Abstract

The exponential increase in aviation activity and air traffic in recent decades has raised several public health issues. One of the critical public health concerns is runway safety and the increasing demand for airports without accidents. In addition to threatening human lives, runway accidents are often associated with severe environmental and pollution consequences. In this study, a three-step approach is used for runway risk assessment considering probability, location, and consequences of accidents through advanced statistical methods. This study proposes novel models for the implementation of these three steps in Iran. Data on runway excursion accidents were collected from several countries with similar air accident rates. The proposed models empower engineers to advance an accurate assessment of the accident probability and safety assessment of airports. For in-service airports, it is possible to assess existing runways to remove obstacles close to runways if necessary. Also, the proposed models can be used for preliminary evaluations of developing existing airports and the construction of new runways. Document type: Article

Abstract

The Single Europe Sky Air Traffic Management Research (SESAR) program develops and implements innovative technological and operational solutions to modernize European air traffic management and to eliminate the negative environmental impacts of aviation activity. This article presents our developments within the SESAR Solution &ldquo Safety Support Tools for Avoiding Runway Excursions&rdquo . This SESAR Solution aims to mitigate the risk of runway excursion, to optimize airport operation management by decreasing the number of runway inspections, to make chemical treatment effective with respect to the environment, and to increase resilience, efficiency and safety in adverse weather situations. The proposed approach is based on the enhancement of runway surface condition awareness by integrating data from various sources. Dangerous windy conditions based on Lidar measurements are also discussed as another relevant factor in relation to runway excursions. The paper aims to explore four different data mining methods to obtain runway conditions from the available input data sources, examines their performance and discusses their pros and cons in comparison with a rule-based algorithm approach. The output of the SESAR Solution is developed in compliance with the new Global Reporting Format of the International Civil Aviation Organization for runway condition description to be valid from 2020. This standard is expected to provide concerned stakeholders with more precise information to enhance flight safety and environmental protection. Document type: Article

Abstract

Runway excursion (RE) is recognized as one of the main Airport Safety Issues. It can be defined as an event in which an aircraft veers-off or overruns the runway surface during either takeoff or landing. In this paper an application of Bayesian Belief Network (BBN) method on RE events is presented. BBN belongs to quantitative class of causal methods. It estimates the risk of incident or accident according to estimation of probability of occurrence of each cause of an event. It might be restricted to pure statistical analysis based on the available data or combine these data with expert judgment (personal beliefs) on the accident causes. The aim of this paper is to identify potential causal factors, analyze and determine the probability of their realization. In order to illustrate BBN application on RE, two models were developed: one for landing and other for take-off. Based on the Ishikawa diagram method, causal factors have been identified, and then through a qualitative BBN model, their interdependencies are presented. The quantification of the model is accomplished by combining statistical data with the expert beliefs. Sensitivity analysis has shown what are the most critical causal factors whose knowledge allows developing certain measures to reduce the risk of RE. Document type: Article