Air traffic assignment to reduce population noise exposure using activity-based approach (Belgrade Nikola Tesla airport case study)

This dataset is related to the paper entitled "Air traffic assignment to reduce population noise exposure using activity-based approach". The aim of the research is to develop a mathematical model and a heuristic algorithm that could assign aircraft to departure and arrival routes so that number of people exposed to noise is as low as possible, taking into account temporal and spatial variations in population in an airport’s vicinity. The approach was demonstrated on Belgrade Nikola Tesla airport to show the benefits of the proposed model. All the data described here support the above-mentioned research and give the detailed description of the calculations conducted in order to backup the obtained results. The detailed description of each file is given below. 1. Air Traffic Data Realistic air traffic data was collected for one summer day with relatively heavy traffic (September 16th, 2016) for Belgrade Nikola Tesla airport. It includes information about origin and destination, aircraft type, actual take-off time (ATOT), arrival time, runway in use, and operation type (take-off or landing). In addition, air traffic assignment for Base Case and Heuristic algorithm Scenario is given and it includes SID/STAR route ID assigned to each operation. 2. Aircraft type Different aircraft types used in this research are presented here, along with the aircraft code for Integrated Noise Model (INM). 22 different aircraft types were classified into 11 groups since the sound exposure level (SEL) for some aircraft types differed by less than 1dB at the same location. 3. Calculated sound exposure level (SEL) data By using the Integrated Noise Model (INM) software, the sound exposure level (SEL) has been calculated for 11 aircraft types in fleet mix, flying over the 27 different departure and arrival routes, for each of the 17 different location separately. 4. Daily migrations According to the 2011 Census methodology, daily migrants are those persons who work or go to school/university outside the place of their usual residence, but who return on a daily basis or several times a week. In accordance with our request to the Statistical Office of the Republic of Serbia, a special processing of 2011 Census data was performed to meet our requirements for this research. 5. Departure and arrival routes Based on the radar data, all trajectories were classified into 27 routes, including 13 departure routes (seven from runway 12 and six from runway 30) and 14 arrival routes (seven from both runways). 6. Fuel Consumption Calculations Detailed fuel consumption calculations are given using the EMEP/EEA air pollutant emission inventory guidebook – 2016. 7. Locations Latitude, longitude, height and population is given for each of the 17 locations. 8. Population Noise Exposure Calculations The number of people annoyed by aircraft noise has been calculated by using the formula given by the European Commission and by using Noise Annoyance Index (SPI).

本数据集关联于题为《基于活动分析法降低人群噪声暴露的空中交通分配》的学术论文。本研究旨在构建数学模型与启发式算法，以分配航空器起降航线，在考虑机场周边人口时空分布差异的前提下，最小化受噪声暴露的人群数量。该方法已在贝尔格莱德尼古拉·特斯拉机场（Belgrade Nikola Tesla Airport）进行验证，以展示所提模型的应用优势。本数据集收录的全部数据均支撑上述研究，并详细记录了为验证所得结果所开展的计算过程。下文将逐一说明各文件的详细信息：
1. 空中交通数据：针对贝尔格莱德尼古拉·特斯拉机场2016年9月16日（交通相对繁忙的一个夏日）收集了写实的空中交通数据，内容涵盖起降航班的起讫点、机型、实际起飞时间（Actual Take-Off Time, ATOT）、到达时间、使用跑道及运行类型（起飞或降落）。此外，本数据集还提供了基准场景与启发式算法场景下的空中交通分配方案，包含为各运行航班分配的标准仪表离场（Standard Instrument Departure, SID）/标准仪表进场（Standard Terminal Arrival Route, STAR）航线编号。
2. 机型信息：本研究使用的各类机型及其集成噪声模型（Integrated Noise Model, INM）对应代码均在此列出。由于部分机型在同一地点的声暴露级（Sound Exposure Level, SEL）差异小于1分贝，故将22种不同机型划分为11个组别。
3. 计算所得声暴露级（Sound Exposure Level, SEL）数据：借助集成噪声模型（INM）软件，本研究针对机队组合中的11种机型，分别在27条不同起降航线上飞行的场景下，针对17个不同监测点位逐一计算了声暴露级（SEL）。
4. 每日通勤人口：依据2011年人口普查的统计方法，每日通勤人口指日常在常住居住地以外工作或就学，但每日或每周数次返回居住地的人群。本研究向塞尔维亚共和国国家统计局提出申请后，对2011年人口普查数据进行了专项处理，以适配本研究的需求。
5. 起降航线：基于雷达数据，所有飞行轨迹被划分为27条航线，其中包含13条离场航线（7条来自12号跑道，6条来自30号跑道）与14条进场航线（两条跑道各分配7条）。
6. 燃油消耗计算：依据《EMEP/EEA空气污染物排放清单指南——2016》，本研究给出了详细的燃油消耗计算过程。
7. 监测点位信息：17个监测点位的纬度、经度、海拔高度及对应人口数均已在此列出。
8. 人群噪声暴露计算：本研究采用欧洲委员会公布的计算公式与噪声烦恼指数（Noise Annoyance Index, SPI），计算了受航空器噪声干扰的人群数量。