Missing　data　is　an　inevitable　and　ubiquitous　problem　in　the　data-driven　Intelligent　Transportation　System　(ITS),　which　seriously　affects　the　accuracy　of　urban　traffic　planning　and　management.　Most　existing　traffic　data　processing　methods　often　only　exploit　the　characteristics　of　single　source　data.　In　this　paper,　we　present　a　novel　coupled　tensors　model　by　using　multi-source　traffic　data　for　missing　data　imputation,　and　propose　a　tensor　completion　algorithm　based　on　a　modified　CMTF-WOPT(Coupled　Matrix　and　Tensor　Factorization-Weighted　OP-Timization)　algorithm　to　recover　the　missing　traffic　data.　We　also　present　extensive　simulation　results　by　using　real　world　traffic　datasets　to　evaluate　the　performance　of　the　proposed　algorithm.　The　simulation　results　show　that　the　proposed　coupled　tensor　completion　algorithm　makes　a　significant　improvement　on　the　recovery　accuracy　compared　with　existing　tensor　completion　algorithms,　especially　under　high　missing　rates.