Dataset Open Access

Dataset defining representative route network for GLOWOPT market segments

Radhakrishnan, Kaushik

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

Files (8.5 MB)
Name Size
Forecast_Network2019.csv
md5:dde585a2cb1c132a3cdfd82ab8491fe3
765.6 kB Download
Forecast_Network2019.mat
md5:8d7afe2d60602255a09289eacc383437
88.2 kB Download
Forecast_Network2025.csv
md5:97bb825c2d1a28a95d707bea67f08434
882.5 kB Download
Forecast_Network2025.mat
md5:e9d4a62c9d8180041950bee29d311b75
102.3 kB Download
Forecast_Network2030.csv
md5:517020524477478e08fdba969fb37098
998.4 kB Download
Forecast_Network2030.mat
md5:ea47e0fc921899f1f328bdd5433a1c98
116.5 kB Download
Forecast_Network2035.csv
md5:eea511910b4cbfbbd53fe24e9ebc1ea3
1.1 MB Download
Forecast_Network2035.mat
md5:3611895f61d39ceae102657a80d84747
102.1 kB Download
Forecast_Network2040.csv
md5:817c73541cb921cddeb0771b8c01b7a1
1.2 MB Download
Forecast_Network2040.mat
md5:cdfbbf9ebf3220ed0b725cab62a29a5a
141.8 kB Download
Forecast_Network2045.csv
md5:de7889614be61a54b937ecd01dcadd0d
1.3 MB Download
Forecast_Network2045.mat
md5:625066cf4ed4f8a254610da87e0c1560
152.1 kB Download
Forecast_Network2050.csv
md5:81c08d16935dd15895b8db5685c1d345
1.3 MB Download
Forecast_Network2050.mat
md5:f686ae6dc2dbe30be00ae5af5ae9eab7
163.0 kB Download
Indexing_Mat_Dataset.PNG
md5:262906f972f2ab9499f74de2ce47c537
29.7 kB Download
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