The　growing　use　of　probe　vehicles　generates　a　huge　number　of　global　navigation　satellite　systems　(GNSS)　data.　Limited　by　satellite　positioning　technology,　further　improving　the　accuracy　of　map　matching　(MM)　is　challenging　work,　especially　for　low-frequency　trajectories.　When　matching　a　trajectory,　the　ego　vehicle＇s　spatial-temporal　information　of　the　present　trip　is　most　useful　with　the　least　amount　of　data.　In　addition,　there　is　a　large　number　of　other　data,　e.g.,　other　vehicles＇　state　and　past　prediction　results,　but　it　is　hard　to　extract　useful　information　for　matching　maps　and　inferring　paths.　Most　of　the　MM　studies　have　used　only　the　ego　vehicle＇s　data　and　ignored　other　vehicles＇　data.　Based　on　those,　this　article　designs　a　new　MM　method　to　make　full　use　of　＂big　data.＂　We　first　sort　all　the　data　into　four　groups　according　to　their　spatial　and　temporal　distance　from　the　present　matching　probe,　which　allows　us　to　sort　for　their　usefulness.　Then　we　design　three　different　methods　to　extract　valuable　information　(scores)　from　them:　a　score　for　speed　and　bearing,　one　for　historical　usage,　and　another　for　traffic　state　using　a　spectral　graph　Markov　neural　network.　Finally,　we　use　a　modified　top-K　shortest-path　method　to　search　the　candidate　paths　within　an　ellipse　region　and　then　use　the　fused　score　to　infer　the　path　(projected　location).　We　test　the　proposed　method　against　baseline　algorithms　using　a　real-world　dataset　in　China.　The　results　show　that　all　scoring　methods　can　enhance　MM　accuracy.　Furthermore,　our　method　outperforms　the　others,　especially　when　the　GNSS　probing　frequency　is　＜=　0.01Hz.