Intelligent　Transportation　Systems　(ITS)　plays　a　significant　role　in　the　traffic　management,　i.e.　traffic　jam　prediction,　route　guidance.　Due　to　the　hardware　failure　or　data　transformation　failure,　some　traffic　observation　data　may　be　occasionally　missed,　which　seriously　affect　intelligent　transportation　information　service.　So,　the　completion　of　traffic　observation　data　has　now　become　an　issue　that　requires　to　be　concerned　and　solved.　By　analyzing　the　traffic　history　data,　we　find　that　traffic　data　tend　to　have　strong　spatio-temporal　correlation.　Considering　this　feature,　we　propose　a　new　low-rank　representation　based　traffic　data　completion　method.　To　further　enhance　the　local　correlation,　we　introduce　an　ordered　regulation　into　our　proposed　method.　We　also　give　an　efficient　solution　to　our　proposed　methods.　In　order　to　verify　the　performance　of　our　methods,　some　traffic　data　completion　experiments　are　conducted　on　the　Beijing　metropolitan　road　speed　dataset　and　the　capital　airport　highway　microwave　dataset.　Experimental　results　show　that　the　proposed　methods　are　superior　to　other　state-of-the-art　traffic　data　completion　methods.　©　2016　IEEE.