Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

Authors

  • Weijia Zhang University of Science and Technology of China
  • Hao Liu National Engineering Laboratory of Deep Learning Technology and Application
  • Yanchi Liu Rutgers University
  • Jingbo Zhou National Engineering Laboratory of Deep Learning Technology and Application
  • Hui Xiong National Engineering Laboratory of Deep Learning Technology and Application

DOI:

https://doi.org/10.1609/aaai.v34i01.5471

Abstract

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 city-wide 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 \hmgnn outperforms seven state-of-the-art baselines.

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Published

2020-04-03

How to Cite

Zhang, W., Liu, H., Liu, Y., Zhou, J., & Xiong, H. (2020). Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 1186-1193. https://doi.org/10.1609/aaai.v34i01.5471

Issue

Section

AAAI Technical Track: Applications