The　energy-saving　low-frequency　floating　car　data　has　increasingly　attracted　significantly　attention　in　traffic　management,　but　the　low-frequency　GPS　data　has　adverse　effects　on　map-matching　in　urban　road　networks.　Therefore,　this　paper　develops　a　new　spatial-temporal　path　filter-based　and　route　choice　model　aided　map-matching　(FCMM)　algorithm　that　enhances　the　map-matching　of　low-frequency　positioning　data　on　an　urban　road　map.　We　introduce　filters　based　on　spatial　and　temporal　analyses,　which　consider　real-time　traffic　status　to　eliminate　unreasonable　paths　between　adjacent　candidate　points,　calculate　the　heuristic　information,　and　establish　the　candidate　graph.　Considering　the　driver　path　preference,　we　further　use　the　route　choice　model　estimated　from　from　real　drive　data　to　assess　each　candidate　path.　The　algorithm　was　evaluated　using　ground　truth　data,　and　the　results　of　the　experiment　show　that　the　proposed　FCMM　algorithm　outperforms　the　baseline　methods　in　both　effectiveness　and　efficiency　under　the　condition　of　low　sampling　rates.　©　2021　CICTP　2021:　Advanced　Transportation,　Enhanced　Connection　-　Proceedings　of　the　21st　COTA　International　Conference　of　Transportation　Professionals.　All　rights　reserved.