Research Article
A Hybrid Spatiotemporal Deep Learning Model for Short-Term Metro Passenger Flow Prediction
Table 1
Description statistics of considered variables.
| Variables | Description | Min | Max | Mean | S.D. |
| Metro flow inbound | Number of passengers entering into station every 10 minutes | 0 | 1020 | 83.16 | 188.62 | Metro flow outbound | Number of passengers exiting from station every 10 minutes | 0 | 1223 | 85.37 | 194.57 | Temperature | The hourly average temperature during the time interval (°C) | 15.3 | 37.3 | 24.321 | 2.935 | Precipitation | The hourly average precipitation during the time interval (mm) | 0 | 49.2 | 0.075 | 1.126 | Wind speed | The hourly average wind speed during the time interval (m/s) | 0 | 14.3 | 3.207 | 1.961 | Pressure | The hourly average pressure during the time interval (hPa) | 1000 | 1010.2 | 1005.21 | 2.102 |
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