Matching analysis for peer-to-peer car-sharing using synthetic populations in France

Abstract

Peer-to-peer (P2P) car-sharing enables privately owned vehicles to be temporarily shared through digital platforms, yet its realistic urban potential remains insufficiently understood. This study applies synthetic populations to assess P2P car-sharing potential. Daily activity–travel patterns are analyzed to extract demand and supply. A two-step assignment process identifies spatially and temporally feasible matches and determines compatible assignments under operational constraints. The framework is applied to French cities, Dunkirk, Nantes, and Paris, representing diverse urban contexts. Results show that walking distance strongly influences matching levels, while temporal patterns are consistent across cities. A significant share of individuals have no feasible match, revealing spatial and temporal imbalances, while a small subset accounts for most interactions. Findings indicate that P2P car-sharing shows the potential to reduce car ownership and parking demand, and support public transport in low-access areas. The framework provides a scalable tool for assessing operational feasibility at the urban scale.