Traffic　Anomaly　Detection　(TAD)　is　an　important　and　difficult　task　in　Intelligent　Transportation　Systems　(ITS).　Traffic　anomaly　events　are　sparse　in　both　spatial　and　temporal　spaces,　posing　a　challenge　to　the　performance　of　model.　Moreover,　a　single　traffic　anomaly　event　can　impact　multiple　road　sections　in　the　neighborhood,　further　undermining　the　accuracy　of　TAD.　In　this　paper,　we　propose　a　new　TAD　method　based　on　spatio-temporal　hypergraph　convolutional　neural　network.　Specifically,　we　adopt　a　spatial–temporal　augmentation　approach　for　traffic　data.　This　will　enhance　the　performance　of　detecting　sparse　anomalies.　Meanwhile,　we　introduce　a　hypergraph　learning　method　to　model　the　road　network.　This　could　capture　the　spreading　features　of　anomalies　for　better　detection　results.　Additionally,　we　design　a　dynamic　hypergraph　construction　method　to　extract　the　evolving　relationships　of　road　segments.　The　proposed　model　evaluation　on　the　Beijing　(SE-BJ)　dataset　for　TAD　reveals　superior　performance　compared　to　state-of-the-art　ones.　©　2024