Noise mitigation of UAV operations through a Complex Networks approach

This research combines complex systems science, geographical information systems, and environmental noise modelling to analyse effects of future air mobility in urban settings and plan efficient routes for vehicles. The research used the environmental noise maps of an urban agglomeration produced under the Environmental Noise Directive (END) as input to inform the UAV operations. These maps reveal potential routes for the UAV operations where the noise impact of the vehicle can be embedded within a high background noise due to the existing sources modelled under the END. When an agent based model is superimposed on a real-world map simple strategies of the diverse agents in interaction with the environment reveal patterns, such as dominant paths, points of congestion, and suggest positioning of terrestrial infrastructure. We investigate how agents can overcome the conflicts and find trade-off solutions by interacting only with their immediate neighbours-therefore enabling autonomy, decentralization, and putting to use emergent self-organising behaviour. The potential impact of increased drone operations on urban and peri urban regions is significant. Route optimisation which does not consider the noise is likely to impact on quite areas within our residential spaces and should be considered as part of noise action planning.