In this paper the authors present social networks in an agent-based model (ABM) for carpooling. The model for the carpooling application is a computational model for simulating the interactions of autonomous agents and for analysing the effects of change in factors related to the infrastructure, behaviour and cost. Primarily, the authors focus on their agent-based approach for creating social networks for the carpooling application using socio-demographic data and daily activity-trip schedules estimated by Feathers, which is an activity-based traffic demand model. Social networks for the carpooling application, called carpooling SocNet in this paper, depicts the potential relationship information between carpoolers. Relationship data is needed to initiate the agent communication model and then employ a route matching algorithm and a utility function to trigger the negotiation process between agents. To generate carpooling SocNet, the authors proposed three similarity measures: profile, path and time interval similarity measure. In order to test the three similarity measures, experiments were conducted with input data in the Hasselt region and Limburg province, Belgium. As a result, it shows an interesting relationship information between the agents, which people in the study area have 65% of similarity to each other based on socio-economic attributes. Moreover, the authors found it is important to find an optimal value of the threshold because of the impact on finding a carpool partner and dependency on the study area. They plan to, as a part of the future work, use this carpooling SocNet data and feed it to their agent-based model to initiate communication, coordination and negotiation in carpooling.