Quantitative assessment of peer-to-peer car-sharing potential in French cities

Peer-to-peer (P2P) car-sharing enables privately owned vehicles to be temporarily shared, offering a flexible alternative to traditional car ownership. However, its city-scale potential remains poorly understood, as existing studies rarely consider spatial and temporal compatibility between travel demand and vehicle availability. Addressing this gap, this study proposes a simulation-based framework to assess the potential of P2P car-sharing using agent-based transportation modeling. Daily activity–travel patterns derive demand- and supply-side availability windows, integrated into a two-step matching process. It first identifies a theoretical set of candidate matches under relaxed constraints and subsequently determines compatible assignments under operational constraints and assignment objectives. The framework is applied to three French cities, Dunkirk, Nantes, and Paris, selected to represent distinct urban forms, population scales, and mobility structures. Findings suggest that P2P car-sharing has the potential to substitute for some car-based trips, contributing to reduced car ownership, lower parking pressure, and more efficient use of existing vehicle resources. The proposed framework provides a scalable and transferable approach to assess P2P car-sharing potential, and supports decision-making by identifying where and when such systems may be operationally feasible for targeting context-specific mobility policies. From a policy perspective, results reveal persistent spatial and temporal imbalances, highlighting pronounced intra-urban heterogeneity, and indicating that uniform deployment strategies are unlikely to be effective. Instead, P2P car-sharing policies should be spatially targeted and time-sensitive, particularly for complementing existing mobility services, in areas with limited public transport accessibility. Furthermore, emerging automated vehicles may enhance P2P car-sharing by enabling vehicle repositioning and improving assignment flexibility.