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Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction
| Content Provider | MDPI |
|---|---|
| Author | Xiao, Xiao Jin, Zhiling Hui, Yilong Xu, Yueshen Shao, Wei |
| Copyright Year | 2021 |
| Description | With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called |
| Starting Page | 3338 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13163338 |
| Journal | Remote Sensing |
| Issue Number | 16 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-08-23 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Transportation Science and Technology Hybrid Spatial–temporal Graph Convolutional Networks On-street Parking Availability Prediction Parking Occupancy Rate Parking Durations |
| Content Type | Text |
| Resource Type | Article |
