Existing agent-based models of co-diffusion of alternative fuel vehicles and their corresponding filling stations are very stylized. In addition, there is a lack of  methodologies for linking such models with a real social and economic background. Aiming at solving this problem, this paper puts forward a method that uses widely  available social, economic, and spatial data to generate driver agents' commute trips, which play an important role in such models. We tested the method with the data of  Shanghai and Beijing, two of the largest cities in China, and found the commute times resulting from the method were in accordance with survey results, which validates the  potential usefulness of the method.