Compared　with　traditional　traffic　detection　data,　floating　vehicle　trajectory　data　have　the　characteristics　of　wide　coverage,　low　cost,　and　high　mobility,　which　have　been　widely　used　in　urban　traffic　status　recognition　and　have　gradually　become　one　of　the　main　data　sources　for　urban　traffic　state　recognition.　However,　most　of　the　existing　traffic　state　recognition　based　on　floating　vehicle　trajectory　data　is　based　on　high-precision　or　multi-source　trajectory　data.　To　address　the　problem　of　low　accuracy　of　sparse　trajectory　data　in　urban　traffic　state　recognition,　this　paper　proposes　a　dynamic　traffic　state　classification　method　combining　Davies-Bouldin　Index　(DBI)　and　trajectory　similarity　metric　to　finely　classify　urban　road　traffic　state,　and　then　realizes　the　spatial　and　temporal　characteristics　of　urban　traffic　state.　Firstly,　we　pre-process　the　trajectory　data　and　road　network　data,　including　data　cleaning,　map　matching,　and　format　conversion,　record　the　relative　spatial　distances　between　trajectory　points　and　matching　road　sections,　and　build　a　set　of　trajectories　of　road　sections　in　different　time　slices.　Secondly,　we　dynamically　extend　the　spatial　dimension　of　trajectories　by　using　Davies-Bouldin　Index　(DBI)　according　to　the　trajectory　speed-spatial　similarity,　and　construct　the　best　vehicle　queue　according　to　the　trajectory　similarity　measure.　After　that,　the　different　vehicle　queues　before　and　after　are　processed　twice,　and　the　vehicle　queues　are　merged　according　to　the　rules　and　connected　to　form　traffic　flow　clusters,　so　as　to　achieve　the　purpose　of　dividing　local　road　sections　and　laying　the　foundation　for　subsequent　recognition　of　local　traffic　states.　Finally,　the　global　trajectory　points　are　clustered　based　on　the　fuzzy　C-means　clustering　method　to　divide　traffic　states,　and　the　speed　bounds　of　different　traffic　states　are　obtained　and　compared　with　the　previous　traffic　flow　cluster　speed　The　comparison　is　carried　out　to　realize　the　local　road　traffic　state　identification,　and　then　realize　the　fine　analysis　of　traffic　state.　The　real　cab　trajectory　data　at　the　intersection　sections　of　Xiamen　Xiahe　Road,　Hubin　West　Road,　and　Hubin　South　Road　are　used　for　testing.　The　results　show　that　the　traffic　speed　map　calculated　by　the　trajectory　similarity　metric　method　can　reflect　the　changes　of　traffic　speed　on　the　road　section　more　clearly,　and　the　method　ensures　that　the　vehicle　queuing　speed　distribution　is　basically　consistent　with　the　original　trajectory　speed　distribution.　Compared　with　the　comparison　methods,　i.e.,　Kmeans++　and　ST-DBSCAN,　the　root-mean-square　error　of　the　proposed　method　decreases　by　18.77%　and　21.22%　on　average,　and　it　performs　more　stable　and　robust　in　different　experimental　road　sections.　It　can　effectively　and　reliably　use　sparse　trajectory　data　to　identify　urban　traffic　states,　and　then　realize　the　fine　analysis　of　urban　traffic　states,　which　provides　auxiliary　decisions　for　the　management　of　urban　road　traffic　problems.　©　2022,　Science　Press.　All　right　reserved.