There is no doubt that the travel demand forecasting stage is the most crucial stage in any transportation project, with the aim of improving existing facilities or establishing one from scratch. Although transportation planners agree widely about the four conventional steps of demand forecasting, there are inherent debates about their algorithmic applications. This article gives a thorough review of the demand forecasting stages. In addition, a complete framework is given for the process of travel demand forecasting in multi-modal networks, which considers the interdependence between the forecasting steps and offers the ability to incorporate the advances of machine learning and transportation simulation programs for the sake of accuracy.