Taxi　travel　flow　patterns　and　their　interday　stability　play　an　important　role　in　the　planning　of　urban　transportation　and　public　service　facilities.　Existing　studies　pay　little　attention　to　the　stability　of　the　travel　flow　patterns　between　days,　and　it　is　difficult　to　consider　the　impact　of　dynamic　changes　in　daily　travel　demand　analysis　when　supporting　related　decision　making.　Taxi　trajectory　data　have　been　widely　used　in　urban　taxi　travel-pattern　analysis.　This　paper　uses　the　taxi　datasets　of　Shenzhen　and　New　York　to　analyze　and　compare　the　interday　stability　of　the　taxi　travel　spatial　structure　and　the　flow　volume　based　on　the　improved　Levenshtein　algorithm　and　geographic　flow　theory.　The　results　show　that　(1)　interday　differences　in　taxi　travel　flow　are　obvious　in　both　spatial　structure　and　flow　volume,　high-frequency　origin-destination　(OD)　trips　are　relatively　stable;　(2)　the　ODs　between　the　central　urban　area　and　surrounding　areas　exhibit　high　traffic　volume　and　high　interday　stability,　and　the　ODs　starting　or　ending　at　an　airport　exhibit　high　traffic　stability;　(3)　one　week＇s　data　can　describe　86%　of　the　overall　travel　structure　and　84%　of　travel　flow　in　Shenzhen,　and　one　week＇s　New　York　data　can　describe　73%　of　travel　structure　and　76%　of　travel　flow.　There　are　differences　in　the　travel　patterns　of　people　in　different　cities,　and　the　representativeness　of　datasets　in　different　cities　will　be　different.　These　findings　can　help　to　better　understand　the　outcomes　of　taxi　travel　patterns　derived　from　a　relatively　short　period　of　data　to　avoid　potential　misuse　in　related　decision　making.