Vehicle　reidentification　refers　to　the　mission　of　matching　vehicles　across　nonoverlapping　cameras,　which　is　one　of　the　critical　problems　of　the　intelligent　transportation　system.　Due　to　the　resemblance　of　the　appearance　of　the　vehicles　on　road,　traditional　methods　could　not　perform　well　on　vehicles　with　high　similarity.　In　this　paper,　we　utilize　hypergraph　representation　to　integrate　image　features　and　tackle　the　issue　of　vehicles　re-ID　via　hypergraph　learning　algorithms.　A　feature　descriptor　can　only　extract　features　from　a　single　aspect.　To　merge　multiple　feature　descriptors,　an　efficient　and　appropriate　representation　is　particularly　necessary,　and　a　hypergraph　is　naturally　suitable　for　modeling　high-order　relationships.　In　addition,　the　spatiotemporal　correlation　of　traffic　status　between　cameras　is　the　constraint　beyond　the　image,　which　can　greatly　improve　the　re-ID　accuracy　of　different　vehicles　with　similar　appearances.　The　method　proposed　in　this　paper　uses　hypergraph　optimization　to　learn　about　the　similarity　between　the　query　image　and　images　in　the　library.　By　using　the　pair　and　higher-order　relationship　between　query　objects　and　image　library,　the　similarity　measurement　method　is　improved　compared　to　direct　matching.　The　experiments　conducted　on　the　image　library　constructed　in　this　paper　demonstrates　the　effectiveness　of　using　multifeature　hypergraph　fusion　and　the　spatiotemporal　correlation　model　to　address　issues　in　vehicle　reidentification.　©　2021　Wang　Li　et　al.