In　this　article,　a　method　of　traffic　state　prediction　at　intersection　based　video　data　is　proposed.　The　method　inherits　the　basic　assumption　of　modified　cell　transmission　model　(MCTM)　and　depends　on　back　propagation　neural　network　(BPNN).　The　training　set　of　the　neural　network　consists　of　traffic　data　from　the　video.　In　order　to　verify　the　good　generalization　of　the　prediction　method,　novel　data　is　used　as　the　verification　set.　The　experimental　results　exhibit　that　the　model　has　virtuous　generalization.　Especially,　the　model　is　suitable　for　short-term　traffic　prediction　at　intersections.　The　prediction　results　of　the　method　serve　to　construct　the　traffic　state　prediction　model　(TSPM)　of　the　urban　traffic　network.　Moreover,　making　route　arrangement　and　traffic　guidance　strategy　also　require　them.　©　2020　IEEE.