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Air Traffic 2019 for Recent Link Regression

Summary

Name: air-traffic-2019-rlr
Description: This dataset involves a transportation network domestic airports in US during 2019. Airports are represented as vertices and flights as edges. Each edge is associated with a timestamp (flight date) and a feature vector derived from weather conditions at the departure and arrival airports. Arrival delay normalized by flight duration serves as the edge target, reflecting the flight delay outcome.
Task: The task is predicting the flight delay given the source and destination airports in addition to their weather conditions at the time of scheduled departure of flight.
Date of Creation: 01.07.2024
Last Update: 01.07.2024
Original Sources: flights, weather
Contact Information: email
License: CC BY 4.0

Statistics

Category	Data
Number of Nodes	274
Number of Edges	484,551
Number of Node Features	0
Number of Edge Features	20
Number of Timestamps	181

Download

Format: Compressed data.pt which involves a python dictionary as follows:

data = {
  "node_attr": None,
  "edge_index": torch.LongTensor,
  "edge_time": torch.FloatTensor,
  "edge_attr": torch.FloatTensor,
  "edge_label": torch.FloatTensor,
  "num_nodes": int
}

Size: 7.4 MB
Location: https://huggingface.co/datasets/ca-aird/airtraffic2019/blob/main/data.zip

Citation

@article{,
    title={Benchmarking Edge Regression on Temporal Networks},
    author={Muberra Ozmen and Florence Regol and Thomas Markovich},
    journal={X},
    volume={X},
    number={X},
    pages={X},
    year={X},
    publisher={X}
}

Preprocessing

The Bureau of Transportation Statistics (BTS), part of the U.S. Department of Transportation, provides datasets on domestic flight performance by major airlines. The original flight records are composed of following fields:

Field	Description	Usage
`Origin`	Origin IATA (International Air Transport Association) airport code.	Used as source node
`Dest`	Destination IATA code.	Used as destination node
`Date`	Scheduled date.	Used as edge time
`ArrTime`	Actual arrival time. Used to calculate edge target such that (`ArrTime` - `CRSArrTime`) / (`CRSArrTime` - `CRSDepTime`).	Used to calculate edge target
`CRSArrTime`	Scheduled arrival time. Used to calculate edge target such that (`ArrTime` - `CRSArrTime`) / (`CRSArrTime` - `CRSDepTime`).	Used to calculate edge target
`CRSDepTime`	Scheduled departure time. Used to calculate edge target such that (`ArrTime` - `CRSArrTime`) / (`CRSArrTime` - `CRSDepTime`).	Used to calculate edge target

Weather conditions at scheduled departure times were integrated from the Open-Meteo.com API. This included data on daily precipitation, maximum and minimum air temperatures, and wind speeds at both departure and arrival airports. The weather conditions are summarized by following parameters for each flight:

Field	Description	Usage
`dest_temperature_2m_max`	Maximum daily air temperature at 2 meters above ground at destination.	Used as edge feature
`dest_temperature_2m_min`	Minimum daily air temperature at 2 meters above ground at destination.	Used as edge feature
`dest_temperature_2m_mean`	Mean daily air temperature at 2 meters above ground at destination.	Used as edge feature
`dest_precipitation_sum`	Sum of daily precipitation at destination (including rain, showers, and snowfall).	Used as edge feature
`dest_rain_sum`	Sum of daily rain at destination.	Used as edge feature
`dest_snowfall_sum`	Sum of daily snowfall at destination.	Used as edge feature
`dest_wind_speed_10m_max`	Maximum wind gusts at destination.	Used as edge feature
`dest_wind_gusts_10m_max`	Maximum wind speed at destination.	Used as edge feature
`dest_wind_direction_10m_dominant`	Dominant wind direction at destination.	Used as edge feature
`origin_temperature_2m_max`	Maximum daily air temperature at 2 meters above ground at origin.	Used as edge feature
`origin_temperature_2m_min`	Minimum daily air temperature at 2 meters above ground at origin.	Used as edge feature
`origin_temperature_2m_mean`	Mean daily air temperature at 2 meters above ground at origin.	Used as edge feature
`origin_precipitation_sum`	Sum of daily precipitation at origin (including rain, showers, and snowfall).	Used as edge feature
`origin_rain_sum`	Sum of daily rain at origin.	Used as edge feature
`origin_snowfall_sum`	Sum of daily snowfall at origin.	Used as edge feature
`origin_wind_speed_10m_max`	Maximum wind gusts at origin.	Used as edge feature
`origin_wind_gusts_10m_max`	Maximum wind speed at origin.	Used as edge feature
`origin_wind_direction_10m_dominant`	Dominant wind direction at origin.	Used as edge feature

Notes

Acknowledgements:
- All funding was provided by Block Inc.
References:
- Trivedi, P. (2021). Flight Delay and Causes. Retrieved from https://www.kaggle.com/datasets/
- Zippenfenig, P. (2023). Open-Meteo.com Weather API (Version 1.0) [Software]. Zenodo.

Author Statement

I, Muberra Ozmen, declare that I bear full responsibility for the dataset described herein, including its contents and compliance with applicable laws and regulations. By providing access to this dataset, I confirm that it is released under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Users are free to:

Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following conditions:

Attribution — appropriate credit must be given to the associated publication indicating if any changes were made. This information should be provided in a manner that is reasonable given the medium, means, and context in which the dataset is shared.

For any use or redistribution of the dataset not permitted under this license, explicit permission from the dataset's creator is required.

The dataset will be hosted on a secure platform that ensures continuous access to the data. We have chosen Hugging Face for its robust infrastructure and capability to handle large datasets. Access to the dataset will be facilitated through a curated interface, providing users with efficient search and retrieval functionalities.

Licensing: The dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to freely use, share, and adapt the material, provided appropriate credit is given to the dataset's creator.

Maintenance: Regular maintenance of the dataset and its hosting platform will be conducted to ensure data integrity, security, and accessibility. Updates to the dataset, if any, will be promptly integrated into the platform to reflect the most current information available.