Traffic　crash　detection　is　a　promising　and　challenging　research　topic.　Due　to　the　limitations　of　data　collection,　previous　studies　mainly　used　traffic　flow　variables　to　establish　a　traffic　crash　detection　model,　and　the　contribution　of　risky　driving　behavior　to　the　traffic　crash　detection　model　was　not　clear.　The　widespread　application　of　traffic　detectors　and　in-vehicle　AutoNavigator　software　make　it　possible　to　collect　and　update　real-time　traffic　flow　data　and　risky　driving　behavior　data　in　a　short　period　of　time.　These　data　lay　the　foundation　for　this　study,　which　aims　to　quantify　the　improvement　degree　of　risky　driving　behavior　in　a　traffic　crash　detection　model　and　then　analyze　the　coupling　effect　of　risky　driving　behavior　and　traffic　operation　state　on　the　impact　of　traffic　crashes.　In　this　research,　we　investigated　real-time　and　dynamic　traffic　flow　data　and　risky　driving　behavior　data　by　using　eXtreme　Gradient　Boosting　(XGBoost)　and　the　logistic　regression　algorithm,　respectively.　In　addition,　SHapley　Additive　exPlanation　(SHAP)　was　employed　to　analyze　the　results　and　the　importance　of　individual　features.　The　results　indicate　that　the　model　with　the　combined　inputs　has　increased　accuracy　of　8%　and　nearly　a　5%　reduction　in　the　false　alarm　rate.　The　results　of　feature　importance　analysis　show　that　in　the　variables　of　risky　driving　behavior　and　traffic　flow,　the　most　important　feature　influencing　traffic　crashes　is　sharp　deceleration.　In　addition,　the　characteristics　of　risky　driving　behavior　increase　or　decrease　the　probability　of　traffic　crashes　caused　by　traffic　flow　characteristics.　The　results　of　this　paper　will　help　with　real-time　crash　detection　and　relevant　policy-making.(C)　2021　Elsevier　B.V.　All　rights　reserved.