Passenger　travel　demand　prediction　is　an　integral　part　of　intelligent　transportation　systems,　and　accurate　travel　demand　prediction　is　of　great　significance　for　vehicle　scheduling.　However,　existing　prediction　methods　are　unable　to　accurately　explore　its　potential　spatiotemporal　correlation　and　mostly　ignore　the　impact　of　historical　inflow　on　travel　demand.　In　order　to　further　exploit　the　spatiotemporal　characteristics　of　spatiotemporal　big　data　and　improve　the　accuracy　of　the　model　in　predicting　passenger　travel　demand,　this　paper　proposes　a　Conv-LSTM　Attention　BiLSTM　(CLAB)　model　for　short-time　prediction　of　passenger　rental　travel　demand.　The　attention-based　Conv-LSTM　module　extracts　spatial　features　and　short-term　temporal　features　of　passenger　travel　demand　at　the　near　moment,　where　the　attention　mechanism　automatically　assigns　different　weights　to　discriminate　the　importance　of　demand　sequences　at　different　times.　To　explore　long-term　temporal　features,　two　BiLSTM　modules　are　used　to　extract　temporal　features　of　historical　inflow　sequences　and　temporal　features　of　daily　passenger　temporal　features　of　the　demand　series.　Experiments　are　conducted　using　the　order　data　of　online　and　cruising　taxis　on　Xiamen　Island,　and　the　results　show　that:　(1)　the　CLAB　model　is　more　suitable　for　predicting　the　future　5-min　short-time　passenger　travel　demand　using　30-min　historical　data;　(2)　the　overall　effect　error　of　the　CLAB　model　is　lower　and　has　better　prediction　results　compared　with　the　benchmark　prediction　model.　The　CLAB　model　is　more　effective　than　the　CNN-LSTM,　LSTM,　BiLSTM,　CNN,　and　ConvLSTM　by　33.179%,　33.153%,　33.204%,　5.401%,　and　5.914%　in　mean　absolute　error　(MAE)　and　34.389%,　34.423%,　34.524%,　6.772%,　and　6.669%　in　Root　Mean　Square　Error　(RMSE),　respectively;　(3)　the　CLAB　model　performs　better　for　weekday　prediction　with　higher　regularity　than　non-working　day　prediction,　with　best　prediction　for　weekday　morning　peaks.　©　2023　Journal　of　Geo-Information　Science.　All　rights　reserved.