Defining　traffic　analysis　zone　(TAZ)　based　on　public　transit　corridor　provides　a　demand　analysis　and　forecast　basis　for　the　bus　line　optimizations　in　the　corridor　which　also　accounts　for　bus　passenger　characteristics　for　analysis.　This　paper　proposed　a　hierarchical　division　method　for　public　transit　corridors,　which　divided　areas　along　the　corridor　as　directly　and　indirectly　influenced　TAZs.　Considering　different　requirements　for　the　results＇　accuracy　in　the　directly　and　indirectly　influenced　TAZs,　this　paper　selected　clustering　indicators　that　are　more　suitable　for　the　TAZ　division　in　public　transit　corridors　based　on　big　data.　It　then　proposed　the　different　clustering　methods　for　the　directly　and　indirectly　influenced　TAZs.　The　paper　also　introduced　the　clustering　factor　which　can　initially　determine　the　number　of　clusters　and　the　center　of　the　traffic　area.　To　illustrate　the　applicability　of　the　proposed　method,　this　paper　presented　a　case　study　using　the　big　data　from　the　public　transit　corridor　of　Guangqu　Road　in　Beijing,　China.　The　results　indicate　the　TAZ　division　based　on　public　transit　performs　better　than　the　traditional　method.　Copyright　©　2020　by　Science　Press.