Monitoring　and　forecasting　of　urban　traffic　operation　is　an　important　task　to　grasp　the　characteristics　of　traffic　operation　changes　and　formulate　strategies　to　alleviate　traffic　congestion.　The　forecasting　results　can　provide　effective　road　information　for　the　public,　and　also　provide　support　for　the　formulation　of　policy　measures　and　the　evaluation　of　the　effect.　Different　from　the　traditional　short-term　traffic　forecasting,　the　forecasting　model　proposed　in　this　paper　is　not　for　the　operation　state　prediction　of　adjacent　periods,　but　for　the　long-span,　for　the　daily　traffic　operation　state　prediction.　This　paper　constructs　a　multidimensional　factors　set　including　time　period,　weather,　holidays,　vehicle　restriction,　large　events　and　so　on;　builds　a　data　training　set　based　on　long-term　historical　traffic　index,　and　proposes　a　daily　road　network　condition　prediction　model　based　on　gradient　boosting　decision　tree;　validates　the　model　by　using　the　optimal　model.　The　results　show　that　the　prediction　accuracy　of　the　model　can　reach　more　than　90%.　The　comparative　analysis　of　the　regression　model　also　shows　that　the　model　presented　in　this　paper　performs　best　in　all　scoring　items,　indicating　that　it　is　more　suitable　for　regression　analysis　with　large　samples　and　multiple　factors.　The　daily　prediction　model　proposed　in　this　paper　has　important　application　value　in　improving　the　quality　of　urban　road　network　operation　and　alleviating　traffic　congestion.　Copyright　©　2019　by　Science　Press.