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Online Stochastic Prediction of Mid-Flight Aircraft Trajectories

Published: 05 November 2019 Publication History

Abstract

Online trajectory prediction is central to the function of air traffic control of improving the flow of air traffic and preventing collisions, particularly considering the ever-increasing number of air travellers. In this paper, we propose an approach to predict the mid-flight trajectory of an aircraft using models learned from historical trajectories. The main idea is based on Hidden Markov Models, representing the location of aircraft as states and weather conditions as observations. Using our approach, one is able to make predictions of future positions of currently mid-flight aircraft for each minute into the future, optionally concatenating these positions to form the remaining predicted trajectory of an aircraft. We evaluated the effectiveness of the proposed approach using a dataset of historical trajectories for flights over the USA. Using prediction accuracy metrics from the aviation domain, we demonstrated that our approach could accurately predict trajectories of mid-flight aircraft, achieving an effectiveness improvement of 26% in horizontal error and 32% in vertical error over baseline models with virtually no loss in prediction efficiency.

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  • (2021)Data Analytics for Air Travel Data: A Survey and New PerspectivesACM Computing Surveys10.1145/346902854:8(1-35)Online publication date: 4-Oct-2021

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cover image ACM Conferences
IWCTS'19: Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science
November 2019
89 pages
ISBN:9781450369671
DOI:10.1145/3357000
© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 05 November 2019

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Author Tags

  1. Air Traffic Management
  2. Aircraft Trajectory Prediction
  3. Markov Models
  4. Time Series

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Overall Acceptance Rate 42 of 57 submissions, 74%

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  • (2021)Data Analytics for Air Travel Data: A Survey and New PerspectivesACM Computing Surveys10.1145/346902854:8(1-35)Online publication date: 4-Oct-2021

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