Traffic congestion and noise emissions with detailed vehicle trajectories from UAVs

Excessive noise in cities due to road traffic negatively affects human health. We utilize detailed vehicle trajectories collected by a swarm of drones to estimate traffic noise emissions in an urban environment. We use the CNOSSOS model to estimate vehicles' sound power level. Considering each vehicle an individual source point, we compute the noise levels at four bus stops, analyze their noise patterns and quantify their contribution. Using well-known acoustic metrics like the equivalent continuous sound level, we perform a sensitivity analysis of the radius around an evaluation point to compute the noise levels. We show the correlation between noise emissions and congestion at the microscopic level, and we analyze noise levels at the macroscopic level due to better sound propagation integration. The results show clear trends for sound power level and differences across them. Finally, the aggregated equivalent continuous sound levels present a clear pattern with vehicle accumulation.