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Abstract Distributed acoustic sensing (DAS) offers a cost effective, nonintrusive method for high-resolution near-surface characterization in urban environments where conventional geophysical surveys are limited or nonexistent. However, passive imaging with DAS in urban settings presents challenges such as strong diurnal traffic noise, nonlinear array geometry, and poor fiber coupling to the ground. We repurposed a dark fiber in Melbourne, Australia, into a 25 km DAS array that traces busy arterial roads, tram routes, and orthogonal sections. By employing noise cross correlation and array beamforming, we calculated dispersion curves and successfully inverted for a near-surface shear-wave velocity model down to 100 meters. Stationary seismic sources are maximized by selecting daytime traffic signals, thereby recovering surface waves and reducing interference from acoustic waves from man-made structures in the subsurface. Poorly coupled channels, which are linked to fiber maintenance pits, are identified through cross-correlation amplitudes. The dispersion curve calculation further considers the channel orientation to avoid mixing Rayleigh and Love waves. Using a trans-dimensional Markov chain Monte Carlo sampling approach, we achieved effective model inversion without a prior reference model. The resulting near-surface profile aligns with mapped lithology and reveals previously undocumented lithological variation.