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.