A relatively new method for data analysis called the adaptive neuro-fuzzy inference system (ANFIS) is illustrated with an example from travel behavior modeling. The options offered by this data analysis technique are illustrated by using data from Australia. In addition, an experiment was performed to identify the optimal split of data into an estimation sample and a validation sample. Comparisons of ANFIS and the more traditional regression methods such as ordinary least-squares linear regression and negative binomial regression were performed by using the mean square error and correlation coefficients. ANFIS is shown to be a useful tool, and its further use in travel behavior research and applications in transportation planning is recommended. However, many additional issues require further scrutiny and experimentation, which are discussed.