Event　cameras　have　a　wide　range　of　applications　in　the　field　of　traffic　flow　detection　owing　to　their　ability　to　accurately　identify　moving　targets　while　being　insensitive　to　stationary　targets.　In　this　study,　an　event　camera　is　installed　in　a　roadside　scenario　to　collect　the　point　cloud　data　of　moving　targets.　For　traffic　identification,　the　geometrical,　quantitative　and　Gaussian　projection　characteristics　of　the　point　clouds　for　motor/non-motor　vehicles　and　pedestrians　are　extracted,　and　their　feature　distributions　are　analyzed.　Furthermore,　to　address　the　problem　of　shadow　noise　caused　by　sunlight,　a　shadow　removal　method　based　on　feature　similarity　is　proposed　considering　the　point　cloud　distribution　characteristics.　Experimental　results　show　that　there　are　significant　differences　in　the　length-width　ratio,　pixel　points,　horizontal　and　vertical　projection　features　among　different　traffic　targets　specified　in　vehicles,　non-motor　vehicles　and　pedestrians.　In　addition,　the　proposed　shadow　removal　scheme　demonstrates　high　accuracy　of　96.5%,　and　the　standard　deviation　is　1.3%.