Accurate　short-time　traffic　flow　prediction　has　gained　gradually　increasing　importance　for　traffic　plan　and　management　with　the　deployment　of　intelligent　transportation　systems　(ITSs).　However,　the　existing　approaches　for　short-term　traffic　flow　prediction　are　unable　to　efficiently　capture　the　complex　nonlinearity　of　traffic　flow,　which　provide　unsatisfactory　prediction　accuracy.　In　this　paper,　we　propose　a　deep　learning　based　model　which　uses　hybrid　and　multiple-layer　architectures　to　automatically　extract　inherent　features　of　traffic　flow　data.　Firstly,　built　on　the　convolutional　neural　network　(CNN)　and　the　long　short-term　memory　(LSTM)　network,　we　develop　an　attention-based　Conv-LSTM　module　to　extract　the　spatial　and　short-term　temporal　features.　The　attention　mechanism　is　properly　designed　to　distinguish　the　importance　of　flow　sequences　at　different　times　by　automatically　assigning　different　weights.　Secondly,　to　further　explore　long-term　temporal　features,　we　propose　a　bidirectional　LSTM　(Bi-LSTM)　module　to　extract　daily　and　weekly　periodic　features　so　as　to　capture　variance　tendency　of　the　traffic　flow　from　both　previous　and　posterior　directions.　Finally,　extensive　experimental　results　are　presented　to　show　that　the　proposed　model　combining　the　attention　Conv-LSTM　and　Bi-LSTM　achieves　better　prediction　performance　compared　with　other　existing　approaches.