Priority　development　of　public　transport　is　an　important　way　to　implement　the　sustainable　development　of　urban　transport,　how　to　scientifically　identify　different　travelers＇　dependence　on　public　transportation　is　conducive　to　explore　the　travelers＇　usage　behavior　of　public　transport,　provide　more　accurate　public　transport　services.　Based　on　the　dynamic　and　static　data　of　public　transport　and　individual　travel　survey　data,　this　study　uses　the　relevancy　and　matching　technology　to　generate　the　individual　travel　chain　information　based　on　fused　data,　then　selects　8　identification　indicators　from　the　dimensions　of　individual　traveling　habits　behavior　and　individual　attributes　to　describe　the　individual　travel　dependence　on　public　transport.　The　two-step　clustering　algorithm　which　can　deal　with　mixed　variables　is　taken　as　an　identification　model　of　individual　travel　dependence　on　public　transport.　Finally,　the　identification　model　is　applied　to　the　actual　research　in　Beijing,　and　the　investigated　population　is　clustered　for　four　categories　from　the　perspective　of　public　transport　travel　dependence.　Then　the　individual　category　of　respondents　is　identified　based　on　incomplete　identification　indicators,　and　the　effects　of　assistant　indicators　on　identification　results　are　quantitatively　evaluated　with　average　hit　ratio　(AHR)　and　average　coverage　ratio　(ACR).　The　results　indicate　that　occupation,　vehicle　ownership,　and　income　can　be　taken　as　assistant　factors　when　the　information　of　assistant　indicators　is　incomplete　and　large　scale　of　traveler　data　need　to　be　collected　and　processed.　The　identification　method　of　individual　travel　dependence　on　public　transport　can　provide　a　meaningful　reference　for　optimizing　public　transport　system　and　improving　public　transport　sharing　rates.