This research focuses on simulating road traffic soundscapes using auralization techniques. A structured workflow for urban traffic auralization is developed by integrating SUMO traffic flow data with acoustic simulation tools such as RAVEN and Pigeon. To generate plausible traffic simulations, a custom script is implemented to produce representative vehicle trajectories. Additionally, parameters such as crossfading and update rates are optimized to ensure smooth and plausible audio transitions in auralization.
Plausibility is assessed through experiments conducted in free-field conditions, focusing on the effects of sound smoothness and varying traffic scenarios. A series of analyses between measured and simulated vehicle trajectory data in auralization indicates that while some aspects of the simulation perform reasonably well, significant differences are observed in overall loudness and timing. These findings suggest that the workflow holds potential to support urban planners in predicting and evaluating noise impacts during the early stages of city planning, though further refinements are needed for greater accuracy.

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