Traffic　flow　prediction　is　a　very　important　and　classic　problem　in　intelligent　transportation　that　has　received　considerable　attention　from　scholars.　Previous　studies　on　traffic　flow　prediction　have　primarily　relied　on　taxi　data.　However,　while　such　studies　have　focused　on　taxi　traffic　flow　information,　they　have　often　overlooked　the　importance　of　passenger　data.　Incorporating　taxi　passenger　information　can　help　uncover　key　traffic　flow　characteristics,　such　as　road　traffic　conditions　and　local　road　resource　demand.　To　this　end,　this　paper　proposes　a　CNN-BIGRU　model　that　leverages　both　taxi　passenger　and　taxi　flow　information　to　achieve　more　accurate　traffic　flow　predictions.　The　proposed　model　transforms　the　taxi　flow　and　passenger　data　into　a　feature　matrix,　which　is　then　fed　into　the　model.　Specifically,　a　convolutional　neural　network　(CNN)　is　utilized　to　mine　the　spatial　information　of　traffic　flow.　The　extracted　spatial　information　is　then　input　into　a　bidirectional　gated　recurrent　unit　network　(BiGRU)　to　capture　the　data＇s　time　sequence　relationships.　Furthermore,　the　extracted　time　and　space　features　are　integrated　to　achieve　the　goal　of　accurately　predicting　short-term　traffic　flow.　Experimental　results　reveal　that　our　proposed　model　outperforms　competing　models　on　real-world　datasets,　demonstrating　the　efficacy　and　superiority　of　combining　passenger　data　into　traffic　flow　forecast　analyses.　©　2023　IEEE.