The emergence of internet and smartphones had played an important role in the increase of on-demand economy. Crowdshipping (CS) is an emerging trend that is expected to reduce the externalities caused by Urban Freight Transport (UFT). However, modelling the CS services, predicting their market share and their effect in the network is not a trivial task. CS matches the demand created by freight transport companies with the available capacity offered by passengers. Currently a gap exists in the literature on models that integrate the decisions related to the supply and the choices that identify the demand and matches them in the real-time. This paper presents a theoretical methodological framework that proposes an innovative collection of preference data in order to develop choice models that identify the need willingness of commuters to crowdship. In parallel it calculates the demand and proposes the development of a real-time matching simulator for the assignment of packets to crowdshippers and then to the network
