Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction

Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction

The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial cha...

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Bibliographic Details
Main Authors: Liangpeng Gao, Wenli Fan, Wenliang Jian
Format: Article
Language:English
Published: MDPI AG 2024-07-01
Series:Applied Sciences
Subjects:
vacant parking spaces
spatio-temporal features
node embedding
graph convolution
Online Access:https://www.mdpi.com/2076-3417/14/13/5927

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https://www.mdpi.com/2076-3417/14/13/5927

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