During　the　structural　health　monitoring　of　bridges,　it　has　been　observed　that　the　vibration　data　collected　can　sometimes　be　randomly　lost　or　sampled　non-uniformly.　This　leads　to　a　low　signal-to-noise　ratio　in　the　spectral　functions　of　the　measured　data,　making　it　difficult　to　identify　weak　modes.　To　address　this　issue,　a　framework　for　operational　modal　identification　is　proposed　in　this　study.　It　utilizes　the　fast　Bayesian　fast　Fourier　transform　(FFT)　method　to　estimate　the　modal　parameters　of　highway　bridges　considering　the　non-uniform　monitoring　data.　The　initial　frequency　parameters　for　the　fast　Bayesian　FFT　approach　are　automatically　determined　using　the　proposed　autoregressive　(AR)　power　spectral　density　(PSD)-guided　peak　picking　method.　This　overcomes　the　challenge　of　capturing　initial　frequencies　related　to　weakly　contributed　modes.　Additionally,　the　bandwidth　parameter　for　each　mode　is　determined　using　the　modal　assurance　criterion　(MAC)　of　the　first　left　singular　vectors　of　PSD　matrices.　Furthermore,　when　analyzing　non-uniform　vibration　data,　it　is　recommended　to　use　the　non-uniform　FFT　(NUFFT)　for　calculating　PSD　functions　in　order　to　improve　identification　accuracy.　The　proposed　method　is　validated　using　acceleration　data　from　both　a　numerical　model　and　a　real-world　bridge.　The　results　demonstrate　that　the　identification　uncertainty　of　modal　parameters　increases　with　higher　non-uniform　levels.