Table 2 Details of each variable before analysis
Feature factors | Data source and pre-processing |
|---|---|
Passenger flow | The daily passenger flow across different stations from October 21 to October 27, 2023, was estimated based on the scaling coefficients derived from the available passenger flow data on January 15, 2018 (Shanghai Government Data Portal). |
Day of the week | Weekdays and weekends. |
Time of the day | Morning rush hour (7:30 am to 9:30 am), noontime non-rush hour (10:00 am to 3:00 pm), and evening rush hour (4:30 pm to 8:00 pm). |
Door type | The automatic screen door type on each platform is sorted according to the height of the screen door, from half to full. |
Depth | The excavation depth of the standard section was surveyed by personal conversation with subway staff. This study classified the depth (in meters) into three levels using the K-means clustering method. |
Train arrival interval | The time interval between two consecutive trains arriving at the station was derived from the official website of Shanghai Subway (https://service.shmetro.com/hcskb/index.htm). |
Outdoor PM2.5 | Concurrent outdoor PM2.5 near each subway station. The data was derived from the Shanghai Air Quality Monitoring Station, with spatial connection assigned by ArcGIS. |
Outdoor PM10 | Concurrent outdoor PM10 near each subway station. The data was derived from the Shanghai Air Quality Monitoring Station, with spatial connection assigned by ArcGIS. |
Temperature | Temperature outside each subway station, which was derived from the Shanghai Meteorological Bureau, with spatial connection assigned by ArcGIS. |
RH (Relative Humidity) | RH outside each subway station, which was derived from the Shanghai Meteorological Bureau, with spatial connection assigned by ArcGIS. |
