Facing　the　currently　large　quantity　of　intelligent　transportation　data,　missing　ones　is　often　inevitable.　Some　previous　works　have　shown　the　advantages　of　tensor　decomposition-based　approaches　in　solving　multidimensional　data　imputation　problems.　However,　a　research　gap　still　exists　in　examining　the　effect　of　applying　these　methods　on　imputation　performance　and　their　application　to　accident　detection.　Thus,　referring　to　a　two-month　spatiotemporal　traffic　speed　dataset,　collected　on　the　national　trunk　highway　in　Shandong,　China,　this　paper　employs　the　Bayesian　Gaussian　CANDECOMP/PARAFAC　(BGCP)　to　impute　missing　speed　data　in　different　missing　rates　and　missing　scenarios.　Moreover,　the　dataset　is　built　while　considering　both　the　temporal　and　the　road　functions.　Applying　the　generated　results　of　data　imputation　in　accident　detection　is　also　of　the　main　targets　of　this　work.　Thus,　while　combining　multiple　sources　of　data,　such　as　traffic　operation　status　and　weather,　eXtreme　Gradient　Boosting　(XGBoost)　is　deployed　to　build　accident　detection　models.　The　generated　results　show　that　the　BGCP　model　can　produce　accurate　imputations　even　under　temporally　correlated　data　corruption.　Added　to　that,　it　is　also　suggested　that,　when　there　are　continuous　periods　of　missing　speed　data　(missing　rate　greater　than　10%),　pre-processing　of　data　imputation　is　imperative　to　maintain　the　accuracy　of　accident　detection.　Thus,　the　objective　of　this　work　is　to　provide　insights　into　traffic　management　and　academics　when　performing　spatiotemporal　data　imputation　tasks.