Dataset defining representative route network for GLOWOPT market segments

For calculating the GLOWOPT representative route network, a forecast model chain was used. The model was calibrated with 2019 flight movement data (unimpeded by COVID-19) and provided forecasted aircraft movements from the year 2019 (~2020) to 2050 in 5 years intervals. Two formats of datasets are generated with the results of the forecast model chain, a csv file format and 4-dimensional array supported with MATLAB (.mat). CSV Datasets For each forecasted year a csv file is generated with the information on the origin-destination (OD) airports IATA codes, region, latitude and longitude of OD pair, representative aircraft type along with the aircraft category , the average load factor and finally, the distance between the OD pair. The airports worldwide are sub-dived into nine regions namely Africa, Asia, Caribbean, Central America, Europe, Middle East, North America, Oceania and South America. There are total of seven datasets, one for each forecasted year i.e. for years 2019 (~2020), 2025, 2030, 2035, 2040, 2045 and 2050. Description of the data labels: Origin- Origin airport IATA code Origin_Region- Region of the Origin Airport Origin_Latitude- Latitude of the Origin Airport Origin_Longitude- Longitude of the Origin Airport Destination- Destination airport IATA code Destination_Region- Region of the Destination Airport Destination_Latitude- Latitude of the Destination Airport Destination_Longitude- Longitude of the Destination Airport AcType- Representative aircraft type Load_Factor- Average load factor per flight Yearly_Frequency- Total aircraft movements per annum RefACType- Aircraft Category based on number of seats (Category 6 represents aircraft with seats 252-301 and category 7 represents aircraft with seats greater than 302.) Distance- Great circle distance between Origin and Destination in Km. MATLAB Datasets The dataset generated with MATLAB is a 4-dimensional array with the extension *.mat. The first dimension is the region of the origin airport and subsequently the second dimensions contains the region of the destination airport. The third and fourth dimension are the aircraft category based on seat numbers and the categorized great circle distances. The information received therein is a 1X1 cell with the IATA codes of the OD pairs, frequency and great circle distance in Km. The 4D array is categorised such that the user can select the route segment specific to a region or a combination of regions. The range categorisation in combination with an aircraft category additionally offers the user the possibility to select routes depending on their great circle distances. The ranges are categorised to represent very short range (0-2000 km), short range (2000-6000 km), medium range (6000-10000 km) and long range (10000 – 15000 km). Indexing based on the categorisation of the 4D array dataset - Refer to file 'Indexing_MAT_Dataset.PNG' For example: To derive the OD pairs and yearly frequency of aircraft movements for routes which originate from Europe and are destined to Asia, operated with category 6 aircraft type and are separated by distances between 10,000 to 15,000 km: In MATLAB (Indexing based on file 'Indexing_MAT_Dataset.PNG' ): Route_Network (5,2,1,4), Description on Index: 5 – Europe: Origin Region 2 – Asia: Destination Region 1– Category 6: Aircraft Type 4 – 10000-15000 km: Range

为计算GLOWOPT典型航线网络，本研究采用了一套预测模型链。该模型以2019年（未受新冠疫情影响）的航班运行数据进行校准，并输出了2019年（约2020年）至2050年以5年为间隔的航空器运行量预测结果。本预测模型链生成了两种格式的数据集成果：逗号分隔值（CSV）文件格式，以及MATLAB支持的四维数组（.mat）格式。

### CSV数据集
针对每个预测年份，均生成一份CSV文件，其中包含起讫点（OD）机场的国际航空运输协会（IATA）代码、起讫机场所在区域、起讫机场的经纬度、典型航空器型号及航空器类别、平均客座率，以及起讫机场对之间的飞行距离。全球机场被划分为九个区域，分别为非洲、亚洲、加勒比海地区、中美洲、欧洲、中东、北美、大洋洲及南美洲。本数据集共包含7份数据文件，分别对应2019年（约2020年）、2025年、2030年、2035年、2040年、2045年及2050年共7个预测年份。

#### 数据标签说明
Origin：出发机场IATA代码
Origin_Region：出发机场所属区域
Origin_Latitude：出发机场纬度
Origin_Longitude：出发机场经度
Destination：到达机场IATA代码
Destination_Region：到达机场所属区域
Destination_Latitude：到达机场纬度
Destination_Longitude：到达机场经度
AcType：典型航空器型号
Load_Factor：航班平均客座率
Yearly_Frequency：年度航空器总运行量
RefACType：基于座位数划分的航空器类别（其中类别6代表座位数252~301的航空器，类别7代表座位数大于302的航空器）
Distance：起讫机场对之间的大圆距离，单位为千米

### MATLAB数据集
本数据集为后缀名为.mat的四维数组文件。第一维度代表出发机场所属区域，第二维度代表到达机场所属区域，第三维度为基于座位数划分的航空器类别，第四维度为分类后的大圆距离区间。该数据集内存储的信息为1×1元胞数组，包含起讫机场对的IATA代码、年度运行量及大圆距离（单位：千米）。该四维数组采用分类索引方式，使用者可根据特定区域或区域组合筛选航线片段；结合航空器类别与距离区间，还可进一步根据大圆距离范围筛选航线。距离区间划分为：超短途（0~2000 km）、短途（2000~6000 km）、中途（6000~10000 km）及长途（10000~15000 km）。

基于该四维数组数据集的索引规则，请参考文件Indexing_MAT_Dataset.PNG。
示例：若需获取从欧洲出发、抵达亚洲、由类别6航空器执飞、飞行距离处于10000~15000 km区间的航线的起讫机场对及年度运行量，在MATLAB中可通过以下索引实现（索引规则参考文件Indexing_MAT_Dataset.PNG）：
Route_Network(5,2,1,4)
索引说明：
5 — 欧洲：出发区域
2 — 亚洲：到达区域
1 — 类别6：航空器类别
4 — 10000~15000 km：距离区间