Machine Learning-Based Parking Occupancy Prediction Using OpenStreetMap Data

Accurate parking occupancy prediction is essential for reducing traffic congestion and optimizing urban mobility. Traditional monitoring methods are costly and difficult to scale, making machine learning a viable alternative. This study employs XGBoost (eXtreme Gradient Boosting) to predict parking occupancy using OpenStreetMap (OSM) data, with simulated occupancy rates based on proximity to the central business district (CBD). The model achieves a Mean Squared Error (MSE) of 0.0022 and an R²value of 0.8922, demonstrating strong predictive accuracy. Results confirm the significance of spatial factors in parking demand. Future work will integrate real-time data and explore deep learning models to further enhance prediction accuracy.