In order to forecast the demands for American Disability Act (ADA) travel, many complicated factors are needed to be considered, including, but not limited to socioeconomic data and service operational characteristics. In other words, the choice of suitable explanatory variables to construct an ADA travel demand model is an important and complex task. Data mining techniques provide a promising way to select the explanatory variables. They work especially well in situations where the number of relevant variables is large and where the interactions among variables or models are not clear. In this study, we applied data mining techniques for selecting variables and building models. Census data was mined to select candidate variables. Also, we compared the performances of three types of models: (1) a traditional linear model, (2) a traditional model with variables selected by the classification and regression tree (CART) method, and (3) a traditional model with the variables selected by the random forest (RF) method. The results show that the fraction of senior citizens (age > 65), average household size (owner occupied), fraction of African Americans, fraction of Hispanics, annual household income, fraction of males, median age, fraction of households with a family member with a disability, and the total population, are the significant variables for modeling ADA travel demands.