Intelligent　transportation　system　(ITS)　contributes　to　allocate　transportation　resources,　from　citywide　ones　to　nationwide,　more　efficiently　with　the　help　of　algorithms.　Due　to　the　fact　that　the　ITS　is　a　quite　comprehensive　field,　it　is　necessary　for　researchers　to　have　a　better　understanding　of　dominant　methods　and　which　one　is　proper　for　the　targeted　subjects　of　ITS.　More　importantly,　with　a　knowledge　of　the　remained　challenges　in　developing　ITS　and　relevant　techniques,　researchers　may　have　a　clearer　direction　to　work　on.　To　provide　researchers　with　dedicated　information　on　specific　machine　learning　(ML)　techniques　used　in　object　recognition　and　traffic　prediction,　two　essential　study　subjects　in　ITS,　this　paper　mainly　focuses　on　deep　learning　and　neural　network　(NN),　one　of　widely-used　ML　algorithms,　and　aims　to　conduct　a　brief　review　on　its　recent　applications　in　ITS,　as　well　as　to　mine　its　potential　usage.　As　a　result,　this　review　introduces　some　popular　NN,　convolutional　neural　network　(CNN),　long　short-term　memory　(LSTM)　network,　gated　recurrent　unit　(GRU)　network,　and　their　hybrid　mechanism,　first.　Then　their　applications　and　performance　in　ITS　are　described.　Finally,　this　paper　discusses　constraints　on　some　of　them　and　suggests　some　promising　research　directions.　©　2021,　The　Editor(s)　(if　applicable)　and　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.