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Data-driven airport management enabled by operational milestones derived from ADS-B messages

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  • Schultz, Michael
  • Rosenow, Judith
  • Olive, Xavier

Abstract

Standardized, collaborative decision-making processes have already been implemented at some network-relevant airports, and these can be further enhanced through data-driven approaches (e.g., data analytics, predictions). New cost-effective implementations will also enable the appropriate integration of small and medium-sized airports into the aviation network. The required data can increasingly be gathered and processed by the airports themselves. For example, Automatic Dependent Surveillance-Broadcast (ADS-B) messages are sent by arriving and departing aircraft and enable a data-driven analysis of aircraft movements, taking into account local constraints (e.g., weather or capacity). Analytical and model-based approaches that leverage these data also offer deeper insights into the complex and interdependent airport operations. This includes systematic monitoring of relevant operational milestones as well as a corresponding predictive analysis to estimate future system states. In fact, local ADS-B receivers can be purchased, installed, and maintained at low cost, providing both very good coverage of the airport apron operations (runway, taxi system, parking positions) and communication of current airport performance to the network management. To prevent every small and medium-sized airport from having to develop its own monitoring system, we present a basic concept with our approach. We demonstrate that appropriate processing of ADS-B messages leads to improved situational awareness. Our concept is aligned with the operational milestones of Eurocontrol’s Airport Collaborative Decision Making (A-CDM) framework. Therefore, we analyze the A-CDM airport London–Gatwick Airport as it allows us to validate our concept against the data from the A-CDM implementation at a later stage. Finally, with our research, we also make a decisive contribution to the open-data and scientific community.

Suggested Citation

  • Schultz, Michael & Rosenow, Judith & Olive, Xavier, 2022. "Data-driven airport management enabled by operational milestones derived from ADS-B messages," Journal of Air Transport Management, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:jaitra:v:99:y:2022:i:c:s0969699721001459
    DOI: 10.1016/j.jairtraman.2021.102164
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    References listed on IDEAS

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    1. Herrema, Floris & Curran, Ricky & Hartjes, Sander & Ellejmi, Mohamed & Bancroft, Steven & Schultz, Michael, 2019. "A machine learning model to predict runway exit at Vienna airport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 329-342.
    2. Jaehyung An & Alexey Mikhaylov & Nikita Moiseev, 2019. "Oil Price Predictors: Machine Learning Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 1-6.
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    Cited by:

    1. Lai, Hsueh-Yi, 2023. "Breakdowns in team resilience during aircraft landing due to mental model disconnects as identified through machine learning," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

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