Big data-driven prediction of airspace congestion
Air Navigation Service Providers (ANSP) worldwide have been making a considerable effort for the development of a better method to measure and predict aircraft counts within a particular airspace, also referred to as airspace density. An accurate measurement and prediction of airspace density is cru...
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Format | Journal Article |
Language | English |
Published |
13.10.2023
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Abstract | Air Navigation Service Providers (ANSP) worldwide have been making a
considerable effort for the development of a better method to measure and
predict aircraft counts within a particular airspace, also referred to as
airspace density. An accurate measurement and prediction of airspace density is
crucial for a better managed airspace, both strategically and tactically,
yielding a higher level of automation and thereby reducing the air traffic
controller's workload. Although the prior approaches have been able to address
the problem to some extent, data management and query processing of
ever-increasing vast volume of air traffic data at high rates, for various
analytics purposes such as predicting aircraft counts, still remains a
challenge especially when only linear prediction models are used.
In this paper, we present a novel data management and prediction system that
accurately predicts aircraft counts for a particular airspace sector within the
National Airspace System (NAS). The incoming Traffic Flow Management (TFM) data
is streaming, big, uncorrelated and noisy. In the preprocessing step, the
system continuously processes the incoming raw data, reduces it to a compact
size, and stores it in a NoSQL database, where it makes the data available for
efficient query processing. In the prediction step, the system learns from
historical trajectories and uses their segments to collect key features such as
sector boundary crossings, weather parameters, and other air traffic data. The
features are fed into various regression models, including linear, non-linear
and ensemble models, and the best performing model is used for prediction.
Evaluation on an extensive set of real track, weather, and air traffic data
including boundary crossings in the U.S. verify that our system efficiently and
accurately predicts aircraft counts in each airspace sector. |
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AbstractList | Air Navigation Service Providers (ANSP) worldwide have been making a
considerable effort for the development of a better method to measure and
predict aircraft counts within a particular airspace, also referred to as
airspace density. An accurate measurement and prediction of airspace density is
crucial for a better managed airspace, both strategically and tactically,
yielding a higher level of automation and thereby reducing the air traffic
controller's workload. Although the prior approaches have been able to address
the problem to some extent, data management and query processing of
ever-increasing vast volume of air traffic data at high rates, for various
analytics purposes such as predicting aircraft counts, still remains a
challenge especially when only linear prediction models are used.
In this paper, we present a novel data management and prediction system that
accurately predicts aircraft counts for a particular airspace sector within the
National Airspace System (NAS). The incoming Traffic Flow Management (TFM) data
is streaming, big, uncorrelated and noisy. In the preprocessing step, the
system continuously processes the incoming raw data, reduces it to a compact
size, and stores it in a NoSQL database, where it makes the data available for
efficient query processing. In the prediction step, the system learns from
historical trajectories and uses their segments to collect key features such as
sector boundary crossings, weather parameters, and other air traffic data. The
features are fed into various regression models, including linear, non-linear
and ensemble models, and the best performing model is used for prediction.
Evaluation on an extensive set of real track, weather, and air traffic data
including boundary crossings in the U.S. verify that our system efficiently and
accurately predicts aircraft counts in each airspace sector. |
Author | Ayhan, Samet Balvedi, Glaucia Leite, Alexandre de Azevedo, Felipe C. F de Oliveira, Ítalo Romani Costas, Pablo |
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BackLink | https://doi.org/10.48550/arXiv.2310.08982$$DView paper in arXiv |
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Snippet | Air Navigation Service Providers (ANSP) worldwide have been making a
considerable effort for the development of a better method to measure and
predict aircraft... |
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SubjectTerms | Computer Science - Artificial Intelligence Computer Science - Computational Engineering, Finance, and Science Computer Science - Systems and Control |
Title | Big data-driven prediction of airspace congestion |
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