Transportation　mode　surveys　are　essential　resources　in　transportation　research.　The　survey　data　is　required　for　urban　traffic　planning.　We　want　to　be　able　to　realize　a　way　of　traffic　survey,　in　the　case　of　without　being　limited　by　the　condition,　we　can　master　everyone＇s　transportation　state　in　anytime　and　anywhere.　In　this　paper,　we　develop　a　transportation　mode　survey　application　for　the　Android　platform.　We　can　obtain　real-time　information　from　GPS　and　accelerometer　sensors　and　the　real-time　information　will　be　stored　in　SD　card　with　the　form　of　database.　We　describe　the　features　extracted　from　GPS　and　accelerometer　sensors　used　to　identify　transportation　modes　with　machine　learning　algorithms　of　decision　tree.　Experimental　results　show　that　the　classification　accuracy　of　the　algorithm　is　93.6782%.　Finally,　we　select　a　test　route　and　verify　the　reliability　of　the　transportation　mode　identification　system.　©　2014　IEEE.