Residual spatial-temporal graph convolutional neural network for on-street parking availability prediction
by Guanlin Chen; Sheng Zhang; Wenyong Weng; Wujian Yang
International Journal of Sensor Networks (IJSNET), Vol. 43, No. 4, 2023

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.

Online publication date: Mon, 08-Jan-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com