Harvesting　O-D　trip　data　from　Tianjin’s　bike-sharing　system,　this　paper　analyzes　the　travel　behavior　of　bike-sharing　in　Tianjin　from　a　regional　perspective,　which　offers　significant　insights　into　understanding　the　spatiotemporal　flow　characteristics　of　bike-sharing　demand.　First,　the　spatiotemporal　travel　behavior　of　bike-sharing　is　analyzed　from　a　regional　perspective.　Second,　this　study　employs　the　non-negative　matrix　factorization　(NMF)　algorithm　to　classify　bike-sharing　travel　patterns　into　two　dimensions,　namely　spatial　and　temporal.　Finally,　the　Poisson　regression　model　is　used　to　interpret　each　pattern.　The　study　presents　a　series　of　findings,　such　as　the　spatial　and　temporal　distribution　patterns　of　bike-sharing　travel　behavior　and　five　different　interpretations　of　bike-sharing　travel　patterns　in　Tianjin.　The　findings　contribute　to　understanding　bike-sharing　behavior,　predicting　bike-sharing　system　demand,　managing　its　services,　and　promoting　green　mobility　in　cities.　©　ASCE.