Interchanges　are　important　highway　facilities　by　connecting　two　or　more　freeways/expressways;　however,　it　has　been　considered　more　dangerous　than　basic　segments　because　of　drivers＇　decision-making　to　stay　or　exit,　weaving,　variations　in　speeds,　etc.　In　this　study,　we　aim　at　contributing　to　the　literature　by　using　a　quasi-induced　exposure　method　and　logistic　regression　modeling　approach　to　identify　contributing　factors　associated　with　the　risk　of　causing　an　interchange　crash.　To　this　end,　the　2014　traffic　crash　data,　roadway　and　drivers＇　characteristics　were　collected　from　Florida.　The　modeling　results　indicate　that　drivers＇　age,　gender,　distraction,　alcohol,　and　other　factors　have　statistically　significant　effects.　In　addition,　the　finding　suggests　that　drivers　are　more　likely　to　cause　crashes　at　cloverleaf　and　direct　connection　interchanges　than　at　diamond　interchanges.　Furthermore,　a　support　vector　machine　(SVM)　model　was　applied　to　compare　its　predictability　with　the　logistic　regression　model,　and　a　sensitivity　analysis　was　conducted.　From　the　comparison　of　the　areas　under　the　receiver　operating　characteristic　curve　(AUC)　of　the　two　approaches,　it　shows　that　the　SVM　model　outperforms　the　logistic　regression　model.　It　is　expected　that　the　findings　would　help　establish　effective　strategies　to　reduce　traffic　crashes　at　interchanges　by　targeted　education,　engineering,　and　enforcement.