The　emergence　of　large-scale　spatiotemporal　trajectory　data　offers　an　excellent　opportunity　to　characterize　collective　human　mobility　patterns　and　their　relationship　with　the　urban　built　environment.　Such　an　approach　can　generate　a　complementary　understanding　of　traditional　individual　travel　behavior　based　on　travel　survey　data.　Using　mobile　phone　data,　this　study　aims　to　investigate　how　residential　built　environments　affect　residents＇　mobility　at　an　aggregated　level.　Specifically,　three　indicators　(movement　distance,　activity　space,　and　the　number　of　stops)　were　derived　from　raw　mobile　phone　data　to　characterize　human　mobility.　The　decay　co-efficients　and　average　values　were　then　employed　to　reveal　the　aggregated　characteristics　of　movement　distance,　activity　space,　and　the　number　of　stops.　Furthermore,　linear　regression　was　applied　to　examine　the　relationship　between　human　mobility　indicators　and　residential　built　environments.　The　results　indicate　that　the　living-built　environment　could　better　explain　the　activity　space　than　the　movement　distance　and　number　of　stops.　In　addition,　some　differences　in　the　relationship　between　human　mobility　and　residential　built　environments　are　identified　between　weekdays　and　weekends.　These　findings　could　provide　new　insights　into　human　mobility　and　its　interaction　with　the　built　environment,　thus　advancing　our　understanding　of　human　travel　behavior　from　both　individual　and　collective　perspectives.