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Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network
| Content Provider | MDPI |
|---|---|
| Author | Liu, Menghang Li, Luning Li, Qiang Bai, Yu Hu, Cheng |
| Copyright Year | 2021 |
| Description | Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into the sidewalks. The crowd count changes dynamically over time with various external factors, such as surroundings, weekends, and peak hours, so it is essential to predict the accurate and timely crowd count. To address this issue, this study introduces graph convolutional network (GCN), a network-based model, to predict the crowd flow in a walking street. Compared with other grid-based methods, the model is capable of directly processing road network graphs. Experiments show the GCN model and its extension STGCN consistently and significantly outperform other five baseline models, namely HA, ARIMA, SVM, CNN and LSTM, in terms of |
| Starting Page | 455 |
| e-ISSN | 22209964 |
| DOI | 10.3390/ijgi10070455 |
| Journal | ISPRS International Journal of Geo-Information |
| Issue Number | 7 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-07-02 |
| Access Restriction | Open |
| Subject Keyword | ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Transportation Science and Technology Pedestrian Flow Prediction Graph Convolutional Network (gcn) Open Public Places Model Performance |
| Content Type | Text |
| Resource Type | Article |
