From aircraft tracking data to network delay model: A data-driven approach considering en-route congestion

Yu Lin, Lishuai Li, Pan Ren, Yanjun Wang, W. Y. Szeto

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En-route congestion causes delays in air traffic networks and will become more prominent as air traffic demand will continue to increase yet airspace volume cannot grow. However, most existing studies on flight delay modeling do not consider en-route congestion explicitly. In this study, we propose a new flight delay model, Multi-layer Air Traffic Network Delay (MATND) model, to capture the impact of en-route congestion on flight delays over an air traffic network. This model is developed by a data-driven approach, taking aircraft tracking data and flight schedules as inputs to characterize a national air traffic network, as well as a system-level model approach, modeling the delay process based on queueing theory. The two approaches combined make the network delay model a close representation of reality and easy-to-implement for what-if scenario analysis. The proposed MATND model includes 1) a data-driven method to learn a network composed of airports, en-route congestion points, and air corridors from aircraft tracking data, 2) a stochastic and dynamic queuing network model to calculate flight delays and track their propagation at both airports and in en-route congestion areas, in which the delays are computed via a space–time decomposition method. Using one month of historical aircraft tracking data over China's air traffic network, MATND is tested and shows to give an accurate quantification of delays of the national air traffic network. “What-if” scenario analyses are conducted to demonstrate how the proposed model can be used for the evaluation of air traffic network improvement strategies, where the manipulation of reality at such a scale is impossible. Results show that MATND is computationally efficient, well suited for evaluating the impact of policy alternatives on system-wide delay at a macroscopic level.

Original languageEnglish
Article number103329
Number of pages40
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - 2021


  • En-route congestion
  • Flight delay
  • Queuing network
  • Trajectory clustering


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