Crowds　are　an　important　feature　of　high-dense　Mass　Rail　Transit　(MRT),　assessing　its　crowding　status　is　a　critical　step　in　crowd　management.　In　this　chapter,　a　pedestrian　crowd　classification　method　based　on　an　improved　ant　colony　clustering　algorithm　(ACCA)　is　developed　for　MRT　systems.　First,　survey　data　from　Automatic　Fare　Collection　(AFC)　regarding　three　statuses　(check-in/check-out　and　sum).　Second,　the　PCI-influenced　factors　were　also　considered　in　the　method,　which　included　average　daily　ridership　intensity,　the　duration　of　crowd,　and　the　scope　of　crowd　influence.　Third,　to　classify　the　pedestrian　crowd,　an　improved　ant　colony　clustering　model　and　its　solving　algorithm　were　presented.　The　results　show　that,　for　the　two　types　of　time　scale,　the　passengers’　time–space　characteristics　present　a　clear　image　of　M,　the　variation　trend　of　morning　and　evening　peak　hour　is　obvious　in　the　MRT.　©　Springer　Nature　Singapore　Pte　Ltd.　2019.