Rural transit demand forecasting is a tool that aids planners and analysts in the allocation of scarce resources for typically underserved populations. As the number of privately owned automobiles has increased over the last several decades, provision of public transportation has decreased, lessening non-drivers ability to participate in the workforce, take advantage of social service programs, and to receive adequate medical care. Using Washington as the case study, three models were developed based on the characteristics of usage for several transportation systems currently in place in four Washington counties. Peer analysis was used to create models with varying levels of complexity and data requirements to predict ridership on county-wide public transportation systems. Of the three models, the Disaggregated Transit Demand (DTD) model estimation techniques are the most refined and flexible. This model provides a significant starting point for developing accurate equations for predicting transit need and demand for underserved areas around the state.