Загрузка...
Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks
Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this...
Сохранить в:
| Главные авторы: | , , , , , |
|---|---|
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
MDPI AG
2019-05-01
|
| Серии: | ISPRS International Journal of Geo-Information |
| Предметы: | |
| Online-ссылка: | https://www.mdpi.com/2220-9964/8/6/243 |
| Метки: |
Добавить метку
Нет меток, Требуется 1-ая метка записи!
|