Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques
Open accessAbstract
Accurate taxi time prediction can be used for more efficient runway scheduling to increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. This paper describes two different approaches to predicting taxi times, which are a data-driven analytical method using machine learning techniques and a fast-time simulation-based approach. These two taxi time prediction methods are applied to realistic flight data at Charlotte Douglas International Airport (CLT) and assessed with actual taxi time data from the human-in-the-loop simulation for CLT airport operations using various performance measurement metrics. Based on the preliminary results, we discuss how the taxi time prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast-time simulation model for implementing it with an airport scheduling algorithm in real-time operational environment.
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ML can help predict runway demand and aircraft taxi time to reduce excess fuel burned due to airport congestion.
“ML can help predict runway demand and aircraft taxi time in order to reduce the excess fuel burned in the air and on the ground due to congestion in airports [379,474].”
From Tackling climate change with machine learning (2023) · cites this paper as [474]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Lee, H., Malik, W., Zhang, B., Nagarajan, B., & Jung, Y. C.
- Affiliations
- University of California, Santa Cruz (US)
- American Airlines (United States) (US)
- Ames Research Center (US)
- Venue
- 15th AIAA Aviation Technology, Integration, and Operations Conference
- Published
- 2015-06-18
- OpenAlex
- W2323439506
- Cited by
- 33
- References
- 28
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 8 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 2 · 2019: 4 · 2018: 4 · 2017: 1 · 2016: 2
- FWCI
- 4.05
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- AIAA Aviation Technology, Integration, and Operations Conference (ATIO) (publishedVersion)
- NASA STI Repository (National Aeronautics and Space Administration) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Railway Systems and Energy Efficiency
- Keywords
- airport surface operations, runway scheduling, runway throughput, fuel consumption reduction, fast-time simulation, machine learning prediction, human-in-the-loop simulation, airport operations, Charlotte Douglas International Airport
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