To　estimate　the　amount　of　emissions,　most　state-of-the-art　microscopic　emission　models,　such　as　VT-micro,　takes　the　individual　vehicle　speed　and　acceleration　as　the　model　input,　which　can　be　collected　efficiently　with　V2I　technology.　However,　there　is　a　gap　in　freeway　traffic　control　since　most　of　them　rely　on　the　macroscopic　traffic　model　and　omit　the　individual　vehicle　status.　To　fill　this　gap,　this　study　proposed　an　individual　vehicle　status　prediction　method　that　utilized　the　convolutional　neural　network　(CNN)　for　freeway　proactive　controls.　Then　the　overall　performance　of　the　road　network　in　multi-objective,　namely　mobility,　safety,　and　emissions,　will　be　evaluated　to　determine　the　optimal　control　signal.　The　proposed　CNN　enabled　individual　vehicle　status　prediction　method　reported　a　good　match　to　the　ground　truth　data　compared　with　the　support　vector　machine　and　artificial　neural　network.　Furthermore,　a　field　data-based　simulation　platform　was　established　to　implement　the　proposed　control　algorithm　with　the　CNN　prediction　network.　The　result　showed　that　the　multi-objective　performance　was　significantly　improved　compared　with　the　uncontrolled　case　and　achieved　further　optimization　of　multi-objective　compared　with　the　original　model.