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2 The emergence of internet and smartphones had played an important role in the increase of 3 on-demand economy. Crowdshipping (CS) is an emerging trend that is expected to reduce the 4 externalities caused by Urban Freight Transport (UFT). However, modelling the CS services, 5 predicting their market share and their effect in the network is not a trivial task. CS matches the 6 demand created by freight transport companies with the available capacity offered by 7 passengers. Currently a gap exists in the literature on models that integrate the decisions related 8 to the supply and the choices that identify the demand and matches them in the real-time. This 9 paper presents a theoretical methodological framework that proposes an innovative collection 10 of preference data in order to develop choice models that identify the need willingness of 11 commuters to crowdship. In parallel it calculates the demand and proposes the development of 12 a real-time matching simulator for the assignment of packets to crowdshippers and then to the 13 network. 14