The　accurate　measurement　of　road　traffic　states　can　provide　decision　making　for　travelers　and　traffic　managers.　In　this　paper,　an　algorithm　based　on　Kernel　k-nearest　neighbors　(Kernel-KNN)　matching　of　regional　traffic　attractors　is　presented　to　estimate　road　traffic　states.　First,　the　road　traffic　running　states　are　divided　into　several　different　modes.　The　concept　of　the　regional　traffic　attractors　of　the　target　link　is　put　forward　for　effective　matching.　The　representative　road　traffic　state　data　are　extracted　to　establish　the　reference　sequences　of　road　traffic　running　characteristics　(RSRTRC).　Then　the　sequence　of　regional　traffic　attractors　is　selected　and　its　kernel　function　is　constructed,　with　which　the　regional　traffic　attractors　can　be　mapped　into　a　high　dimensional　feature　space.　The　reference　and　current　sequences　of　regional　traffic　attractors　are　extracted　and　the　Euclidean　distances　in　the　feature　space　between　them　are　obtained.　Finally,　the　road　traffic　states　are　estimated　from　weighted　averages　of　the　selected　k　road　traffic　states,　which　correspond　to　the　k　smallest　Euclidean　distances.　Several　typical　links　in　Beijing　are　adopted　for　case　studies.　The　final　results　of　the　experiments　are　presented,　which　prove　that　this　road　traffic　state　measurement　approach　based　on　Kernel-KNN　matching　of　regional　traffic　attractors　is　feasible　and　can　achieve　a　high　accuracy.　(C)　2016　Elsevier　Ltd.　All　rights　reserved.