Passenger　flow　prediction　is　of　great　significance　to　bus　scheduling　and　route　optimization.　In　this　paper,　a　novel　algorithm,　namely,　Bi-directional　Long　Short-Term　Memory　with　Attention　Mechanism　(Bi-LSTM-AT)　is　proposed　to　predict　transit　passenger　flow.　We　utilize　the　Bi-LSTM　structure　with　attention　mechanism　to　capture　the　spatiotemporal　features,　meanwhile,　taking　into　account　external　factors　that　affect　passenger　choices.　We　conducted　an　experiment　using　field　data　collected　at　Urumqi,　China.　The　prediction　results　show　an　averaged　absolute　error　(MAE)　as　low　as　5.75,　which　demonstrated　the　feasibility　of　applying　Bi-LSTM-AT　in　transit　passenger　flow　forecasting.　©　2021　CICTP　2021:　Advanced　Transportation,　Enhanced　Connection　-　Proceedings　of　the　21st　COTA　International　Conference　of　Transportation　Professionals.　All　rights　reserved.