The　prediction　of　traffic　crashes　is　an　essential　topic　in　traffic　safety　research.　Most　of　the　previous　studies　conducted　experiments　on　real-time　crash　prediction　of　expressways　or　freeways,　based　on　traffic　flow　data.　However,　the　influence　of　risky　driving　behavior　on　traffic　crash　risk　prediction　has　rarely　been　considered.　Thus,　a　traffic　crash　risk　prediction　model　based　on　risky　driving　behavior　and　traffic　flow　has　been　developed.　The　data　employed　in　this　research　were　captured　using　the　in-vehicle　AutoNavigator　software.　A　random　forest　to　select　variables　with　strong　impacts　on　crashes　and　the　synthetic　minority　oversampling　technique　(SMOTE)　to　adjust　the　imbalanced　dataset　were　included　in　the　research.　A　logistic　regression　model　was　developed　to　predict　the　risk　of　traffic　crash　and　to　interpret　its　relationship　with　traffic　flow　and　risky　driving　behavior　characteristics.　This　model　accurately　predicted　84.48%　of　the　crashes,　while　its　false　alarm　rate　remained　as　low　as　9.75%,　which　indicated　that　this　traffic　crash　risk　prediction　model　had　high　accuracy.　By　analyzing　the　relationship　between　traffic　flow,　risky　driving　behavior,　and　crashes　through　partial　dependency　plots　(PDPs),　the　impact　of　traffic　flow　and　risky　driving　behavior　variables　on　certain　traffic　crashes　in　the　prediction　model　were　determined.　Through　this　study,　the　data　of　traffic　flow　and　risky　driving　behavior　could　be　used　to　assess　the　traffic　crash　risk　on　freeways　and　lay　a　foundation　for　traffic　safety　management.