Semi-Supervised City-Wide Parking Availability Prediction via Hierarchical Recurrent Graph Neural Network
Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, Tong Xu, Hui Xiong
Abstract
The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. The effective prediction of city-wide parking availability can boost parking efficiency, improve urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for city-wide parking availability prediction 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 bluetooth sensor). To this end, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>S</u>emi-supervised <u>H</u>ier<u>a</u>rchical <u>Re</u>current Graph Neural Network-<u>X</u></i> ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SHARE-X</small> ) to predict parking availability of each parking lot within a city. Specifically, we first propose a hierarchical graph convolution module to model the non-euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a multi-resolution soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Moreover, we devise a hierarchical attentive recurrent network module to incorporate both short and long-term dynamic temporal dependencies of parking lots. Additionally, a parking availability approximation module is introduced to estimate missing real-time parking availabilities from both spatial and temporal domains. Finally, experiments on two real-world datasets demonstrate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SHARE-X</small> outperforms eight state-of-the-art baselines in parking availability prediction.