The　main　goal　of　this　study　is　to　investigate　the　unobserved　heterogeneity　in　VRU-MV　crash　data　and　to　determine　the　relatively　important　contributing　factors　of　injury　severity.　For　this　end,　a　latent　class　analysis　(LCA)　coupled　with　random　parameters　logit　model　(LCA-RPL)　is　developed　to　segment　the　VRU-MV　crashes　into　relatively　homogeneous　clusters　and　to　explore　the　differences　among　clusters.　The　random-forest-based　SHapley　Additive　exPlanation　(RF-SHAP)　approach　is　used　to　explore　the　relative　importance　of　the　contributing　factors　for　injury　severity　in　each　cluster.　The　results　show　that,　vulnerable　group　(VG),　intersection　or　not　(ION)　and　road　type　(RT)　clearly　distinguish　the　crash　clusters.　Moto-vehicle　type　and　functional　zone　have　significant　impact　on　the　injury　severity　among　all　clusters.　Several　variables　(e.g.　ION,　crash　type　[CT],　season　and　RT)　demonstrate　a　significant　effect　in　a　specific　sub-cluster　model.　Results　of　this　study　provide　specific　and　insightful　countermeasures　that　target　the　contributing　factors　in　each　cluster　for　mitigating　VRU-MV　crash　injury　severity.