With　the　rapid　development　of　the　economy,　car　ownership　has　grown　rapidly,　which　causes　many　traffic　problems.　In　recent　years,　intelligent　transportation　systems　have　been　used　to　solve　various　traffic　problems.　To　achieve　effective　and　efficient　traffic　management,　intelligent　transportation　systems　need　a　large　amount　of　complete　traffic　data.　However,　the　current　traffic　data　collection　methods　result　in　different　forms　of　missing　data.　In　the　last　twenty　years,　although　many　approaches　have　been　proposed　to　impute　missing　data　based　on　different　mechanisms,　these　all　have　their　limitations,　which　leads　to　low　imputation　accuracy,　especially　when　the　collected　traffic　data　have　a　large　amount　of　missing　values.　To　this　end,　this　paper　proposes　a　latent-factor-model-based　approach　to　impute　the　missing　traffic　data.　In　the　proposed　approach,　the　spatial　information　of　the　road　network　is　first　combined　with　the　spatiotemporal　matrix　of　the　original　traffic　data.　Then,　the　latent-factor-model-based　algorithm　is　employed　to　impute　the　missing　data　in　the　combined　matrix　of　the　traffic　data.　Based　on　the　real　traffic　data　from　METR-LA,　we　found　that　the　imputation　accuracy　of　the　proposed　approach　was　better　than　that　of　most　of　the　current　traffic-data-imputation　approaches,　especially　when　the　original　traffic　data　are　limited.