Dynamic noise mapping represents a relatively new concept in the airport noise literature.
Although the ultimate goal of making any noise map should be to determine the number of people exposed to noise, none of the approaches consider population movement dynamics. Even though such dynamic noise maps indicate different noise levels during the observed periods for which they are made, it is assumed that the population is constant in all observed locations, which is not the case in reality.
The first attempt to include the dynamics of population’s movements into the assessment of aircraft noise exposure was carried out by Ganić et al, followed by a series of papers by Ganić et al. and Ho-Huu et al. In all this research efforts, the emphasis was on optimising the aircraft assignment to departure and arrival routes, while dynamic noise maps were created only for one-day scenarios to demonstrate the possibilities of the developed algorithm to reduce noise annoyance and fuel consumption. Furthermore, the calculations of daily population mobility included many assumptions and were based only on data from the census.
Below, a real case study conducted within the ANIMA project, based on the one-year air traffic data, will shed light on the benefits of using this new approach and demonstrate how daily movements influence the estimated population noise annoyance around an airport.
Human mobility patterns
In the sense of dynamic noise mapping, human mobility patterns (sometimes also referred to as daily population mobility or movement patterns) are defined as the movements of human beings (individuals and groups) in space and time. The motivation behind people’s movements on a daily basis is manifold. While most commonly, daily trips include commuting to and from work or school, they are also connected with social, leisure and other activities.
During the last decade, substantial progress has been made in the study of human mobility. Not only that significant advancement in the field of information and communication technologies enabled more accurate tracking of people’s movements, but also collection and processing of such data is more accessible to the general public.
While geography might be the first discipline to analyse mobility data and put forward corresponding theories to describe travel patterns (H. Barbosa et al), the study of human mobility currently spans several disciplines. It is widely used in transportation studies to explain how people plan and schedule their daily travel and provide better forecasts of future travel patterns.
A better understanding of human mobility patterns leads to more appropriate urban planning and infrastructure design, new tools to monitor health and well-being in cities, reduction of pollution, internal security and epidemic modelling, to name but a few. Here, special emphasis will be given to using human mobility patterns to estimate more accurately the number of people annoyed by aircraft noise.
