Most　of　the　safety　studies　mainly　takes　the　probability　of　crashes　or　relevant　records　as　the　main　indicators.　Accident-based　safety　analysis　may　lead　to　overestimation　or　underestimation　of　traffic　risks,　which　are　delayed　and　sparse.　Aggressive　driving　behavior　is　one　of　the　major　causes　of　traffic　accidents,　which　can　accurately　measure　traffic　risk,　but　is　not　widely　adopted　due　to　limitations　in　data　collection.　This　paper　took　the　Traffic　Order　Index　(TOI)　as　the　surrogate　index　of　safety　risk　based　on　aggressive　driving　behavior　and　speed　variation　and　developed　the　Multinomial　Logistic　Regression　(MLR)　and　the　Random　Forest　(RF)　model　to　identify　risk　level　on　bridge　sections　of　freeway,　which　can　minimize　the　restrictions　of　crash　occurrence　or　crash-related　data　in　discovering　contributing　factors　of　traffic　risks.　The　results　revealed　that　the　RF　has　a　better　performance　than　MLR　in　the　performance　comparison　of　the　two　classifiers.　The　feature　importance　based　on　the　Gini　coefficient　was　used　to　identify　the　most　influential　variable　of　identified　results　of　risk.　The　top　four　ranked　variables　that　significantly　affect　the　identified　results　of　traffic　order　level　are　congestion　index,　road　section　types,　the　level　of　the　number　of　users,　and　weather.　In　addition,　the　partial　dependency　plots　of　the　explanatory　variables　are　presented　to　reveal　interactions　between　different　variable　types　on　traffic　risks.　Finally,　the　conclusion　based　on　the　traffic　order　level　analysis　has　basically　corresponded　to　the　accident　analysis.　Identification　of　these　specific　risk　prone　conditions　could　improve　our　understanding　of　traffic　risk　and　would　shed　light　on　countermeasures　for　improving　the　safety　of　bridge　sections　of　freeways.　(C)　2022　Elsevier　B.V.　All　rights　reserved.