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

arXiv: https://arxiv.org/abs/1911.10516 · HTML: https://mlsanity.com/paper/1911.10516/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2019.

## Built on by (most cited first, 30 of 30)

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- [AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting](https://mlsanity.com/paper/2409.16586/) (2024)
- [Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training](https://mlsanity.com/paper/2306.14126/) (2023)
- [Machine Learning for Urban Air Quality Analytics: A Survey](https://mlsanity.com/paper/2310.09620/) (2023)
- [Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification](https://mlsanity.com/paper/2410.18686/) (2024)
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- [DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction](https://mlsanity.com/paper/2108.08170/) (2021)
- [Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network](https://mlsanity.com/paper/2210.07765/) (2022)
- [Prediction-based One-shot Dynamic Parking Pricing](https://mlsanity.com/paper/2208.14231/) (2022)
- [Beyond Prediction: On-street Parking Recommendation using Heterogeneous Graph-based List-wise Ranking](https://mlsanity.com/paper/2305.00162/) (2023)
- [Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends](https://mlsanity.com/paper/2401.00713/) (2024)
- [Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach](https://mlsanity.com/paper/2405.18910/) (2024)
- [PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization](https://mlsanity.com/paper/2407.08910/) (2024)
- [Truck Parking Usage Prediction with Decomposed Graph Neural Networks](https://mlsanity.com/paper/2401.12920/) (2024)
- [TransLLM: A Unified Multi-Task Foundation Framework for Urban Transportation via Learnable Prompting](https://mlsanity.com/paper/2508.14782/) (2025)
- [CubeletWorld: A New Abstraction for Scalable 3D Modeling](https://mlsanity.com/paper/2511.17664/) (2025)

## Abstract (arXiv)

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.