To　address　the　challenges　of　obtaining　demand　for　air-rail　passenger　flows　within　urban　agglomerations　and　understanding　passenger　flow　patterns,　a　comprehensive　analysis　framework　integrating　air-railway　passenger　flow　identification　and　prediction　modules　is　proposed.　Firstly,　considering　the　spatial　constraints　of　transportation　hubs　and　the　spatiotemporal　characteristics　of　travel,　a　method　for　identifying　intermodal　passenger　flows　using　signaling　data　is　introduced,　accompanied　by　an　analysis　of　distribution　patterns.　Subsequently,　leveraging　the　Bidirectional　Gated　Recurrent　Unit　(BiGRU),　time　period　coding　is　integrated　to　construct　a　Temporal-Bidirectional　Gated　Recurrent　Unit　(T-BiGRU)　for　passenger　flow　prediction.　Finally,　the　framework　is　validated　using　the　Beijing-Tianjin-Hebei　urban　agglomeration　as　a　case　study.　Results　indicate　that　the　intermodal　passenger　flow　in　the　Beijing-Tianjin-Hebei　urban　agglomeration　exhibits　a　clustered　distribution,　with　the　scenarios　of　Beijing　South　Railway　Station-Tianjin　Railway　Station-Tianjin　Binhai　Airport　and　Beijing　West　Railway　Station-Zhengding　Airport　Station-Shijiazhuang　Zhengding　Airport　having　the　highest　proportion,　exceeding　65%　of　the　total　intermodal　passenger　flow.　The　T-BiGRU　model　accurately　predicts　the　demand　for　air-rail　intermodal　passenger　flows.　The　bidirectional　passenger　flow　prediction　accuracy　for　the　two　main　scenarios　exceeds　89%,　surpassing　multiple　baseline　models.　These　findings　offer　support　for　the　coordinated　development　of　air-rail　transportation　and　the　optimization　of　air-rail　intermodal　services　in　urban　agglomerations.　©　2024　Journal　Northern　Jiaotong　University.　All　rights　reserved.