| Autor: |
Rohit Mital, Michael Albert, Bruce H. Wilson, Ramakrishna Raju, Kamala Shetty |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC). |
| DOI: |
10.1109/dasc52595.2021.9594389 |
| Popis: |
In response to the needs of various stakeholders, the FAA has developed an automated tool that provides values for expected next day airport runway configurations and their respective arrival and departure rates. The arrival and departure rates at an airport define the capacity of an airport and as such are a critical piece in determining whether not a capacity/demand imbalance is expected. A forecast of these imbalances facilitates next-day planning and provides to stakeholders transparency regarding potential traffic constraints. The use case that motivated the work described in this paper is to develop various machine-learning (ML) models and compare their performance against the current automated tool, which finds operational periods with similar facility (airport) weather and uses the most frequently occurring runway configuration and rates as the capacity forecast for the next day. Extensive model development across five airports and four years of operational data and forecasted weather showed that the ML models consistently had higher accuracy than the automated tool. Among the ML models, superior options were found, but a single best model across all airports and predictions was not. In a follow-on effort, the authors hope to explore opportunities for working with the user community, incorporate their feedback, and determine if one or more ML models can be used in practice. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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