In　order　to　reveal　the　internal　dynamic　property　of　short-term　traffic　flow,　the　nonlinear　analysis　method　is　used　to　identify　the　chaotic　property　of　traffic　flow　which　is　the　basis　for　the　prediction　of　the　traffic　flow　time　series.　Traffic　flow　time　series　is　reconstructed　in　phase-space　based　on　chaos　theory.　The　embedding　dimension　and　delay　time　are　first　calculated　via　the　C-C　method.　The　correlative　dimension　of　attractor　is　then　calculated　with　the　Grassberger-Procaccia　method.　The　largest　Lyapunov　exponent　of　traffic　flow　set　is　calculated　on　the　basis　of　the　improved　small　data　set　method　to　verify　the　presence　of　the　chaos　in　traffic　flow　time　series.　A　novel　multi-step　adaptive　prediction　method　is　proposed　to　solve　the　problem　of　adjusting　the　filter　parameters　of　the　chaos　local　adaptive　prediction　method　during　traffic　flow　multi-step　prediction.　The　traffic　flow　time　series　are　found　to　have　chaotic　properties　in　different　statistical　scales　of　2,　4,　and　5　min　and　show　that　the　improved　small　data　set　method　can　accurately　evaluate　the　chaotic　property　for　traffic　flow　time　series,　and　that　the　multi-step　adaptive　prediction　method　is　capable　of　effectively　predicting　its　fluctuation,　which　provides　a　useful　reference　for　traffic　guidance　and　control.