In order to address concerns about the dominance of petroleum-fueled vehicles, the transition to alternative-fueled counterparts is urgently needed. Top barriers preventing the widespread diffusion of alternative-fuel vehicles (AFV) are the limited range and the scarcity of refueling or recharging infrastructures in convenient locations. Researchers have been developing models for optimally locating refueling facilities for range-limited vehicles, and recently a strategy has emerged to cluster refueling stations to encourage consumers to purchase alternative-fuel vehicles by building a critical mass of stations. However, clustering approaches have not yet been developed based on flow-based demand. This study proposes a Threshold Coverage extension to the original Flow Refueling Location Model (FRLM). The new model optimally locates p refueling stations on a network so as to maximize the weighted number of origin zones whose refuelable outbound round trips exceed a given threshold, thus to build critical mass based on flow-based demand on the network. Unlike other clustering approaches, this model can explicitly ensure that flow demands “covered” in the model are refuelable considering the limited driving range of AFVs. Despite not explicitly including local intra-zonal trips, numerical experiments on a statewide highway network proved the effectiveness of the model in clustering stations based on inter-city flow volumes on the network. The model’s policy implementation will provide managerial insights for some key concerns of the industry, such as geographic equity vs. critical mass, from a new perspective. This project will serve as a step to support a more successful public transition to alternative-fuel vehicles.