Predicting crowd movements in real-time during mass events has been shown to be a complex yet valuable task in order to reduce the risk of overcrowding. The aim of this research is to propose and validate a crowd movement forecasting method for which simulation is performed offline (i.e. prior to the event) but the forecast is done online, in real-time. A number of scenarios is formulated and simulated creating what is called a database of scenarios. In real-time, based on information from the event's crowd monitoring systems, a scenario from this database is then selected which corresponds to the prediction. The research is focused on addressing the concepts related to the two pillars of the method: the formulation of the scenarios to be included in the database, and the operationalization of the system to select a scenario in real-time.