Traffic　violation　is　one　of　the　leading　causes　of　traffic　crashes.　In　the　context　of　global　aging,　it　is　important　to　study　traffic　violations　by　elderly　drivers　for　improving　traffic　safety　in　preparation　for　a　worldwide　aging　population.　In　this　study,　a　hybrid　approach　of　Latent　Class　Analysis　(LCA)　and　XGBoost　based　SHAP　is　proposed　to　identify　hidden　clusters　and　to　understand　the　key　contributing　factors　on　the　severity　of　traffic　violations　by　elderly　drivers,　based　on　the　police-reported　traffic　violation　dataset　of　Beijing　(China).　First,　LCA　is　applied　to　segment　the　dataset　into　several　latent　homogeneous　clusters,　then　XGBoost　based　SHAP　is　established　on　each　cluster　to　identify　feature　contributions　and　the　interaction　effects　of　the　key　contributing　factors　on　the　severity　of　traffic　violations　by　elderly　drivers.　Two　comparison　groups　were　set　up　to　analyze　factors,　which　are　responsible　for　the　different　severities　of　traffic　violations.　The　results　show　that　elderly　drivers　can　be　classified　into　four　groups　by　age,　urban　or　not,　license,　and　season;　factors　such　as　less　annual　number　of　traffic　violations,　national　&　provincial　highway,　night　and　winter　are　key　contributing　factors　for　higher　severity　of　traffic　violations,　which　are　consistent　with　common　cognition;　key　contributing　factors　for　all　clusters　are　similar　but　not　identical,　for　example,　more　annual　number　of　traffic　violations　contribute　to　more　severe　violation　for　all　clusters　except　for　Cluster　2;　some　factors　which　are　not　key　contributing　factors　may　affect　the　severity　of　traffic　violations　when　they　are　combined　with　other　factors,　for　example,　the　combination　of　lower　annual　number　of　traffic　violations　and　county　&　township　highway　contributes　to　more　severe　violation　for　Cluster　1.　These　findings　can　help　government　to　formulate　targeted　countermeasures　to　decrease　the　severity　of　traffic　violations　by　specific　elderly　groups　and　improve　road　service　for　the　driving　population.