Urban Parking Management through Federated Learning: A Privacy-Preserving Approach to Parking Slot Prediction

In the contemporary urban landscape, the efficient management of parking resources has emerged as a critical concern. The ability to predict parking slot availability has far-reaching implications for urban mobility, traffic congestion mitigation, economic efficiency, and overall quality of life. This research paper presents a comprehensive approach that leverages deep learning and machine learning techniques, including neural network models, to address the multifaceted problem of parking slot prediction. Unique to this research is the integration of federated learning, a distributed approach that preserves data privacy and security while aggregating knowledge from spatial and temporal data sources. The research unfolds in two primary stages: spatial and temporal. In the spatial case, models are trained to understand the intricate relationships between parking slot occupancy and the physical characteristics of the environment. Simultaneously, in the temporal case, models capture temporal patterns in parking slot occupancy, considering variables such as time of day and seasonal variations.Federated aggregation combines the models’ insights, uniting the knowledge acquired from these diverse data sources. The result is a final model that harmonizes spatial and temporal understanding, while respecting data privacy and maintaining data security. This innovative approach results in a unified model that provides a nuanced and comprehensive understanding of parking slot prediction, with federated learning techniques safeguarding data integrity.