Parking Availability Prediction

Parking availability prediction aims to forecast the occupancy of parking spaces, leveraging data-driven approaches to alleviate traffic congestion and improve urban planning. Current research heavily utilizes deep learning models, including graph neural networks, transformers, and convolutional neural networks, often incorporating multi-source data fusion (e.g., traffic patterns, parking lot sensor data) to improve prediction accuracy and efficiency. These advancements are significant for both urban management, enabling optimized pricing and resource allocation, and for drivers, providing real-time information to facilitate smoother navigation and parking experiences.