Abstract: Previous methods on forecasting passenger flow of rail transit lacks consideration of dynamic volatility,and cannot predict the range of short-term passenger flow.Taking typical rail transit stations in Beijing as a case study,an ARIMA-GARCH model is established to simulate the prediction interval (PI),and fit the stochastic volatility of shortterm passenger flow.The effect of "sharp peak and heavy tail" is analyzed by using t distribution.The asymmetry volatility effects are addressed by using T-GARCH and E-GARCH models.Results show that the integrated ARIMA-GARCH models can significantly reduce the mean prediction interval length (MPIL) in forecasting passenger flow by more than 20%,and improve the prediction interval coverage probability (PICP) by about 1%.It is also found that volatility of passenger flow in weekdays is larger than weekends,while no evident volatility exists during non-peak hours.Note that,an ARIMA-GARCH model will not significantly reduce mean absolute prediction error (MAPE).However,the hybrid models can accurately forecast the range of passenger flow of rail transit under the premise of ensuring single-point forecasting.