Motorcyclists　are　an　integral　component　of　the　traffic　stream　especially,　in　low-income　developing　countries　like　Pakistan.　However,　motorcyclists　lay　in　the　group　of　vulnerable　road　users　i.e.,　road　users　with　the　least　protection,　along　with　pedestrians　and　cyclists.　Therefore,　the　current　study　employed　the　random　parameters　logit　model　to　identify　key　risk　factors　associated　with　motorcyclist　injury　severity　using　three　years　crash　data　(2017-2019)　for　city　of　Rawalpindi,　Pakistan.　To　calibrate　the　model,　motorcyclist　injury　severity　thresholds　are　classified　as　no　injury,　minor　injury,　severe　injury,　and　fatal　injury.　For　motorcyclist　injury　severity　analysis,　the　effects　of　vehicle　crash　characteristics,　weather　conditions,　rider　attributes　and　other　socio-demographic　considerations　were　primarily　considered.　The　study　results　showed　that　severe　and　fatal　injury　risk　is　increased　for　crashes　occurred　during　weekdays,　involving　riders　aged　above　50　years,　involving　the　collision　of　motorcycles　with　passenger　car　and　heavy　vehicles,　involving　a　female　as　a　pillion　rider,　and　those　that　occurred　due　to　over　speeding.　Based　on　the　results　obtained　from　the　model,　the　study　suggests　several　policy　implication　as　strict　implementation　of　traffic　regulations　such　as　heavy　fines　or　cancellation　of　driving　license　on　over　speeding,　wrong　turns,　inappropriate　passing,　running　a　red　light,　not　wearing　helmet　etc.,　instructing　the　females　not　to　wear　loose　clothes　or　trailing　scarves　while　riding　the　bike　as　pillion　riders.　The　outcomes　are　expected　to　stimulate　more　interest　and　discussion　regarding　motorcycle　safety　in　the　country　and　can　be　used　by　city　traffic　police　and　highway　authorities　to　boost　the　road　safety.