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As traffic noise has become a major source of noise in urban public spaces, key traffic factors such as vehicle speed and honking patterns have been shown to have a significant impact on people’s perception of their environment; consequently, more refined measurement and research methods are required to support relevant traffic management. This paper proposes a method for measuring the perception of traffic noise using Action Units (AUs) based on facial expression recognition (FER), and conducts perception experiments to investigate the effects of vehicle speed and honking patterns. The results show that medium- to high-speed traffic noise significantly reduces valence (by 0.04–0.10), whilst low-speed traffic noise has no significant effect; when foreground horn sounds were introduced, the initial decrease in valence was similar across conditions, whilst the subsequent recovery following the cessation of the horn varied between 0.06 and 0.14 across different patterns. This methodology and its findings help to elucidate the non-verbal emotional responses triggered by traffic noise and provide a basis for refined traffic management measures, such as speed control and horn usage regulation.