Article: Residual spatial-temporal graph convolutional neural network for on-street parking availability prediction Journal: International Journal of Sensor Networks (IJSNET) 2023 Vol.43 No.4 pp.246 - 257 Abstract: Smart cities can provide people with a wealth of information to make their lives more convenient. Among many other benefits, effective parking availability prediction is essential as it can improve the overall efficiency of parking and significantly reduce city congestion and pollution. In this paper, we propose a novel model for parking availability prediction, i.e., the residual spatial-temporal graph convolutional neural network, which enhances the accuracy and efficiency of the prediction process. The model utilises graph neural networks and temporal convolutional networks to capture the spatial and temporal features, respectively, fusing through a residual structure called the residual spatial-temporal convolutional block. We conducted experiments using real-world datasets to compare the performance of the proposed model with that of the baseline models. The experimental results demonstrate that our model outperforms the baseline models in predicting the long-term parking occupancy rate and achieves the fastest prediction speed. Inderscience Publishers - linking academia, business and industry through research

Title: Residual spatial-temporal graph convolutional neural network for on-street parking availability prediction

Authors: Guanlin Chen; Sheng Zhang; Wenyong Weng; Wujian Yang

Addresses: School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China; School of Computer Science, Zhejiang University, Hangzhou, 310012, China ' School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China; School of Computer Science, Zhejiang University, Hangzhou, 310012, China ' School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China ' School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China

Abstract: Smart cities can provide people with a wealth of information to make their lives more convenient. Among many other benefits, effective parking availability prediction is essential as it can improve the overall efficiency of parking and significantly reduce city congestion and pollution. In this paper, we propose a novel model for parking availability prediction, i.e., the residual spatial-temporal graph convolutional neural network, which enhances the accuracy and efficiency of the prediction process. The model utilises graph neural networks and temporal convolutional networks to capture the spatial and temporal features, respectively, fusing through a residual structure called the residual spatial-temporal convolutional block. We conducted experiments using real-world datasets to compare the performance of the proposed model with that of the baseline models. The experimental results demonstrate that our model outperforms the baseline models in predicting the long-term parking occupancy rate and achieves the fastest prediction speed.

Keywords: RST-GCNN; on-street parking availability prediction; graph neural network.

DOI: 10.1504/IJSNET.2023.135840

International Journal of Sensor Networks, 2023 Vol.43 No.4, pp.246 - 257

Received: 30 Jun 2023
Accepted: 04 Jul 2023
Published online: 08 Jan 2024 *

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Title: Residual spatial-temporal graph convolutional neural network for on-street parking availability prediction

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