A Novel Approach to Detect Parking Space Occupancy for Efficient Urban Management
Vol. 11 No. 2 (2025): February
Research Articles
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Doi: 10.28991/CEJ-2025-011-02-015
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Pore, A. A., & Nemade, P. D. (2025). A Novel Approach to Detect Parking Space Occupancy for Efficient Urban Management. Civil Engineering Journal, 11(2), 658–669. https://doi.org/10.28991/CEJ-2025-011-02-015
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[1] Karpagam, G.R., Ganapathy, A., Kavin Raj, A.C., Manigandan, S., Neeraj Julian, J.R., & Raaja Vignesh, S. (2021). Leveraging CNN Deep Learning Model for Smart Parking. Deep Learning and Big Data for Intelligent Transportation. Studies in Computational Intelligence. Springer, Cham, Switzerland. doi:10.1007/978-3-030-65661-4_8.
[2] Nithya, R., Priya, V., Sathiya Kumar, C., Dheeba, J., & Chandraprabha, K. (2022). A Smart Parking System: An IoT Based Computer Vision Approach for Free Parking Spot Detection Using Faster R-CNN with YOLOv3 Method. Wireless Personal Communications, 125(4), 3205–3225. doi:10.1007/s11277-022-09705-y.
[3] Awaisi, K. S., Abbas, A., Khattak, H. A., Ahmad, A., Ali, M., & Khalid, A. (2023). Deep reinforcement learning approach towards a smart parking architecture. Cluster Computing, 26(1), 255–266. doi:10.1007/s10586-022-03599-y.
[4] Slavova, S., Sebastian Piest, J. P., & Van Heeswijk, W. (2022). Predicting truck parking occupancy using machine learning. Procedia Computer Science, 201, 40–47. doi:10.1016/j.procs.2022.03.008.
[5] Sutjarittham, T., Gharakheili, H. H., Kanhere, S. S., & Sivaraman, V. (2022). Monetizing Parking IoT Data via Demand Prediction and Optimal Space Sharing. IEEE Internet of Things Journal, 9(8), 5629–5644. doi:10.1109/JIOT.2020.3044900.
[6] Safran, M., Alajmi, A., & Alfarhood, S. (2024). Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems. Journal of Sensors, 2024(1), 4917097. doi:10.1155/2024/4917097.
[7] Kim, S., Tak, Y., Barmpounakis, E., & Geroliminis, N. (2024). Monitoring Outdoor Parking in Urban Areas With Unmanned Aerial Vehicles. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2024.3397588.
[8] Li, J., Qu, H., & You, L. (2023). An Integrated Approach for the Near Real-Time Parking Occupancy Prediction. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3769–3778. doi:10.1109/TITS.2022.3230199.
[9] Errousso, H., Alaoui, E. A. A., Benhadou, S., & Nayyar, A. (2024). Intelligent parking space management: a binary classification approach for detecting vacant spots. Multimedia Tools and Applications, 1–41. doi:10.1007/s11042-024-18777-w.
[10] Dujić Rodić, L., Perković, T., Š kiljo, M., & Š olić, P. (2023). Privacy leakage of LoRaWAN smart parking occupancy sensors. Future Generation Computer Systems, 138, 142–159. doi:10.1016/j.future.2022.08.007.
[11] Sneha Channamallu, S., Kermanshachi, S., Michael Rosenberger, J., & Pamidimukkala, A. (2024). Enhancing Urban Parking Efficiency Through Machine Learning Model Integration. IEEE Access, 12, 81338–81347. doi:10.1109/access.2024.3411391.
[12] Rafique, S., Gul, S., Jan, K., & Khan, G. M. (2023). Optimized real-time parking management framework using deep learning. Expert Systems with Applications, 220, 119686. doi:10.1016/j.eswa.2023.119686.
[13] Canli, H., & Toklu, S. (2021). Deep Learning-Based Mobile Application Design for Smart Parking. IEEE Access, 9, 61171–61183. doi:10.1109/access.2021.3074887.
[14] Harish Padmanaban, P. C., & Sharma, Y. K. (2024). Optimizing the Identification and Utilization of Open Parking Spaces Through Advanced Machine Learning. Advances in Aerial Sensing and Imaging, 267–294. doi:10.1002/9781394175512.ch12.
[15] Elomiya, A., KŠ™upka, J., JovÄić, S., & Simic, V. (2024). Enhanced prediction of parking occupancy through fusion of adaptive neuro-fuzzy inference system and deep learning models. Engineering Applications of Artificial Intelligence, 129, 107670. doi:10.1016/j.engappai.2023.107670.
[16] Neupane, D., Bhattarai, A., Aryal, S., Bouadjenek, M. R., Seok, U., & Seok, J. (2024). Shine: A deep learning-based accessible parking management system. Expert Systems with Applications, 238, 122205. doi:10.1016/j.eswa.2023.122205.
[17] Balamutas, J., Navikas, D., Markevicius, V., Cepenas, M., Valinevicius, A., Zilys, M., Frivaldsky, M., Li, Z., & Andriukaitis, D. (2023). Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array. IEEE Access, 11, 50984–50993. doi:10.1109/ACCESS.2023.3278986.
[18] Alhaj Ali, A. (2024). PKLot Datase. Kaggle: Your Machine Learning and Data Science Community. San Francisco, United States. Available online: https://www.kaggle.com/datasets/ammarnassanalhajali/pklot-dataset (accessed on January 2025).
[19] Lin, D., Zhang, Q., Chen, X., & Wang, S. (2023). Maximum Correntropy Quaternion Kalman Filter. IEEE Transactions on Signal Processing, 71, 2792–2803. doi:10.1109/TSP.2023.3300631.
[20] Sun, G., Gao, Y., Xu, Y., & Feng, W. (2020). Data-Driven Fault Diagnosis Method Based on Second-Order Time-Reassigned Multisynchrosqueezing Transform and Evenly Mini-Batch Training. IEEE Access, 8, 120859–120869. doi:10.1109/ACCESS.2020.3006152.
[21] Zhao, Z., Chao, L., Zhang, X., Xie, N., & Zeng, Q. (2024). MCAGCN: Multi-component attention graph convolutional neural network for road travel time prediction. IET Intelligent Transport Systems, 18(1), 139–153. doi:10.1049/itr2.12440.
[22] Fang, N., & Cao, Q. (2024). Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems. IEEE Access, 12, 56291–56308. doi:10.1109/ACCESS.2024.3390670.
[2] Nithya, R., Priya, V., Sathiya Kumar, C., Dheeba, J., & Chandraprabha, K. (2022). A Smart Parking System: An IoT Based Computer Vision Approach for Free Parking Spot Detection Using Faster R-CNN with YOLOv3 Method. Wireless Personal Communications, 125(4), 3205–3225. doi:10.1007/s11277-022-09705-y.
[3] Awaisi, K. S., Abbas, A., Khattak, H. A., Ahmad, A., Ali, M., & Khalid, A. (2023). Deep reinforcement learning approach towards a smart parking architecture. Cluster Computing, 26(1), 255–266. doi:10.1007/s10586-022-03599-y.
[4] Slavova, S., Sebastian Piest, J. P., & Van Heeswijk, W. (2022). Predicting truck parking occupancy using machine learning. Procedia Computer Science, 201, 40–47. doi:10.1016/j.procs.2022.03.008.
[5] Sutjarittham, T., Gharakheili, H. H., Kanhere, S. S., & Sivaraman, V. (2022). Monetizing Parking IoT Data via Demand Prediction and Optimal Space Sharing. IEEE Internet of Things Journal, 9(8), 5629–5644. doi:10.1109/JIOT.2020.3044900.
[6] Safran, M., Alajmi, A., & Alfarhood, S. (2024). Efficient Multistage License Plate Detection and Recognition Using YOLOv8 and CNN for Smart Parking Systems. Journal of Sensors, 2024(1), 4917097. doi:10.1155/2024/4917097.
[7] Kim, S., Tak, Y., Barmpounakis, E., & Geroliminis, N. (2024). Monitoring Outdoor Parking in Urban Areas With Unmanned Aerial Vehicles. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2024.3397588.
[8] Li, J., Qu, H., & You, L. (2023). An Integrated Approach for the Near Real-Time Parking Occupancy Prediction. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3769–3778. doi:10.1109/TITS.2022.3230199.
[9] Errousso, H., Alaoui, E. A. A., Benhadou, S., & Nayyar, A. (2024). Intelligent parking space management: a binary classification approach for detecting vacant spots. Multimedia Tools and Applications, 1–41. doi:10.1007/s11042-024-18777-w.
[10] Dujić Rodić, L., Perković, T., Š kiljo, M., & Š olić, P. (2023). Privacy leakage of LoRaWAN smart parking occupancy sensors. Future Generation Computer Systems, 138, 142–159. doi:10.1016/j.future.2022.08.007.
[11] Sneha Channamallu, S., Kermanshachi, S., Michael Rosenberger, J., & Pamidimukkala, A. (2024). Enhancing Urban Parking Efficiency Through Machine Learning Model Integration. IEEE Access, 12, 81338–81347. doi:10.1109/access.2024.3411391.
[12] Rafique, S., Gul, S., Jan, K., & Khan, G. M. (2023). Optimized real-time parking management framework using deep learning. Expert Systems with Applications, 220, 119686. doi:10.1016/j.eswa.2023.119686.
[13] Canli, H., & Toklu, S. (2021). Deep Learning-Based Mobile Application Design for Smart Parking. IEEE Access, 9, 61171–61183. doi:10.1109/access.2021.3074887.
[14] Harish Padmanaban, P. C., & Sharma, Y. K. (2024). Optimizing the Identification and Utilization of Open Parking Spaces Through Advanced Machine Learning. Advances in Aerial Sensing and Imaging, 267–294. doi:10.1002/9781394175512.ch12.
[15] Elomiya, A., KŠ™upka, J., JovÄić, S., & Simic, V. (2024). Enhanced prediction of parking occupancy through fusion of adaptive neuro-fuzzy inference system and deep learning models. Engineering Applications of Artificial Intelligence, 129, 107670. doi:10.1016/j.engappai.2023.107670.
[16] Neupane, D., Bhattarai, A., Aryal, S., Bouadjenek, M. R., Seok, U., & Seok, J. (2024). Shine: A deep learning-based accessible parking management system. Expert Systems with Applications, 238, 122205. doi:10.1016/j.eswa.2023.122205.
[17] Balamutas, J., Navikas, D., Markevicius, V., Cepenas, M., Valinevicius, A., Zilys, M., Frivaldsky, M., Li, Z., & Andriukaitis, D. (2023). Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array. IEEE Access, 11, 50984–50993. doi:10.1109/ACCESS.2023.3278986.
[18] Alhaj Ali, A. (2024). PKLot Datase. Kaggle: Your Machine Learning and Data Science Community. San Francisco, United States. Available online: https://www.kaggle.com/datasets/ammarnassanalhajali/pklot-dataset (accessed on January 2025).
[19] Lin, D., Zhang, Q., Chen, X., & Wang, S. (2023). Maximum Correntropy Quaternion Kalman Filter. IEEE Transactions on Signal Processing, 71, 2792–2803. doi:10.1109/TSP.2023.3300631.
[20] Sun, G., Gao, Y., Xu, Y., & Feng, W. (2020). Data-Driven Fault Diagnosis Method Based on Second-Order Time-Reassigned Multisynchrosqueezing Transform and Evenly Mini-Batch Training. IEEE Access, 8, 120859–120869. doi:10.1109/ACCESS.2020.3006152.
[21] Zhao, Z., Chao, L., Zhang, X., Xie, N., & Zeng, Q. (2024). MCAGCN: Multi-component attention graph convolutional neural network for road travel time prediction. IET Intelligent Transport Systems, 18(1), 139–153. doi:10.1049/itr2.12440.
[22] Fang, N., & Cao, Q. (2024). Leaf in Wind Optimization: A New Metaheuristic Algorithm for Solving Optimization Problems. IEEE Access, 12, 56291–56308. doi:10.1109/ACCESS.2024.3390670.
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