In　order　to　solve　the　problem　of　environmental　governance　investment　planning　in　the　transportation　industry,　a　cost　prediction　model　is　proposed　under　technological　constraints,　where　the　input　output　indictors　emphasizes　the　flexibility　of　prediction　and　its　characters　are　asymmetric,　while　the　constructs　of　prediction　model　focuses　on　the　standardization　and　its　characters　are　symmetrical.　The　basic　principle　of　the　cost　prediction　model　is　based　on　an　extended　belief　rule-based　(EBRB)　system　to　model　the　input-output　relationship　in　investment　planning,　and　a　parameter　learning　model　to　improve　the　accuracy　of　the　EBRB　system.　Additionally,　the　technological　innovation　factors　are　also　embedded　in　the　cost　prediction　model　to　investigate　the　influence　of　technology-related　outcomes　on　investment　planning.　Finally,　based　on　the　data　of　environmental　governance　in　China＇s　transportation　industry　from　2003　to　2016,　the　cost　of　transportation　industry　environmental　management　in　China＇s　thirty　provinces　from　2017　to　2033　is　predicted　under　the　constraints　of　technological　innovation.　Results　show　that:　(1)　the　accuracy　of　the　proposed　cost　prediction　model　is　higher　than　some　existing　cost　prediction　methods;　(2)　the　predicted　environmental　governance　costs　have　a　significant　regional　difference;　(3)　the　upgrading　of　technological　innovation　is　conducive　to　saving　the　future　environmental　governance　costs　of　the　transportation　industry　in　some　provinces.　In　addition　to　the　above　results,　the　present　study　provides　model　supports　and　policy　references　for　government　decision　makers　in　transportation　industry-related　environmental　cost　planning.