A High-Precision Method for Evaluating Sector Capacity in Bad Weather Based on an Improved WITI Model

Abstract

The rapid development of the civil aviation industry has increased the pressure on airspace resources in China. The traditional sector capacity assessment method does not take into account the impact of bad weather, resulting in flight plans often deviating markedly from the predicted plans, causing flight delays and affecting the punctuality rate of flights. To solve this issue, we propose a novel evaluation method based on an improved Weather-Impacted Traffic Index (WITI) model to calculate sector capacity. The WITI model is optimized in order to calculate the weather-influence coefficients under different types of bad weather. These coefficients were also considered in a controller workload model. Finally, the model was trained using a deep-neural-network algorithm, which is combined with a linear regression algorithm to calculate sector capacity under different bad weather conditions. The novel approach leads to the output results being within a specified error range, which greatly improves their accuracy. This method was applied to the actual case data of Yinchuan Hedong International Airport to consider different types of bad weather and quantify their severity, which more specifically assesses the sector capacity under the condition of bad weather.

Keywords

• WITI model
• bad weather
• controller workload
• sector capacity
• deep neural network