Understanding　commuting　patterns　could　provide　effective　support　for　the　planning　and　operation　of　public　transport　systems.　One-month　smart　card　data　and　travel　behavior　survey　data　in　Beijing　were　integrated　to　complement　the　socioeconomic　attributes　of　cardholders.　The　light　gradient　boosting　machine　(LightGBM)　was　introduced　to　identify　the　commuting　patterns　considering　the　spatiotemporal　regularity　of　travel　behavior.　Commuters　were　further　divided　into　fine-grained　clusters　according　to　their　departure　time　using　the　latent　Dirichlet　allocation　model.　To　enhance　the　interpretation　of　the　behavior　patterns　in　each　cluster,　we　investigated　the　relationship　between　the　socioeconomic　characteristics　of　the　residence　locations　and　commuter　cluster　distributions.　Approximately　3.1　million　cardholders　were　identified　as　commuters,　accounting　for　67.39%　of　daily　passenger　volume.　Their　commuting　routes　indicated　the　existence　of　job-house　imbalance　and　excess　commuting　in　Beijing.　We　further　segmented　commuters　into　six　clusters　with　different　temporal　patterns,　including　two-peak,　staggered　shifts,　flexible　departure　time,　and　single-peak.　The　residences　of　commuters　are　mainly　concentrated　in　the　low　housing　price　and　high　or　medium　population　density　areas;　subway　facilities　will　promote　people　to　commute　using　public　transport.　This　study　will　help　stakeholders　optimize　the　public　transport　networks,　scheduling　scheme,　and　policy　accordingly,　thus　ameliorating　commuting　within　cities.