Travel　mode　recognition　is　a　key　issue　in　urban　planning　and　transportation　research.　While　traditional　travel　surveys　use　manual　data　collection　and　have　limited　coverage,　poor　timeliness,　and　insufficient　sample　capacity,　recent　advancements　in　Global　Positioning　System　(GPS)　technology　allow　large-scale　data　collection　and　offer　novel　opportunities　to　enhance　travel　mode　recognition.　However,　existing　studies　often　neglect　regular　differences　and　changes　in　motion　states　across　different　travel　modes　and　fail　to　fully　integrate　multi-scale　spatio-temporal　features,　which　limits　the　accurate　classification　of　travel　modes.　To　fill　this　gap,　this　study　proposes　a　multi-scale　spatio-temporal　attribute　fusion　(MSAF)　model　for　precise　travel　mode　identification　using　solely　GPS　trajectories　without　altering　their　sampling　rate.　The　MSAF　model　segments　GPS　trajectories　into　various　temporal　and　spatial　scales,　extracting　local　motion　states　and　spatial　features　at　multiple　scales.　The　spatio-temporal　feature　extraction　module　is　constructed　to　extract　local　motion　states　and　capture　spatio-temporal　dependencies.　Additionally,　the　model　incorporates　a　multi-scale　feature　fusion　module,　which　effectively　combines　features　of　various　scales　through　a　series　of　fusion　techniques　to　obtain　a　comprehensive　representation,　enabling　automatic　and　accurate　travel　mode　identification.　Experiments　on　real-world　datasets,　including　the　GeoLife　Trajectories　dataset　and　the　Sussex-Huawei　Locomotion-Transportation　(SHL)　dataset,　demonstrate　the　effectiveness　of　the　MSAF　model,　achieving　a　competitive　accuracy　of　95.16%　and　91.70%.　This　represents　an　improvement　of　2.50%　to　7.95%　and　0.8%　to　6.62%　over　several　state-of-the-art　baselines,　effectively　addressing　sample　imbalance　challenges.　Moreover,　the　experiments　demonstrate　the　significant　role　of　multiscale　feature　fusion　in　improving　model　performance.　©　2024　Wuhan　University.　Published　by　Informa　UK　Limited,　trading　as　Taylor　&　Francis　Group.