Urban　roads　are　the　lifeline　of　urban　transportation　and　satisfy　the　commuting　and　travel　needs　of　citizens.　Following　the　acceleration　of　urbanization　and　the　frequent　extreme　weather　in　recent　years,　urban　waterlogging　is　occurring　more　than　usual　in　summer　and　has　negative　effects　on　the　urban　traffic　networks.　Extracting　flooded　roads　is　a　critical　procedure　for　improving　the　resistance　ability　of　roads　after　urban　waterlogging　occurs.　This　paper　proposes　a　flooded　road　extraction　method　to　extract　the　flooding　degree　and　the　time　at　which　roads　become　flooded　in　large　urban　areas　by　using　global　positioning　system　(GPS)　trajectory　points　with　driving　status　information　and　the　high　position　accuracy　of　vector　road　data　with　semantic　information.　This　method　uses　partition　statistics　to　create　density　grids　(grid　layer)　and　uses　map　matching　to　construct　a　time-series　of　GPS　trajectory　point　density　for　each　road　(vector　layer).　Finally,　the　fusion　of　grids　and　vector　layers　obtains　a　more　accurate　result.　The　experiment　uses　a　dataset　of　GPS　trajectory　points　and　vector　road　data　in　the　Wuchang　district,　which　proves　that　the　extraction　result　has　a　high　similarity　with　respect　to　the　flooded　roads　reported　in　the　news.　Additionally,　extracted　flooded　roads　that　were　not　reported　in　the　news　were　also　found.　Compared　with　the　traditional　methods　for　extracting　flooded　roads　and　areas,　such　as　rainfall　simulation　and　SAR　image-based　classification　in　urban　areas,　the　proposed　method　discovers　hidden　flooding　information　from　geospatial　big　data,　uploaded　at　no　cost　by　urban　taxis　and　remaining　stable　for　a　long　period　of　time.