Because existing models have their limitations, there is a significant need for a model to estimate demand for intercity bus services, especially in rural areas. The general objective of this research was to develop an intercity mode choice model that can be incorporated into a statewide travel demand model to estimate demand for rural intercity bus services. Four intercity transportation modes were considered in the study: automobile, bus, rail, and air. A stated preference survey was conducted of individuals across the state of North Dakota, and a mixed logit model was developed to estimate a mode choice model. Results from the mode choice model showed the significant impacts of individual, trip, and mode characteristics on choice of mode. Gender, age, income, disability, trip purpose, party size, travel time, travel cost, and access distance were all found to have significant impacts on mode choice, and traveler attitudes were also found to be important. The study demonstrated how the mode choice model can be incorporated into a statewide travel demand model, and intercity bus mode shares were estimated for origin-destination pairs within the state. Alternative scenarios were analyzed to show how mode shares would change under different conditions or service characteristics. This study was conducted in the largely rural state of North Dakota, but results could be transferable to other areas with similar geographic characteristics. 2.1.1 Major Ridership Generators Studies of intercity bus demand need to go beyond the simple gravity model to include places that are potential attractors of intercity bus ridership. Previous studies have included colleges and universities, major military bases, hospitals and major medical facilities, regional correctional facilities, recreation areas, and major intermodal connections at airports as facilities that would attract ridership (Utah Department of Transportation 2010, KFH Group 2003, Yang and Cherry 2012). Yao and Morikawa (2005) calculated attractiveness using factors such as number of headquarters, number of international conferences, and other business-relevant factors for business trips. For non-business travel, they used factors capturing suitability for leisure activities, such as number of resorts, sport centers, museums, cinemas, shopping centers, etc.