As a result of the outbreak of COVID-19, the demand for rail travel has decreased. It is not reasonable to compare the rail travel demand during the pandemic year with previous years in order to examine this decrease. The travel demand forecast for the pandemic year should be compared with the actual demand for the pandemic year. In this study, the effect of the COVID-19 pandemic on passenger demand for Iranian railways has been investigated. Using seasonal data from 2011 to 2018, the linear regression model, multilayer perceptron neural network, SARIMA, HoltWinters, and a combination of them (the average result of other models) have been fitted. Rail travel demand for the year before the COVID-19 outbreak (normal conditions) is predicted, and the models' results are compared based on MAD, RMSE, and MAPE. Finally, using the superior model (the hybrid model), rail travel demand for the first year of the COVID-19 outbreak is forecast. Active population and employment have a positive relationship, and vehicles per capita have a negative relationship with rail travel demand. Also, the annual rail travel demand for the Iranian railways in the period of one year after the outbreak of COVID-19 compared to the forecast of the superior model has decreased by 73.12 percent, which is equal to 13.9 billion passenger kilometers.