The　accurate　depiction　and　understanding　of　the　travel　behavior　characteristics　of　public　transport　(PT)　commuters　is　an　important　foundation　for　better　improving　PT　service　and　encouraging　car　owners　to　use　the　sustainable　and　ecofriendly　PT;　and　there　are　significant　differences　in　the　travel　stability　(TS)　characteristic　of　PT　commuters,　developing　methods　for　accurately　measuring　such　differences　is　an　issue.　Therefore,　smart　card　transaction　data,　line　and　stop　data,　and　travel　survey　data　from　Beijing　were　collected,　then　individual　travel　chain　information　of　commuting　passengers　was　extracted　using　the　associating　and　matching　method.　Thereafter,　a　multilevel　characteristic　indicator　system　including　the　number　of　nonhome　activity　points,　commuting　trip　ratio,　travel　spatial　equilibrium,　time　stability　and　departure　time　concentration　was　constructed　to　capture　the　individual　TS.　Moreover,　an　association　rule　mining　model　based　on　the　frequent-pattern　(FP)　growth　algorithm　was　developed　by　modeling　the　indicators　as　items　and　the　PT-commuter　TS　as　transactions.　Thus,　seven　meaningful　rules　for　revealing　the　internal　relationships　between　individual　travel　characteristics　and　commuter　TS　were　obtained,　and　PT　commuters　were　classified　into　three　groups　according　to　the　TS　levels.　Finally,　a　conceptual　model　of　the　mode　shift　to　higher　TS　levels　among　commuters　was　developed,　and　some　targeted　measures　for　enhancing　the　TS　levels　of　PT　users　were　proposed.　The　findings　are　expected　to　provide　new　perspectives　for　travel　behavior　analysis　and　policy　control　to　enhance　and　maintain　passenger　TS,　also　are　conducive　to　increasing　PT　usage　while　reducing　the　usage　of　cars.