With　the　development　of　location-based　services　and　data　collection　equipment,　the　volume　of　trajectory　data　has　been　growing　at　a　phenomenal　rate.　Raw　trajectory　data　come　in　the　form　of　sequences　of　＂coordinate-time-attribute＂　triplets,　which　require　complicated　manual　processing　before　they　can　be　used　in　data　mining　algorithms.　Current　works　have　started　to　explore　the　emerging　deep　representation　learning　method,　which　maps　trajectory　sequences　to　vector　space　and　applies　them　to　various　downstream　applications　for　boosting　accuracy　and　efficiency.　In　this　work,　we　propose　a　universal　trajectory　representation　learning　method　based　on　a　Siamese　geography-aware　transformer　(TRT　for　short).　Specifically,　we　first　propose　a　geography-aware　encoder　to　model　geographical　information　of　trajectory　points.　Then,　we　apply　a　transformer　encoder　to　embed　trajectory　sequences　and　use　a　Siamese　network　to　facilitate　representation　learning.　Furthermore,　a　joint　training　strategy　is　designed　for　TRT.　One　of　the　training　objectives　is　to　predict　the　masked　trajectory　point,　which　makes　the　trajectory　representation　robust　to　low　sampling　rates　and　noises.　The　other　is　to　distinguish　the　difference　between　trajectories　by　means　of　contrastive　learning,　which　makes　the　trajectory　representation　more　uniformly　distributed　over　the　hypersphere.　Last,　we　design　a　benchmark　containing　four　typical　traffic-related　tasks　to　evaluate　the　performance　of　TRT.　Comprehensive　experiments　demonstrate　that　TRT　consistently　outperforms　the　state-of-the-art　baselines　across　all　tasks.