297. https://doi.org/10.26682/csjuod.2023.26.2.27
Section
Pure and Engineering Sciences
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