Intelligent　Transportation　Systems　(ITS)　research　and　applications　benefit　from　accurate　short-term　traffic　state　forecasting.　To　improve　the　forecasting　accuracy,　this　paper　proposes　a　deep　learning　based　multitask　learning　Gated　Recurrent　Units　(MTL-GRU)　with　residual　mappings.　To　enhance　the　performance　of　the　MTL-GRU,　feature　engineering　is　introduced　to　select　the　most　informative　features　for　the　forecasting.　Then,　based　on　real-world　datasets,　numerical　results　show　that　the　MTL-GRU　can　well　estimate　traffic　flow　and　speed　simultaneously,　and　performs　better　than　other　counterparts.　Experiments　also　show　that　the　deep　learning　based　MTL-GRU　model　can　overpower　the　bottleneck　caused　by　enlarging　training　datasets　and　continue　to　gain　benefits.　The　results　suggest　the　proposed　MTL-GRU　model　with　residual　mappings　is　promising　to　forecast　short-term　traffic　state.　©　2013　IEEE.