Parking Availability

Predicting parking availability aims to alleviate traffic congestion and improve urban planning by providing real-time information on vacant parking spaces. Current research focuses on developing sophisticated prediction models, often employing deep learning architectures like recurrent neural networks (RNNs) and graph neural networks (GNNs) to leverage both temporal and spatial correlations in parking data from various sources, including sensor networks and shared fleet information. These advancements enable more accurate predictions, supporting the development of smart parking applications and informing efficient urban parking management strategies. The resulting improvements in traffic flow and reduced search times contribute to enhanced urban livability and sustainability.