At　present,　high　road　surface　temperature　(RST)　is　threatening　the　safety　of　expressway　transportation.　It　can　lead　to　accidents　and　damages　to　road,　accordingly,　people　have　paid　more　attention　to　RST　forecasting.　Numerical　methods　on　RST　prediction　are　often　hard　to　obtain　precise　parameters,　whereas　statistical　methods　cannot　achieve　desired　accuracy.　To　address　these　problems,　this　paper　proposes　GBELM-RBF　method　that　utilizes　gradient　boosting　to　ensemble　Radial　Basis　Function　Extreme　Learning　Machine.　To　evaluate　the　performance　of　the　proposed　method,　GBELM-RBF　is　compared　with　other　ELM　algorithms　on　the　datasets　of　airport　expressway　and　Badaling　expressway　during　November　2012　and　September　2014.　The　root　mean　squared　error　(RMSE),　accuracy　and　Pearson　Correlation　Coefficient　(PCC)　of　these　methods　are　analyzed.　The　experimental　results　show　that　GBELM-RBF　has　the　best　performance.　For　airport　expressway　dataset,　the　RMSE　is　less　than　3,　the　accuracy　is　78.8%　and　PCC　is　0.94.　For　Badaling　expressway　dataset,　the　RMSE　is　less　than　3,　the　accuracy　is　81.2%　and　PCC　is　0.921.