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). <strong>CSV Datasets</strong> 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. <strong>Description of the data labels:</strong> <strong>Origin-</strong> Origin airport IATA code <strong>Origin_Region-</strong> Region of the Origin Airport <strong>Origin_Latitude-</strong> Latitude of the Origin Airport <strong>Origin_Longitude-</strong> Longitude of the Origin Airport <strong>Destination-</strong> Destination airport IATA code <strong>Destination_Region-</strong> Region of the Destination Airport <strong>Destination_Latitude-</strong> Latitude of the Destination Airport <strong>Destination_Longitude-</strong> Longitude of the Destination Airport <strong>AcType- </strong>Representative aircraft type <strong>Load_Factor- </strong>Average load factor per flight <strong>Yearly_Frequency-</strong> Total aircraft movements per annum <strong>RefACType-</strong> 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.) <strong>Distance-</strong> Great circle distance between Origin and Destination in Km. <strong>MATLAB Datasets</strong> 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). <strong>Indexing based on the categorisation of the 4D array dataset</strong> - 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: <strong>In MATLAB (Indexing based on file </strong> 'Indexing_MAT_Dataset.PNG' <strong>): </strong> <strong>Route_Network (5,2,1,4), </strong> 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代表性航线网络，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）的航空器起降架次数据进行校准，可输出2019年（约2020年）至2050年、每5年为间隔的航班起降架次预测数据。研究基于该预测模型链的结果生成了两种格式的数据集：逗号分隔值（CSV）文件，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，其中包含起讫机场（Origin-Destination，OD）对的IATA（International Air Transport Association）代码、起讫机场所在区域、起讫机场的经纬度、代表性机型及机型类别、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9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。本次数据集共包含7份数据，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-**：起飞机场IATA代码 - **Origin_Region-**：起飞机场所属区域 - **Origin_Latitude-**：起飞机场纬度 - **Origin_Longitude-**：起飞机场经度 - **Destination-**：目的机场IATA代码 - **Destination_Region-**：目的机场所属区域 - **Destination_Latitude-**：目的机场纬度 - **Destination_Longitude-**：目的机场经度 - **AcType-**：代表性机型 - **Load_Factor-**：单航班平均客座率 - **Yearly_Frequency-**：年度总航空器起降架次 - **RefACType-**：基于座位数划分的机型类别（第6类代表座位数252~301的航空器，第7类代表座位数超过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 MATLAB格式数据集为扩展名为*.mat的四维数组。第一维度为起飞机场所属区域，第二维度为目的机场所属区域，第三维度为基于座位数划分的机型类别，第四维度为分类后的大圆距离区间。数组内存储的信息为1×1元胞数组，包含起讫机场对的IATA代码、航班频次及大圆距离（单位：千米）。该四维数组经过分类设计，用户可选取特定区域或区域组合对应的航线段；结合机型类别与距离区间，用户还可根据大圆距离筛选目标航线。距离区间划分为：极短程（0~2000 km）、短程（2000~6000 km）、中程（6000~10000 km）及远程（10000~15000 km）。 #### 四维数组数据集索引规则 详见文件`Indexing_MAT_Dataset.PNG`。 示例：若需获取从欧洲始发、飞往亚洲、由第6类机型执飞且大圆距离介于10000~15000 km的航线的起讫机场对及年度航班起降架次，在MATLAB中可通过以下索引实现：`Route_Network(5,2,1,4)`，索引说明如下： 5 — 欧洲：起飞机场所属区域 2 — 亚洲：目的机场所属区域 1 — 第6类机型：航空器类别 4 — 10000~15000 km：距离区间