Traditional　taxis　and　the　e-hailing　cars　are　two　main　transport　vehicles　for　the　public　in　current　taxi　market,　which　aim　to　satisfy　the　customized　travel　demand　in　daily　lives　of　citizens　in　urban　public　transportation　system.　Due　to　the　differences　in　service　modes　and　commercial　patterns,　the　two　vehicles　are　appropriate　for　different　target　groups.　Investigating　the　spatial　and　temporal　characteristics　of　these　two　types　of　vehicle　based　on　human　travel　flows　can　support　the　applications　such　as　optimization　of　the　urban　public　transportation　and　land　use　planning.　The　geographical　flow　space　theory　proposed　recently　provides　a　new　theoretical　perspective　as　well　as　a　systematical　analysis　framework　in　studying　the　flow　patterns　of　the　travels　by　different　types　of　vehicles.　In　this　paper,　we　adopt　this　formulated　theory　framework　to　describe　the　travel　flow.　We　select　five　typical　flow　patterns,　namely　random,　clustering,　aggregation,　divergence,　and　community　patterns,　to　reveal　their　spatial　distribution　characteristics　and　compare　the　differences　in　their　travel　patterns.　The　trajectory　dataset　of　traditional　taxis　and　the　e-　hailing　cars　in　Xiamen　City　is　employed　to　validate　the　effectiveness　of　the　geographical　flow　space　theory.　We　find　that:　(1)　the　travel　flows　of　the　two　types　of　vehicles　present　significant　non-random　characteristics　in　flow　space;　(2)　people　tend　to　choose　e-hailing　cars　for　long　distance　travel,　while　prefer　the　traditional　taxis　for　short　and　medium　distance　travels;　(3)　the　two　types　of　cars　show　different　spatial　distribution　characteristics　of　the　four　typical　flow　patterns.　The　travels　by　e-　hailing　cars　are　more　widely　distributed　and　exhibit　clustering　patterns　around　the　sub-centers　at　the　suburban　areas　outside　the　core　Xiamen　Island　and　the　east-southern　software　park　area　inside　the　Xiamen　Island.　Due　to　the　travel　demand　driven　model,　the　e-hailing　cars　satisfy　the　emerging　high　travel　demand　areas　and　tend　to　form　community　patterns.　While　the　traditional　cars　are　mainly　distributed　around　the　well-known　city　landmarks　(e.g.,　Zengcuoan,　Zhongshan　road)　on　the　Island;　(4)　approximately　a　quarter　of　the　local　areas　have　more　than　one　typical　flow　patterns.　Different　types　of　cars　exhibit　different　co-location　flow　patterns　and　spatial　distribution　characteristics.　The　mixed　flow　patterns　derived　from　the　geographical　flow　theory　provide　a　more　comprehensive　perspective　to　better　understand　the　travel　flows,　which　can　mitigate　the　misleading　information　from　each　isolated　flow　pattern.　The　above　findings　imply　that　the　geographical　flow　theory　can　help　to　better　understand　the　characteristics　of　the　geographical　flows　and　can　be　used　to　improve　the　applications　based　on　related　results.　©　2023　Research　Institute　of　Beijing.　All　rights　reserved.