[Objectives]　Identifying　regular　travel　groups　on　freeways　is　a　prerequisite　for　accurately　guiding　travel　demand　and　providing　personalized　travel　services.　[Methods]　Existing　research　mainly　focuses　on　urban　transportation,　extracting　travel　characteristics　based　on　time-frequency　indicators　to　identify　regular　travel　groups.　However,　the　identification　of　regular　users　for　freeway　systems,　characterized　by　large　travel　time　spans　and　varying　travel　times,　has　not　been　fully　explored.　[Results]　This　study,　based　on　toll　data　from　the　Guangxi　Zhuang　Autonomous　Region　freeway　network,　analyzes　user　trajectories　between　toll　booth　entry　and　exit　points　as　signal　inputs.　Using　Fourier　transform,　the　variance　of　the　top　three　amplitudes　corresponding　to　periodic　signals　is　calculated　as　a　special　index　to　estimate　whether　trips　are　regular.　A　Fourier　transform-based　＇trajectory　plotting-spectral　transform-period　analysis＇　method　is　proposed　for　identifying　and　analyzing　cyclical　freeway　users,　with　the　Guangxi　Zhuang　Autonomous　Region　as　the　case　study.　[Conclusions]　The　results　show　that:　(1)　Freeway　travel　records　in　Guangxi　Zhuang　Autonomous　Region　exibit　a　head　effect,　with　20%　of　vehicles　accounting　for　55%~60%　of　toll　records.　(2)　At　a　periodic　variance　threshold　of　0.5,　about　12%　of　passenger　and　cargo　vehicles　are　identified　as　regular　users;　at　a　threshold　of　1.0,　this　increases　to　about　18%;　and　at　a　threshold　of　2.0,　it　rises　to　approximately　25%.　(3)　Passenger　cars　with　frequent　travel　patterns　do　not　necessarily　exhibit　periodicity,　while　vehicles　(both　passenger　and　cargo)　with　periodic　patterns　typically　have　shorter　travel　distances　and　more　stable　travel　times.　The　findings　of　this　study　provide　a　reference　for　expanding　methods　of　identifying　regular　freeway　users　and　exploring　the　periodic　characteristics　of　roadway　user　trips.　©　2025　Science　Press.　All　rights　reserved.