Noise-aware urban drone delivery: Integrating demand modeling, routing optimization and spatial noise exposure assessment

While drone delivery is increasingly discussed for urban logistics, concerns regarding resident noise exposure potentially limit its large-scale deployment. In this study, we attempt to evaluate noise-aware urban delivery strategies by proposing an integrated modeling framework that links demand estimation with spatially explicit drone routing and noise simulation. Consumer choice of drone delivery is predicted based on a multinomial logit model, and the resulting distance-based delivery demand is translated into daily order volumes. Drone operations are then simulated under four scenarios combining direct-to-home delivery versus neighborhood-scale micro-hub-based (convenience-store-based) delivery and distance-optimal versus noise-aware routing. A modified A* algorithm incorporating altitude-dependent sound propagation and population-weighted noise penalties is applied to generate flight paths. Results based on the assessment of grid-based sound exposure indicate that noise-aware routing and micro-hub delivery reshape the spatial distribution of noise exposure, with mixed effects across exposure thresholds, while higher flight altitudes further mitigate peak noise levels. These findings offer important insights for urban planners and logistics providers by demonstrating that drone-related noise is not an avoidable externality, but a controllable outcome shaped by noise-aware integrated routing frameworks. The proposed framework provides a practical approach for assessing noise-aware urban drone deployment strategies in dense city environments.