Expressway　section　speed　can　visually　reflect　the　section　operation　condition,　and　accurate　short　time　section　speed　prediction　has　a　wide　range　of　applications　in　path　planning　and　traffic　guidance.　However,　existing　expressway　speed　prediction　data　have　defects,　such　as　sparse　density　and　incomplete　object　challenges.　Thus,　this　paper　proposes　a　framework　for　a　combined　expressway　traffic　speed　prediction　model　based　on　wavelet　transform　and　spatial-temporal　graph　convolutional　network　(WSTGCN)　of　the　Electronic　Toll　Collection　(ETC)　gantry　transaction　data.　First,　the　framework　pre-processes　the　ETC　gantry　transaction　data　to　construct　the　section　speeds.　Then　wavelet　decomposition　and　single-branch　reconstruction　are　performed　on　the　section　speed　sequences,　and　the　spatial　features　are　captured　by　graph　convolutional　network　(GCN)　for　each　reconstructed　single-branch　sequence,　and　the　temporal　features　are　extracted　by　connecting　the　gated　recurrent　unit　(GRU).　The　experiments　use　the　ETC　gantry　transaction　data　of　the　expressway　from　Quanzhou　to　Xiamen.　The　results　indicate　that　the　WSTGCN　model　makes　notable　improvements　compared　to　the　model　of　the　baseline　for　different　prediction　ranges.