Identification of Robust Terminal-Area Routes in Convective Weather

Abstract

Convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. Traffic Flow Management algorithms require reliable forecasts of route blockage to schedule and route traffic. This paper demonstrates how raw convective weather forecasts, which provide deterministic predictions of the Vertically Integrated Liquid (the precipitation content in a column of airspace) can be translated into probabilistic forecasts of whether or not a terminal-area route will be blocked.Given a flight route through the terminal-area, we apply techniques from machine learning to determine the likelihood that the route will be open in actual weather. The likelihood is then used to optimize terminalarea operations, by dynamically moving arrival and departure routes to maximize the expected capacity of the terminal area. Experiments using real weather scenarios on stormy days show that our algorithms recommend that a terminal-area route be modified 30% of the time, opening up 13% more available routes that were forecast blocked during these scenarios. The error rate is low, with only 5% of cases corresponding to a modified route being blocked in reality, while the original route is in fact open. In addition, for routes predicted to be open with probability 0.95 or greater by our method, 96% of these routes (on average over time horizon) are indeed open in the weather that materializes.

Estimativa do impacto das condições meteorológicas sobre a carga de trabalho dos controladores de tráfego aéreo na TMA-SP

“…Esse fato é semelhante ao que é estabelecido por DECEA (2014a, p. 15) como sobrecarga de trabalho não controlada. Assim, é possıvel perceber como a capacidade do espaço aeŕeo é impactada pelos movimentos das aeronaves em um determinado setor, em decorrencia de condições adversas de tempo e ações de gerenciamento de luxo, fato destacado principalmente nos trabalhos de Krozel et al (2011;2007), Krozel (2011), Michalek e Balakrishnan (2010;2009a;2009b), Song et al (2009), DeLaura et al (2008, Kuln (2008), DeLaura e Evans (2006), Weber et al (2007;, e Rhoda et al (2002), entre outros.…”

“…Os trabalhos realizados por Krozel et al (2011;2007), Krozel (2011), Michalek e Balakrishnan (2010;2009a;2009b), Song et al (2009), DeLaura et al (2008, Kuln (2008), DeLaura e Evans (2006), Weber et al (2007; e Rhoda et al (2002), entre vários outros, focam na identi icaçaõ e no emprego de tećnicas para se evitar as condições convectivas de tempo durante o voo.…”

Improving Operational Acceptability of Dynamic Weather Routes Through Analysis of Commonly Use Routings

“…Data mining and machine learning techniques have been applied to learn trends and correlations from historical data in many fields, and Air Traffic Management is no exception 10,11,12,13,14,15,16 . However, in order for data mining to be useful, it is critical that the correct data is used and that it is used in the correct form, requiring the application to be well understood.…”

Joint Optimal Pricing and Ordering Decisions for Seasonal Products with Weather-Sensitive Demand

“…Nikolopoulos and Fildes [19] consider the uncertainty that comes with short-term weather forecasts and study the exact effect of weather on weekly or daily sales, which has been given very little attention in the forecasting literature. Pfeil and Balakrishnan [20] think that convective weather is responsible for large delays and widespread disruptions in the U.S. National Airspace System, especially during summer. They demonstrate how raw convective weather forecasts, which provide deterministic predictions of the vertically integrated liquid, can be translated into probabilistic forecasts of whether or not a terminal area route will be blocked.…”