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<p>As the demand and complexity of air traffic increase, it becomes crucial to maintain
the safety and efficiency of the operations in airspaces, which, however, could lead to an
increased workload for Air Traffic Controllers (ATCs) and delays in their decision-making
processes. Although terminal airspaces are highly structured with the flight procedures such
as standard terminal arrival routes and standard instrument departures, the aircraft are
frequently instructed to deviate from such procedures by ATCs to accommodate given traffic
situations, e.g., maintaining the separation from neighboring aircraft or taking shortcuts to
meet scheduling requirements. Such deviation, called vectoring, could even increase the
delays and workload of ATCs. This thesis focuses on developing a framework for trajectory
pattern identification and classification that can provide ATCs, in vectored airspace, with
real-time information of which possible vectoring pattern a new incoming aircraft could
take so that such delays and workload could be reduced. This thesis consists of two parts,
trajectory pattern identification and trajectory pattern classification.
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<p>In the first part, a framework for trajectory pattern identification is proposed based on
agglomerative hierarchical clustering, with dynamic time warping and squared Euclidean
distance as the dissimilarity measure between trajectories. Binary trees with fixes that are
provided in the aeronautical information publication data are proposed in order to catego-
rize the trajectory patterns. In the second part, multiple recurrent neural network based
binary classification models are trained and utilized at the nodes of the binary trees to
compute the possible fixes an incoming aircraft could take. The trajectory pattern identifi-
cation framework and the classification models are illustrated with the automatic dependent
surveillance-broadcast data that were recorded between January and December 2019 in In-
cheon international airport, South Korea .
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| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15054126 |
| Date | 26 July 2021 |
| Creators | Chuhao Deng (11184909) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/TRAJECTORY_PATTERN_IDENTIFICATION_AND_CLASSIFICATION_FOR_ARRIVALS_IN_VECTORED_AIRSPACE/15054126 |
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