Urban Rail Transit Hourly Ridership Evolution Model under Rainfall Weather

The lack of understanding of ridership evolution frequently causes emergency operation plans to fail when ridership increases suddenly. Based on a multiple regression model, this paper focuses on the urban rail transit ridership evolution under rainfall. The first stage explores the rainfall-ridership relationship. The second stage establishes a multiple regression model using the hourly ridership and the current hour, the current hourly rainfall, the previous hourly rainfall, the previous hourly ridership, and the rainfall in the previous period. The third stage uses the rainfall and ridership data in Nanjing in 2016 and 2017 to train and test the model and confirmed that the ridership prediction of the multiple regression model is close to the actual ridership. The results can predict future hourly ridership at stations and support transportation system management to allow operation plans to be adjusted according to predictions.