Dynamic population maps

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.

ANIMA Case Study: Dynamic Noise Maps for Ljubljana Airport

This case study aims to show how population movements affect the estimated number of people exposed to aircraft noise. The research was done within the ANIMA project to demonstrate the capabilities of dynamic noise mapping compared to the traditional way of developing noise maps. More detailed information about the methodology and input data for this study can be found in this article.

Ljubljana Jože Pučnik Airport was chosen for the case study. It is the busiest airport in Slovenia, handling more than 1,7 million passengers and approximately 31 thousand aircraft operations in 2019. The airport has a single runway 3,300 m long and is located 20 km northwest of the Ljubljana capital. Even though the Ljubljana airport is not recognised as a „major airport“, as defined in the Directive 2002/49/EC, proactive noise assessment actions have been undertaken by the airport authority, including the development of noise contour maps and regular, continuous noise monitoring in the most noise-exposed areas for several years.

The year 2018 has been selected for this case study based on data availability regarding population mobility patterns and air traffic. The yearly traffic at Ljubljana airport for 2018 consisted of 35,512 aircraft operations, performed by 213 different aircraft types, indicating that on average, there were approximately 97 operations per day or 4 movements per hour. There were several peaks during the day, with the most flights occurring between 5 PM and 6 PM (nine flights on average), while only 6% of operations were performed during the night hours.

The population mobility patterns are assessed using a dedicated national travel survey. The most recent one conducted in Slovenia was the Daily Passenger Mobility Survey (TR-MOB 2017). The data were obtained from the Statistical Office of the Republic of Slovenia through the special request for protected microdata containing detailed information about each trip on an individual level. The number of respondents in this survey was 8,842, while the number of trips conducted was 24,195. Since 1,355 (15.3%) survey participants reported that they stayed at home, the average number of trips per day can be calculated as 2.7 or 3.2, depending on whether all the respondents are considered or only the ones conducting the trips. In terms of different trip purposes, leisure activities were the reasons behind most of the recorded trips (35.6%), followed by commuting trips to and from work (24.3%). Other trip purposes included education, professional and personal business, shopping, and escorting (driving/picking up/accompanying a child or other person). More information about this survey, including explanations about methodology, is contained in the Statistical Office of the Republic of Slovenia.

Now that all the data have been collected, the A-weighted equivalent continuous sound level was calculated to match the number of people exposed to those noise levels at each location (500 m x 500 m grid) during each hour. As a result, the cumulative noise impact for each person has been assessed and the results are presented below.

Results – ANIMA Case Study: Dynamic Noise Maps for Ljubljana Airport

The results lead to the conclusion that even though people live at locations enclosed in the 37 dB L_den noise contour (the threshold for becoming annoyed according to the EC), 10.1% of them (marked with a white square symbol on Figure 1) are not exposed to aircraft noise levels (L_den) above 37 dB due to daily mobility to locations away from the airport. Furthermore, apart from the 4,884 people that are living within the presented noise contour (marked with a red star symbol), there are additional 704 persons (14.4%) also experiencing aircraft noise exposure (marked with yellow triangles) even though they are located outside the 37 dB noise contour. It can be explained by considering that people who live outside the area affected by aircraft noise may work or study within these areas at some time during the day and are, therefore, affected by aircraft noise. The fourth group of people (marked with grey circles) resides outside this noise contour and is not affected by aircraft noise, even when the daily mobility patterns are considered.

^{Figure 1. Dynamic noise map for Ljubljana airport}

To better demonstrate the dynamics behind this novel approach, the 24 different hourly noise maps, including the estimated number of people at each location, are combined and presented in the video below (Figure 2). This video illustrates the temporal and spatial variation in population around Ljubljana airport. In addition, the change in the number of operations between each hour is clearly visible through the different shapes and areas that calculated noise contours take. As expected, the most minor changes are observed during night hours, when the traffic frequency is low as well as the movements of the people.

^{Figure 2. Simulation of dynamic noise maps}

It should be kept in mind that this case study only considers L_den as a factor for estimating annoyance. As shown within the ANIMA project, aside from noise exposure, non-acoustic factors have a considerable influence on perceived annoyance, and they should also be taken into account in future research on dynamic noise mapping.

Heathrow Case Study

After the first case study for Ljubljana airport, which was considered to be an airport with relatively low traffic, the next aim was to test the model on one of the largest airports in Europe surrounded by millions of inhabitants. Therefore, Heathrow airport was chosen for the second case study regarding dynamic noise maps. Due to its proximity to highly populated and dense areas, the airport has been engaging in noise management for many decades. Located 21km west of central London, the airport employs over 76,000 people - half of whom live in the surrounding five London Boroughs.

In 2019, Heathrow airport handled more than 80 million passengers and approximately 475,000 aircraft operations. The year 2019 has been selected for this case study based on the availability of data regarding population mobility patterns and air traffic. All the data regarding air traffic were obtained from Environmental Research and Consultancy Department (ERCD) of the Civil Aviation Authority (CAA).

Heathrow airport has two parallel runways. The Northern runway (direction 09L/27R) is 3,902 m long and 50 m wide, while Southern runway (direction 09R/27L) has length of 3,658 m with the same width of 50 m. The ratio of westerly (i.e. Runway 27L/27R) and easterly (i.e. Runway 09L/09R) operations is referred to as the runway modal split. During 2019, Heathrow recorded a 75/25 split in favour of westerly/easterly operations (Heathrow Airport, 2019). ANOMS is the Noise and Track Keeping system (NTK) at Heathrow, which receives radar data from NATS Air Traffic Control (ATC). Mean departure and arrival flight tracks were generated from summer radar data.

In order to obtain the human mobility patterns for population around Heathrow airport, London Travel Demand Survey (LTDS) data have been used for this case study. In 2018/2019, 18,575 people participated in the LTDS survey . There were 37,755 trips, taken by 12,995 persons. The other 5,580 survey participants (≈30%) reported that they stayed at home for the whole day. Weights provided in the LTDS were used to scale up the results and to give a rough estimate of the expected impact on the Greater London population (8,826,935).

In this research, Output Areas (OAs) are used as locations of usual residence. Output Area is the lowest geographical level at which census estimates are provided. Workplace Zones (WZs) represent usual places of work. They are an output geography for England and Wales that has been produced using workplace data from the 2011 Census. Data about population and workplace-based statistics were mostly provided by the Office for National Statistics (ONS) (https://www.ons.gov.uk/). Boundaries or Local Authority Districts and Population Weighted Centroids for OAs and WZs were obtained from The Open Geography portal from the Office for National Statistics (https://geoportal.statistics.gov.uk/). Detailed statistics about the characteristics of the workplace population was obtained from NOMIS website (https://www.nomisweb.co.uk/). Classification of Workplace Zones (COWZ) and cluster membership was used to assess the purpose of use for each workplace zone.

Since human mobility patterns were estimated from London Travel Demand Survey data, the population noise exposure analysis in this case study only included the residents of Greater London Area, while the affected population located west of the Heathrow Airport was not considered in this pilot research. Nevertheless, it has been shown that out of 1,121,096 persons living in 3,297 Output Areas for which the calculated noise levels are valid, 82.1% of them (920,505 persons living in 2,710 OAs) are located within the area for which the LTDS data are available, as shown in Figure 1.

^{Figure 1. Output Areas around Heathrow Airport and hourly LAeq noise contours (source: ONS, LTDS and ERCD UK CAA)}

For this case study, the hourly LAeq noise contours (Figure 1) and noise levels for each location have been produced by the Environmental Research and Consultancy Department (ERCD) of the Civil Aviation Authority (CAA) based on the inputs provided by ANIMA research team. For that purpose, the UK aircraft noise contour model (ANCON 2.4) was used, which calculates the emissions and propagation of noise from arriving and departing air traffic according to ECAC.CEAC Doc 29 4th Edition Volume 2.

The average results of 1,000 iterations showed that a 10.9% increase in the number of people estimated to be highly annoyed by aircraft noise can be expected when daily movement of the population is considered, compared to the traditional approach of calculating noise exposure using only census data. This implies that people spend more time during the day in the areas most affected by aircraft noise, while there are less people in less affected areas around the airport. The absence of significant population movements during the night leads to a minor difference (-0.2%) in the number of people estimated to be highly sleep-disturbed by aircraft noise between the two approaches. Although this decrease suggests that during the night people tend to spend more time outside the areas affected by aircraft noise, such difference is negligible.

To assess how population movement patterns influence the changes in noise exposure on an individual level, detailed analysis conducted for L_den and L_night noise indicators showed that for some people these differences in noise exposure were much more pronounced. In the case of L_den (Figure 2), this noise impact during the whole day can be underestimated by up to 33.9 dB or overestimated by up to 35.4 dB, and the similar situation was observed for L_night indicator.

^{Figure 2. L_den difference between Dynamic and Reference scenario}

To better demonstrate the dynamics behind this novel approach, the 24 different hourly noise maps, including the estimated population change at each location, are combined and presented in the video below (Figure 3). This video illustrates the temporal and spatial variation in population around Heathrow airport. In addition, the change in the number of operations between each hour is clearly visible through the different shapes and areas that calculated noise contours take. As expected, the most minor changes are observed during night hours, when the traffic frequency is low as well as the movements of the people.

^{Figure 3. Simulation of dynamic noise maps for Heathrow airport}

When comparing the noise exposure of people travelling in and out of the aircraft noise affected areas, the main conclusion is that many of them experience noise levels different from that expected at their residential locations. This suggests that a more accurate noise mapping approach needs to include the consideration of daily mobility patterns when assessing population noise exposure.