A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data

The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry during the winter seasons. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classifcation model to predict slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and data from runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. To evaluate the performance of the models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy when domain knowledge is used in the variable extraction. The XGBoost models are combined with SHAP (SHapley Additive exPlanations) approximations to provide a comprehensible decision support system for airport operators and pilots, which can contribute to safer and more economic operations of airport runways

Importance Measures for Multicomponent Binary Systems

In this paper we review the theory of importance measures for multicomponent binary systems starting out with the classical Birnbaum measure. We then move on to various time independent measures for systems which do not allow repairs including the Barlow and Proschan measure and the Natvig type 1 measure. For the case with repairs we discuss a measure suggested by Barlow and Proschan along with some new suggestions. We also present some new results regarding importance measures for sets of components. In particular we present a generalization and a new representation of the Natvig type 1 set importance measure. We also indicate how the set measures can be extended to the case with repairs

On regularity, amenability and optimal factoring strategies for reliability computations

In the present paper we investigate three classes of reliability systems: regular clutters, total amenable clutters and clutters where a certain factoring strategy for computing reliability is in a sense optimal. In the first part of the paper it is shown that the class of regular clutters is strictly contained in the class of total amenable clutters, while the class of total amenable clutters is strictly contained in the third class. In the second part of the paper we present some important new properties of regular clutters. Especially, we provide two new types of regular clutters related to undirected networks

Trends and Local Effects in Aviation Accident Rates Related to Deregulation

When analyzing flight accident data over some period of time, it is clear that the rates of serious accidents per year show a steady decline. For a recent analysis of this see e.g., Landsberg [2]. However, by focusing only on long term trends it is easy to overlook local effects like sudden drops or increases in the accident rates. When using standard statistical methods like regression analysis, local effects have a tendency to be reduced to a few scattered outliers. As a result important issues affecting the accident rates may not be addressed. In this paper we shall study the accident rates for general aviation in USA for the period 1960-2003. In particular we will focus on a special period in the years around 1980 when the aviation business was deregulated. We will show that during this short period the accident rates were significantly lower than one could expect