Data　missing　remains　a　difficult　and　important　problem　in　the　transportation　information　system,　which　seriously　restricts　the　application　of　the　intelligent　transportation　system　(ITS),　dominatingly　on　traffic　monitoring,　e.g.,　traffic　data　collection,　traffic　state　estimation,　and　traffic　control.　Numerous　traffic　data　imputation　methods　had　been　proposed　in　the　last　decade.　However,　lacking　of　sufficient　temporal　variation　characteristic　analysis　as　well　as　spatial　correlation　measurements　leads　to　limited　completion　precision,　and　poses　a　major　challenge　for　an　ITS.　Leveraging　the　low-rank　nature　and　the　spatial-temporal　correlation　of　traffic　network　data,　this　paper　proposes　a　novel　approach　to　reconstruct　the　missing　traffic　data　based　on　low-rank　matrix　factorization,　which　elaborates　the　potential　implications　of　the　traffic　matrix　by　decomposed　factor　matrices.　To　further　exploit　the　temporal　evolvement　characteristics　and　the　spatial　similarity　of　road　links,　we　design　a　time-series　constraint　and　an　adaptive　Laplacian　regularization　spatial　constraint　to　explore　the　local　relationship　with　road　links.　The　experimental　results　on　six　real-world　traffic　data　sets　show　that　our　approach　outperforms　the　other　methods　and　can　successfully　reconstruct　the　road　traffic　data　precisely　for　various　structural　loss　modes.