Smart City Gnosys

Smart city article details

Title	Residual Spatial-Temporal Graph Convolutional Neural Network For On-Street Parking Availability Prediction
ID_Doc	45999
Authors	Chen G.; Zhang S.; Weng W.; Yang W.
Year	2023
Published	International Journal of Sensor Networks, 43, 4
DOI	http://dx.doi.org/10.1504/IJSNET.2023.135840
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. © 2023 Inderscience Enterprises Ltd.
Author Keywords	graph neural network; on-street parking availability prediction; RST-GCNN

Similar Articles

Id	Similarity	Authors	Title	Published
52514	0.952	Xiao X.; Jin Z.; Hui Y.; Cheng N.; Luan T.H.	Spatial-Temporal Graph Convolutional Networks For Parking Space Prediction In Smart Cities	IEEE Vehicular Technology Conference, 2021-September (2021)
4604	0.912	Wang T.; Li S.; Li W.; Yuan Q.; Chen J.; Tang X.	A Short-Term Parking Demand Prediction Framework Integrating Overall And Internal Information	Sustainability (Switzerland), 15, 9 (2023)
42703	0.9	Bilotta S.; Ipsaro Palesi L.A.; Nesi P.	Predicting Free Parking Slots Via Deep Learning In Short-Mid Terms Explaining Temporal Impact Of Features	IEEE Access, 11 (2023)
16834	0.899	Qian C.; Yang K.; He J.; Peng X.; Huang H.	Curb Parking Occupancy Prediction Based On Real-Time Fusion Of Multi-View Spatial-Temporal Information Using Graph Attention Gated Networks	Applied Soft Computing, 171 (2025)
1340	0.897	Arjona J.; Linares M.P.; Casanovas J.	A Deep Learning Approach To Real-Time Parking Availability Prediction For Smart Cities	ACM International Conference Proceeding Series (2019)
30883	0.892	Arjona J.; Linares M.; Casanovas-Garcia J.; Vázquez J.J.	Improving Parking Availability Information Using Deep Learning Techniques	Transportation Research Procedia, 47 (2020)
22414	0.892	Ramesh M.; Sriram N.; Suganth K.M.; Shree Tharun Krushna K.S.; Anitha Rani A.; Kodeeswari K.	Efficient Smart Parking Space Identification And Classification System Using Yolov8 Network Model	3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025 (2025)
60526	0.891	Ribas M.M.; Mendes H.B.; De Oliveira L.E.; Zanlorensi L.A.; De Almeida P.L.	Using Deep Neural Networks To Quantify Parking Dwell Time	Proceedings - 2024 International Conference on Machine Learning and Applications, ICMLA 2024 (2024)
38660	0.89	Inam S.; Mahmood A.; Khatoon S.; Alshamari M.; Nawaz N.	Multisource Data Integration And Comparative Analysis Of Machine Learning Models For On-Street Parking Prediction	Sustainability (Switzerland), 14, 12 (2022)
4739	0.889	Mettupally S.N.R.; Menon V.	A Smart Eco-System For Parking Detection Using Deep Learning And Big Data Analytics	Conference Proceedings - IEEE SOUTHEASTCON, 2019-April (2019)