  The ability to predict city-wide parking availability is crucial for the
successful development of Parking Guidance and Information (PGI) systems.
Indeed, the effective prediction of city-wide parking availability can improve
parking efficiency, help urban planning, and ultimately alleviate city
congestion. However, it is a non-trivial task for predicting citywide parking
availability because of three major challenges: 1) the non-Euclidean spatial
autocorrelation among parking lots, 2) the dynamic temporal autocorrelation
inside of and between parking lots, and 3) the scarcity of information about
real-time parking availability obtained from real-time sensors (e.g., camera,
ultrasonic sensor, and GPS). To this end, we propose Semi-supervised
Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide
parking availability. Specifically, we first propose a hierarchical graph
convolution structure to model non-Euclidean spatial autocorrelation among
parking lots. Along this line, a contextual graph convolution block and a soft
clustering graph convolution block are respectively proposed to capture local
and global spatial dependencies between parking lots. Additionally, we adopt a
recurrent neural network to incorporate dynamic temporal dependencies of
parking lots. Moreover, we propose a parking availability approximation module
to estimate missing real-time parking availabilities from both spatial and
temporal domain. Finally, experiments on two real-world datasets demonstrate
the prediction performance of SHARE outperforms seven state-of-the-art
baselines.
