Accurate　and　reliable　traffic　flow　data　are　essential　for　intelligent　transportation　systems;　however,　limitations　arising　from　hardware　and　communication　costs　often　lead　to　missing　data.　Tensor　decomposition　is　widely　used　to　address　these　issues.　However,　existing　imputation　methods　employ　a　fixed　geographic　feature　similarity　matrix　to　constrain　the　tensor　decomposition　process,　which　fails　to　accurately　capture　the　spatial　heterogeneity　of　traffic　flows,　thus　limiting　the　imputation　accuracy　and　robustness.　This　study　proposes　a　tensor　decomposition　method　embedded　with　geographic　meta-knowledge　(Meta-TD)　to　accurately　determine　the　spatial　heterogeneity　of　traffic　flows.　The　key　innovation　is　establishing　a　dynamic　relationship　between　the　geographic　meta-knowledge　and　spatial　heterogeneity　of　traffic　flows,　and　then　using　the　spatial　heterogeneity　of　the　traffic　flows　to　constrain　the　tensor　decomposition　process.　Experimental　results　based　on　real　urban　traffic　flows　demonstrated　the　superiority　of　Meta-TD　over　fifteen　baseline　models　under　random,　block,　and　long　time-series　missing　patterns,　achieving　reductions　in　MAE,　RMSE,　and　MAPE　of　6.97-97.05%,　3.33-94.68%,　and　0.72-90.89%,　respectively.　Notably,　Meta-TD　maintained　high　accuracy　for　sudden　changes　in　traffic　flow　states,　evidencing　its　robustness　to　varying　missing　data　rates　and　distribution　patterns.　This　adaptability　makes　it　highly　suitable　for　complex　and　dynamic　urban　traffic　environments.