The　extraction　of　public　transit　passengers＇travel　information　has　great　significance　to　master　the　time-space　characteristics　of　public　transit　travel　and　to　improve　the　efficiency　of　residents＇　commuting.　Through　matching　and　processing　the　multi-source　public　transport　data　which　derived　from　bus　smart　card　data,　bus　location　data　and　subway　AFC　system　data,　this　paper　mainly　studies　the　methods　and　rules　of　the　transfer　relationship　judgment,　commuting　travel　identification　and　trip　starting　point　matching,　which　are　essential　steps　for　the　extraction　of　public　transportation　trip　chain　information.　The　thresholds　for　trip　chain　matching　and　connecting　is　also　calibrated,　and　the　public　transit　commuting　chain　extraction　model　is　established　based　on　the　individual　riders＇travel　data.　The　results　of　the　extract　model　validation　show　that　the　success　rate　of　trip　chain　structure　extraction　is,　and　commuting　travel　identification　reach　100%,　and　the　success　rate　of　origin　stop　and　destination　stop　of　passengers＇trip　is　87.5%　and　the　accuracy　is　up　to　97.1%　The　study　provide　essential　foundation　for　the　public　transport　commuter　travel　identification　and　the　public　transit　trip　chains　time-space　features　analysis　based　on　the　individual　travelers＇ridership　data.　Copyright　©　2017　by　Science　Press.