Exploring　complicated　dynamic　spatiotemporal　correlations　has　always　been　a　challenging　issue　in　traffic　prediction.　Besides,　methods　that　make　predictions　directly　from　data　with　missing　values,　have　received　much　attention　due　to　the　inevitable　and　pervasive　nature　of　data　incompleteness　in　real　scenarios.　In　this　paper,　an　end-to-end　representation　learning　framework,　named　spatial-　temporal　periodical　adaptive　graph　contrastive　learning　(ST-A-PGCL),　is　proposed　to　address　such　issues.　ST-A-PGCL　mainly　consists　of　three　independent　branches　to　respectively　model　three　long-term　periodicities　of　traffic　flow　(recent,　daily,　and　weekly　periodicities).　In　each　branch,　the　spatial　and　tem-poral　correlations　are　extracted　by　improved　adaptive　graph　convolution　network　(ImpAdapGCN)　and　fused　seasonal-trend　temporal　convolution　network　(FST-TCN)　in　an　encoder,　respectively,　to　obtain　hidden　representation.　Besides,　each　branch　accepts　one　periodic　segment　which　will　be　synthetically　augmented　with　different　missing　patterns　to　simulate　real　scenarios　(weak　communication　signal,　detector　malfunction,　area-wide　power　failure,　etc)　and　generate　different　views.　These　views　will　be　fed　into　a　periodical　graph　contrastive　learning　(PGCL)　module　to　learn　periodical　similarity　features　based　on　Siamese　network　to　defeat　data　incompleteness.　Bidirectional　gate　recurrent　unit　(Bi-GRU)　is　selected　to　decode　the　hidden　representations　and　generate　final　prediction　results.　Specifically,　the　overall　framework　is　trained　in　end-to-end　dual-task　(traffic　prediction　and　contrastive　learning)　process　without　requiring　identifying　the　position　of　missing　values.　Our　framework　is　evaluated　across　four　real-world　datasets　and　twenty　baseline　models.　Experimental　results　show　that　the　proposed　ST-A-PGCL　achieves　superior　prediction　performance,　especially　in　long-term　prediction　tasks　with　high　missing　rates.(c)　2023　Elsevier　B.V.　All　rights　reserved.