This　paper　proposes　a　method　to　quantify　the　packing　function　of　particles　in　packed　aggregate　blend.　Three　indicators　are　defined　to　capture　the　composition　of　aggregate　blend　and　the　packing　function　of　particles　in　packed　aggregate　blend,　which　are　the　percentage　of　contribution　to　the　packing　volume　(PCPV),　the　percentage　of　contribution　to　the　bulk　volume　(PCBV),　and　the　relative　change　between　the　contribution　of　particles　to　the　packing　volume　and　that　to　the　bulk　volume　(RCP-13).　Packing　strategies　to　quantify　PCPV　and　Rc-3　are　developed　based　on　the　loose　filling　test,　the　dry-rodded　test　and　the　Superpave　gyratory　compactor　(SGC)　test.　Two　typical　aggregates,　one　with　good　angularity　(denoted　as　crushed　stone)　and　the　other　with　poor　angularity　(denoted　as　gravel),　are　selected　for　investigating　the　proposed　method　with　3　stone　matrix　asphalt　(SMA)　gradations　arid　3　asphalt　concrete　(AC)　gradations.　The　test　results　show　that　the　PCPV　provides　a　new　way　to　understand　the　composition　of　aggregate　blend.　The　RC,.78　can　be　employed　to　quantitatively　investigate　the　packing　functions　of　the　particles　in　packed　aggregate　blend.　The　zero　of　RC,y3　represents　the　balance　point　between　air　voids　filling　function　and　skeleton　building　function.　A　positive　RC.,713　means　more　skeleton　building　function.　And　the　bigger　RCP-B　the　stronger　the　skeleton　building　function.　A　negative　RCsy　means　major　air　voids　filling　function.　And　the　smaller　RCP-8　the　more　significant　the　air　voids　filling　function.　The　balance　size　for　the　SMA　gradation　with　the　nominal　maximum　particle　size　(NMPS)　of　16　mm　is　2.36　mm,　which　is　also　considered　as　the　balance　size　for　the　reference　upper　limit　and　a　designed　gradations　of　AC　with　the　NMPS　of　26.5.　The　balance　size　for　the　reference　gradation　lower　limit　gradation　of　AC　is　bigger　than　2.36　mm　but　smaller　than　4.75　mm.　The　compact　energy　of　the　filling　test　and　the　particle　morphology　have　no　impacts　on　the　PCPV　and　RC＇s-P,-B　but　significant　effects　on　the　air　void　content.　The　SGC　test　is　recommended　to　quantify　the　indicators.　(C)　2018　Elsevier　Ltd.　All　rights　reserved.