Missing　traffic　data　collected　by　IoT　sensors　is　a　common　issue.　Having　complete　traffic　data　can　help　people　with　their　studies　and　work　in　real　world.　A　spatio-temporal　enhanced　k　nearest　neighbor　(ST-KNN)　method　is　proposed　in　this　paper　to　interpolate　missing　traffic　data　according　to　its　corresponding　spatio-temporal　dependence.　The　proposed　method　is　improved　in　three　aspects:　initially,　localized　data　are　involved　in　the　computation,　the　distance　metric　formula　is　re-designed　secondly,　and　the　data　regression　model　is　improved.　We　conducted　our　experimental　evaluations　on　publicly　available　real　dataset,　and　the　results　are　compared　to　those　from　existing　state-of-the-art　models.　The　results　of　our　experiments　show　that　the　method　proposed　in　this　paper　can　effectively　improve　traffic　data　interpolation　accuracy.