This　research　investigates　the　impacts　of　the　driver＇s　risky　driving　behavior　on　traffic　crash　risk　detection　by　constructing　four　models,　including　models　with　and　without　the　use　of　risky　driving　behavior　data　before　and　during　crash.　The　major　findings　are　as　follows.　First,　the　accuracy　of　the　model　was　improved　by　adding　risky　driving　behavior　variables　to　the　traffic　crash　risk　detection　model.　In　the　optimal　model,　the　accuracy,　recall,　precision,　false　alarm　rate,　and　the　missing　report　rate　are　93.0%,　91.2%,　77.5%,　6.6%,　and　8.8%　respectively.　Second,　the　impact　of　traffic　flow　variables　on　crash　risk　in　the　model　was　modified　by　introducing　risky　driving　behavior　variables.　Among　them,　the　impact　of　traffic　volume　on　crash　risk　increases,　while　the　relationship　between　the　average　speed,　congestion　index,　and　crash　risk　decreases.　Third,　the　effects　of　changes　in　risky　driving　behavior　and　traffic　flow　on　the　risk　of　traffic　crashes　are　different　at　the　time　slices　and　space　segments,　and　this　effect　relationship　is　characterized　by　nonlinearity.　The　results　demonstrate　the　influence　of　temporal　and　spatial　characteristics　of　risky　driving　behaviors　on　crash　risk,　which　supports　detection　of　traffic　crash　risks　in　time　and　develops　crash　prevention　measures.