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Disaggregate choice models have been widely studied to quantify the influence of the characteristics of travelers as well as the attributes of alternatives and choices in their travel modes. However, due to their model specifications and primary assumptions on unobserved disturbances, their modeling capability is constrained. In this study, a Markov Logic Network (MLN)-based approach is developed to combine bounded rationality principles with travelers' behavior in travel mode choices. This approach is established based on logical domain knowledge and probabilistic models. MLN can extract logical domain knowledge and represent the impacts of significant attributes using independent logical formulas that are weighted correspondingly by their relative relationships. Travel-mode choice is determined based on travelers' personal preferences and logical domain knowledge. The numerical examples and parameter sensitivity analyses indicate this approach performs reasonably well. The research findings are helpful to better understand travel mode-choice model specifications and travel behavior interpretations. [ABSTRACT FROM AUTHOR]