Vacant Lot

Research on vacant lots, specifically focusing on urban parking lots and land use, is rapidly advancing, driven by the need for efficient urban planning and resource management. Current studies employ sophisticated machine learning models, including graph convolutional networks and transformers, to predict parking availability, optimize parking recommendations, and even guide vacant lot conversion decisions. These efforts leverage diverse data sources, such as real-time parking sensor data, traffic patterns, and geographical information, to improve prediction accuracy and efficiency. Ultimately, this research aims to enhance urban mobility, optimize resource allocation, and improve the quality of life in cities.