Is Crowdshipping a solution for congestion? A passenger-freight matching model for crowdshipping services

Crowdshipping (CS) is an emerging form of freight transport that is expected to reduce the externalities of urban freight transport. The supply of CS services originates from people with an intention to travel, who can choose to engage in a parcel delivery service as incidental carrier. The popular expectation is that this consolidation of freight and passenger trips could save freight trips and thus alleviate urban transport congestion and environmental pollution. A key challenge in the prediction of CS service volumes and impacts, however, is to match existing service demand and supply. This has not yet been addressed in the literature with models that give an empirically realistic representation of individual decision-making. We approach this problem using a disaggregate activity-based models for urban passenger transport and freight transport. Allocation of parcels to travellers is done based on a simulated random utility discrete choice model. We present a first case study for the city of The Hague, The Netherlands, to illustrate the model empirically.

Rodrigo Tapia has experience in stated preference design, choice modelling and Agent-Based Models. He holds an MSc and a PhD from the UFRGS (Brazil) in Transportation Systems, having developed his thesis in freight choice modelling. During this period he worked in the Economic Management department and the Freight & Logistics department at the Ministry of Transport of Argentina. After his PhD, in 2019, he worked as a researcher in the Choice Modelling Centre at the Institute for Transport Studies of the University of Leeds and collaborated with Significance as an external researcher. Since 2020, he is a researcher in the Transport and Planning department of TU Delft working on Horizon projects using Agent-Based and behavioural models and applying them in the freight context and in 2022 he has joined Significance as a researcher.