Decision support for air traffic management based on probabilistic weather forecasts

Abstract

European air traffic is continuously growing and now reaching the airspace capacity limits even in normal weather conditions. Adverse weather can considerably reduce airspace capacity, as a result suitable measures need to be taken to ensure air traffic is kept safe and reliable under such conditions. Air traffic regulations are the main measure taken to avoid that arrival traffic is exceeding the available airport capacity. Under such a regulation aircraft which would arrive in the regulated period are delayed on ground at the origin. The delay is not only inconvenient for passengers, but also a major cost factor for airlines. Weather forecasts are inherently uncertain. An adequate way to represent these uncertainties are probabilistic weather forecasts. For optimal implementation of such forecasts in air traffic management decisionmaking, a suitable decision support framework is required. Previous work on weather integration in air traffic management is reviewed as a basis for proposing a decision support framework for the air traffic flow and capacity management at airports. Two utility measures are developed for decision making. The first measure is based on a cost model which uses flight delay and flight diversions derived from air traffic simulations as input. The second measure represents the balance of traffic entering an airspace volume with the available capacity and is obtained from traffic demand and expected weather scenarios. In case studies the suitability of the utility measures for decision making is investigated. A simple cost-loss decision making approach and a more complex approach based on evaluating expected utility for a range of decisions and weather scenarios are applied. Results show that cost of delay is very sensitive to small variations of input data, while the traffic-capacity balance is more robust. i