Authors : Norah Hadi Al Saleem; Remas Hadi Al Sulaie; Dr. Ahmed Mohammed Al Masabi

Volume/Issue : Volume 10 - 2025, Issue 12 - December

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Google Scholar : https://tinyurl.com/3999axwu

Scribd : https://tinyurl.com/3kvs377b

DOI : https://doi.org/10.38124/ijisrt/25dec222

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This paper presents a data-driven smart parking simulation and prediction model using the “smart parking dataset.csv” dataset. The study focuses on four key features: parking duration, available slots, cars in queue, and time of day. An XGBoost clas-sifier was trained to predict whether a driver will wait (is waiting), achieving perfect evaluation metrics. Additionally, a discrete-event simulation using SimPy was imple- mented to model real-time parking flow and compute historical waiting rates. The results demonstrate the effectiveness of machine learning combined with simulation techniques for smart parking management.

References :

1. R. T. S. K. Al-Nasrawi and A. T. Hameed,” IoT-Enabled Smart Parking System using Machine Learning for Real-Time Parking Prediction,” in 2024 International Conference on Advanced Science and Engineering (ICASE), 2024, pp. 1-6.
2. J. Zhang, H. Yu, T. Yu, and C. Yu,” A metadata-based smart parking system using ensemble learning mechanisms for intelligent transportation,” Journal of Transportation Engineering, vol. 1, no. 1, pp. 1-10, 2024.
3. M. K. Jha, P. Schonfeld, and F. McCullough,” A machine learning and simulation-based dynamic parking choice model for airports,” Journal of Air Transport Management, vol. 111, p. 102425, 2023.
4. J. S. P. D. Coelho,” Simulation using SimPy,” Big Data Ethics, 2020.
5. M. H. Al-Dmour, S. A. Al-Rousan, and M. I. Al-Tawayha,” Machine Learning-Based Parking Occupancy Prediction Using OpenStreetMap Data,” Preprints, 2025.