Affiliation:
1. EUROCONTROL, Centre du Bois des Bordes CS 41005, 91222 Brétigny-sur-Orge, France
Abstract
Accurate prediction of traffic demand and airport capacity plays a crucial role in minimising ground delays and airborne holdings. This paper focuses on the latter aspect. Adverse weather conditions present significant challenges to airport operations and can substantially reduce capacity. Consequently, any predictive model, regardless of its complexity, should account for weather conditions when estimating the airport capacity. At present, the sole shared platform for airport capacity information in Europe is the EUROCONTROL Public Airport Corner, where airports have the option to voluntarily report their capacities. These capacities are presented in tabular form, indicating the maximum number of hourly arrivals and departures for each possible runway configuration. Additionally, major airports often provide a supplementary table showing the impact of adverse weather in a somewhat approximate manner (e.g., if the visibility is lower than 100 m, then arrival capacity decreases by 30%). However, these tables only cover a subset of airports, and their generation is not harmonised, as different airports may use different methodologies. Moreover, these tables may not account for all weather conditions, such as snow, strong winds, or thunderstorms. This paper presents a machine learning approach to learn mapping from weather conditions and runway configurations to the 99th percentile of the delivered throughput from historical data. This percentile serves as a capacity proxy for airports operating at or near capacity. Unlike previous attempts, this paper takes a novel approach, where a single model is trained for several airports, leveraging the generalisation capabilities of cutting-edge machine learning algorithms. The results of an experiment conducted using 2 years of historical traffic and weather data for the top 45 busiest airports in Europe demonstrate better alignment in terms of mean pinball error with the observed departure and arrival throughput when compared to the operational capacities reported in the EUROCONTROL Public Airport Corner. While there is still room for improvement, this system has the potential to assist airports in defining more reasonable capacity values, as well as aiding airlines in assessing the impact of adverse weather on their flights.
Subject
Atmospheric Science,Environmental Science (miscellaneous)
Reference20 articles.
1. Wang, Y. (2011, January 16–20). Prediction of weather impacted airport capacity using ensemble learning. Proceedings of the IEEE/AIAA 30th Digital Avionics Systems Conference, Seattle, WA, USA.
2. Wang, Y. (2012, January 14–18). Prediction of weather impacted airport capacity using RUC-2 forecast. Proceedings of the IEEE/AIAA 31st Digital Avionics Systems Conference (DASC), Williamsburg, VA, USA.
3. Dhal, R., Roy, S., Taylor, C.P., and Wanke, C.R. (2013, January 12–14). Forecasting Weather-Impacted Airport Capacities for Flow Contingency Management: Advanced Methods and Integration. Proceedings of the 2013 Aviation Technology, Integration, and Operations Conference, Los Angeles, CA, USA.
4. Kicinger, R., Chen, J.T., Steiner, M., and Pinto, J. (2014, January 13–17). Probabilistic Airport Capacity Prediction Incorporating Weather Forecast Uncertainty. Proceedings of the AIAA Guidance, Navigation, and Control Conference, National Harbor, MD, USA.
5. Airport Capacity Prediction with Explicit Consideration of Weather Forecast Uncertainty;Kicinger;J. Air Transp.,2016