Short-term　traffic　flow　prediction　is　one　of　the　most　important　issues　in　the　field　of　intelligent　transportation　systems.　It　plays　an　important　role　in　traffic　information　service　and　traffic　guidance.　However,　complex　traffic　systems　are　highly　nonlinear　and　stochastic,　making　short-term　traffic　flow　prediction　a　challenging　issue.　Although　long　short-term　memory　(LSTM)　has　a　good　performance　in　traffic　flow　prediction,　the　impact　of　temporal　features　on　prediction　has　not　been　exploited　by　existing　studies.　In　this　paper,　a　temporal　information　enhancing　LSTM　(T-LSTM)　is　proposed　to　predict　traffic　flow　of　a　single　road　section.　In　view　of　the　similar　characteristics　of　traffic　flow　at　the　same　time　each　day,　the　model　can　improve　prediction　accuracy　by　capturing　the　intrinsic　correlation　between　traffic　flow　and　temporal　information.　The　experimental　results　demonstrate　that　our　method　can　effectively　improve　the　prediction　performance　and　obtain　higher　accuracy　compared　with　other　state-of-the-art　methods.　Furthermore,　we　propose　a　novel　missing　data　processing　technique　based　on　T-LSTM.　According　to　the　experimental　results,　this　technique　can　well　restore　the　characteristics　of　original　data　and　improve　the　accuracy　of　traffic　flow　prediction.