Imminent　Throughput　(ITP),　the　number　of　vehicles　in　a　movement　that　can　pass　through　an　intersection　given　a　unit　of　effective　green　time,　serves　as　a　crucial　control　input　in　many　real-time　optimization-based　control　schemes.　The　accuracy　of　ITP　prediction　can　significantly　influence　control　performance.　If　a　movement　with　a　green　signal　cannot　discharge　as　many　vehicles　as　predicted　by　the　controller,　its　performance　may　be　significantly　reduced.　However,　most　existing　studies　have　focused　on　the　design　of　control　schemes　while　neglecting　the　importance　of　precise　ITP　prediction.　These　studies　either　assume　that　ITP　can　be　accurately　predicted　or　use　traditional　indices　(e.g.,　saturation　flow　rate)　or　heuristic　methods　to　predict　ITP,　resulting　in　relatively　low　accuracy.　This　paper　proposes　the　use　of　a　Deep　Neural　Network　(DNN)　to　predict　ITP　and　demonstrates　that　the　DNN　with　Multiple　Classifications　(NN-C)　models　can　predict　ITP　with　higher　accuracy,　lower　mean　absolute　error,　and　lower　root　mean　squared　error　than　other　prediction　methods　(regression,　decision　tree,　and　heuristic　methods).　Experiments　also　show　that　control　performance　can　be　improved　with　more　accurate　ITP　predictions　using　the　NN-C.　©　2023　IEEE.