Runway Incursions Research Articles

Utilizing the Systematic Literature Review in Aviation: A Case Study for Runway Incursions

This research presents the process for a systematic literature review, and demonstrates the application of this process using a case study that investigates the factors that contribute to runway incursions (RIs). Runway safety is a top priority in aviation ( Federal Aviation Administration [FAA] , 2012a). One factor that threatens runway safety is RIs. In the United States, an average of three RIs occurs daily. Although the reduction of RIs has been a topic of interest for many years, the number of RIs has been increasing since 2012. In this paper, a systematic literature review approach was used to synthesize the results of previous studies in a systematic way, identify contributing factors for RIs , and provide insight regarding the causes for RIs . One hundred and thirty-four articles were identified in the initial literature search from 22 databases, and 22 articles were analyzed after using filtering criteria. As a result of this analysis, six categories of contributing factors to RIs were identified: human factors, airport geometry, technical factors, airport characteristics, environmental factors, and organizational factors. Recommendations for reduction of RIs and suggestions for further studies were presented based on these factors.

Rapid detection and social media supervision of runway incursion based on deep learning

In order to solve the problem of runway incursion, which is a serious threat to the safety of the aviation industry, we analyse social media data which shows people's concern about safety of the aviation industry, we analyse social media data which shows people's concern about airport runway incursion. We take the target of aircrafts and vehicles on the runway as the research objects, and put forward the airport target detection method based on optimised YOLO framework. The simulation experiment is carried out by constructing the airport simulation environment. We study the airport target detection in single target, multi-target and extreme environment target, and focus on the influence of the overlooking angle of the monitoring system on the detection results. We selected Tiny YOLO and Faster R-CNN as the control group to demonstrate the performance of the optimised YOLO detector at speed. The experimental result shows that the airport target detection based on optimised YOLO has excellent fastness and accuracy.

Rapid detection and social media supervision of runway incursion based on deep learning

Rapid detection and social media supervision of runway incursion based on deep learning

Influencing factors of runway incursion risk and their interaction mechanism based on DEMATEL-ISM

Influencing factors of runway incursion risk and their interaction mechanism based on DEMATEL-ISM

Risk Analysis of Runway Incursion Based on Fuzzy Bayesian Network

Runway incursion is a kind of unsafe flight incident which can easily lead to catastrophic collision of aircraft. It is of great significance to analyze the risk and improve the safety of air traffic management. Many factors affect the runway incursion unsafe event, and the analysis of influence degree of various factors has certain difficulty, and the Bayesian network is a kind of uncertainty causality model which can deal with uncertain problems and effectively express and merge multi-source information, therefore, this problem is studied by Bayesian network based on the actual operation data of some domestic airports. Bayesian network calculation model of runway incursion was built based on the analysis of factors by SHELLO model, calculated prior probability based on historical data, then the probability of the occurrence of runway incursion affected by factors was obtained by the model, it turns out L-S cause 66% of the runway incursions, the importance degree of factors and measures are sorted in the final.

Geo-fencing to secure airport perimeter against sUAS

PurposeSmall unmanned aircraft systems (sUAS) are becoming increasingly popular among hobbyists, and with this popularity there comes the risk of runway incursion between a commercial aircraft and sUAS around airports. To keep airports safe and secure, the purpose of this paper is to propose a module, called the Airport Secure Perimeter Control System, that can be attached to every hobbyist’s sUAS for the purpose of notification and prevention.Design/methodology/approachUpon startup, the module connects to a database containing the central coordinates of every airport in the USA. A five-mile critical radius plus an additional one-mile buffer region is established around each point. The buffer region is created in order to inform the user that he/she is approaching a safe airspace and needs to take corrective action. Once the five-mile zone has been breached, autopilot software takes over the manual controls, and the sUAS is landed in a controlled manner, while the user still has lateral control of the vehicle in order to avoid any potential hazards below it. Then, both operator and airport receive messages about the event.FindingsTo demonstrate the proposed design, a prototype was developed that successfully implemented this system, and was formally tested within a controlled environment.Originality/valueThis solution would drastically reduce the security threat of sUAS breaching the critical regions surrounding airports, and its implementation is relatively simple.

Statistical modelling of runway incursion occurences in the United States

Runway incursions are an important aviation safety concern; between 2002 and 2015 there were 16,785 runway incursions at United States airports ranging in size from small general aviation (GA) to large commercial airline hubs. When examining airports with the 50 highest incursion count over the past 5 years, the predominant categories were large hubs, which accounted for 21 airports and general aviation (GA) airports which accounted for 16 airports. In June 2015, the Federal Aviation Administration (FAA) announced the Runway Incursion Mitigation (RIM) program to identify airport risk factors that might contribute to a runway incursion and develop strategies to help airport stakeholders mitigate those risks. Different size airports serve different aircraft fleets, serve different operating volumes, and have different resources available (both funds and technologies) for incursion mitigation. Therefore, it is valuable to determine the correlating factors that affect incursions at different size airports. This paper uses econometrics based modelling techniques to identify statistically significant factors in data provided by the (FAA) public web sites on runway incursions. The model identified statistically significant variables that correlate with incursions, based on severity, for airports categories defined by the National Plan of Integrated Airport Systems (NPIAS).The model results indicate that operational incidents (OI) are more likely at large hub airports. In contrast, at GA/non-hub airports, pilot deviations (PD) were significant for less severe incursions (severity C and D). Only one variable, “number of years since 2002”, was found to be significant for all the three airport categories; this variable was correlated with severity A incursions and indicated a statistically significant reduction in severity A incursions, despite an overall 80% increase in incursions between 2002 and 2015.

Repeated Induction of Inattentional Blindness in a Simulated Aviation Environment

The study reported herein is a subset of a larger investigation on the role of automation in the context of the flight deck and used a fixed-based, human-in-the-loop simulator. This portion explored the relationship between automation and inattentional blindness (IB) occurrences in repeated induction using two types of runway incursions directly relevant to primary task performance. Sixty non-pilot participants performed the final five minutes of a landing scenario twice in one of three automation condition: full automation (FA), partial automation (PA), and no automation (NA). The first induction resulted in a 70% detection failure rate and the second induction resulted in a 50% detection failure rate. Detection improved in all conditions. IB group membership (IB vs. Detection) in the FA condition showed the most improvement and rated the Mental Demand and Effort subscales of the NASA-TLX significantly higher for Time 2 compared Time 1. Participants in the FA condition used the experience of IB exposure to reallocate attentional resources and improve task performance. These findings support the role of engagement in attention detriment and the consideration of attentional failure causation to select appropriate mitigation strategies.

Airfield Incursion Modeling of United States Hub Airports

The current Federal Aviation Administration’s (FAA) safety risk management (SRM) process in the United States involves mainly the use of qualitative risk measures. The development of accurate quantitative models could complement existing qualitative analysis. This paper discusses quantitative models in the form of airfield incursion models (AIM) to predict runway incursions by severity type. A dataset consisting of 65 hub airports in the United States were carefully selected in order to obtain a consistent dataset and to capture the effects of different contributing factors. The predictor variables selected for the model were annual airport operations, runway geometric configuration, number of taxiways, and weather variables. Separate negative multinomial models were estimated for three different runway incursion severities: total, categories A–C, and category D. Model performance measures such as parameter variation, log-likelihood, overdispersion, and cumulative residual plots were examined. Model validation was accomplished using data from 27 additional airports. The mean bias, mean absolute, and root mean square errors of , 1.76, and 2.72, respectively, for the severe incursions model (i.e., A–C) show that model overfitting did not occur. The AIM, in combination with the empirical Bayes method, is then illustrated via a case study—safety effectiveness of the implementation of ASDE-X technology at nine airports. The results of the case study were inconclusive due to limited data. The model structures and methodologies introduced are widely used in other transportation safety areas and are shown to be applicable to airport safety, for example, for use in FAA’s SRM.

Enhancing Safety Risk Management with Quantitative Measures

At the airfield in hub airports, many activities occur that involve a range of participants, including various-size aircraft, ground vehicles, and workers. The safety management system is FAA's approach for systematically managing aviation safety. A major component of the safety management system is safety risk management (SRM), which entails analysis, assessment, and control of safety risks, including risks on the airfield. Current SRM has few specific safety models to estimate the likelihood or frequency of risks. This paper presents an example for development and incorporation of safety models into SRM. Specifically, it discusses safety models for runway incursion that use the following variables: total and general aviation operations, length of runway by type, number of taxiway intersections, snowfall, precipitation, number of hot spots, and construction activity. Categorization and processing of data were significant because each variable used could take on multiple forms, and some types of data involved review of airfield diagrams. The data used were from 137 U.S. hub airports for 2009 through 2014. For modeling, the negative multinomial distribution was used because it proved suitable for representing overdispersed data such as runway incursion frequency. Performance of the models was assessed through the goodness-of-fit measures of log likelihood, overdispersion, and cumulative residual plots. Models were developed for five severity categories of runway incursions and three types of surface events. The safety modeling approach presented here can serve as a foundation for development of other safety models that can be integrated into SRM to enable quantitative analysis of safety risks.

Aviation Human Factors Related Industry News

Aviation Human Factors Related Industry News

Statistical Models of Runway Incursions Based on Runway Intersections and Taxiways

According to the Federal Aviation Administration (FAA), the number of runway incursions are rising. The configuration of runways and taxiways at airports has been identified by the FAA as possibly being related to the number of incursions. In this paper, the relationship between airport geometry factors and the number of runway incursions at specific United States airports is explored using statistical analyses. Airport operations data from the FAA Air Traffic Activity System, runway incursion data from the FAA Aviation Safety Information Analysis and Sharing System from 2009 through 2013, and airport geometry data created using airport geometry features from the FAA airport diagrams were collected. The 30 busiest airports with intersecting runways and the 30 busiest airports without intersecting runways were compared. As expected, the analysis of the data show that at a 5 0.05 level, runway incursions occur at a more frequent rate for airports with intersecting runways compared to airports with no intersecting runways. In the second phase of statistical analysis, the number of incursions per 100,000 operations at the 63 busiest United States airports was analyzed using four airport geometry factors as independent variables in regression analysis. The resulting regression equation was significant at the a 5 0.05 level and contained two independent variables: the number of crossing taxiways per runway and the number of runway intersections per runway. The equation and each variable in the equation are statistically significant and the equation explains 17.3% of the variation in incursions per 100,000 operations.

A Runway Incursion Detection Approach Based on Multiple Protected Area and Flight Status Machine for A-SMGCS

A-SMGCS is a modular system defined in the ICAO (International Civil Aviation Organization) Manual on Advanced Surface Movement Guidance and Control System (A-SMGCS) .One of A-SMGCS goals is to provide enhanced safety and protection of the runway. This paper presents a novel runway incursion detection approach for A-SMGCS, in which a multiple protected area is proposed to decrease complexity of pre-treatment for incursion judgment, and a flight status machine is designed to specify the transitions of one flight from one target status to another. Additionally, an HMI (Human Machine Interface) independently developed by the Second Research Institute of CAAC (Civil Aviation Administration of China) was designed in order to validate the runway incursion detection approach, the result shows that the algorithm has the potential to significantly improve runway safety by early detection and alerting of runway incursions.

Strengthening air traffic safety management by moving from outcome-based towards risk-based evaluation of runway incursions

Current safety management of aerodrome operations uses judgements of severity categories to evaluate runway incursions. Incident data show a small minority of severe incursions and a large majority of less severe incursions. We show that these severity judgements are mainly based upon the outcomes of runway incursions, in particular on the closest distances attained. As such, the severity-based evaluation leads to coincidental safety management feedback, wherein causes and risk implications of runway incursions are not well considered. In this paper we present a new framework for the evaluation of runway incursions, which effectively uses all runway incursions, which judges same types of causes similarly, and which structures causes and risk implications. The framework is based on risks of scenarios associated with the initiation of runway incursions. As a basis an inventory of scenarios is provided, which can represent almost all runway incursions involving a conflict with an aircraft. A main step in the framework is the assessment of the conditional probability of a collision given a runway incursion scenario. This can be effectively achieved for large sets of scenarios by agent-based dynamic risk modelling. The results provide detailed feedback on risks of runway incursion scenarios, thus enabling effective safety management.

Analysis of Air Traffic Incidents using event trees with fuzzy probabilities

This paper presents a methodology for analysing Air Traffic Incidents based on an estimate of the probability of an Air Traffic Incident developing into an Air Traffic Accident (Air Traffic Incidents are officially defined as events which are dangerous but without a catastrophic impact). A new approach is presented as previous research in this area has focused more on defining accident occurrence probabilities. Access to detailed information on Air Traffic Incidents is limited and for this reason the methodology developed was based on Fuzzy Set Theory. Analysis was carried out using Event Trees leading from a real Incident to a hypothetical Accident with the probabilities of occurrence of the various scenarios being defined by fuzzy sets. The results of this analysis enable the calculation of the fuzzy probability of the Incident being transformed into an Accident. Performing this analysis, new measuring techniques for the comparison of fuzzy sets were developed and partially verified. The Case Study presented in the paper analyses a Serious Runway Incursion Incident. This Serious Incident is analysed for influencing factors such as: pilot and flight controller skill levels, airport traffic volume, weather conditions, airport procedures and airport geometry. Applying the methods presented in this paper enables an assessment of the effectiveness of Preventive Recommendations of Accident/Incident Investigation Commissions. Additionally, probability estimations can be developed for different incident situations allowing the identification of Security System Weak Points thus enabling appropriate proactive measures to be taken for their elimination.

Electrical Safety of Aeronautical Ground Lighting Systems

Aeronautical ground lighting (AGL) systems have been developed to deliver warnings and status indications to pilots and maneuvering area vehicle drivers on runways. Their role in preventing the hazard of runway incursions, present in the aviation industry, is therefore crucial. AGL systems basically consist of a primary series lighting circuit, which is supplied at constant current and high voltage (e.g., 5 kV), and AGL transformers supplying ground lights at their secondary sides. Protection of persons against electric shock in AGL systems cannot be conveyed by disconnection of supply as ground faults would cause the out-of-service of the lighting system, thereby compromising the safety of aerodromes; alternative protective measures are therefore employed. In this paper, the authors examine the electrical safety issues present during maintenance of AGL systems, often performed live in order to guarantee the continuity of the service. Experimental measurements on AGL isolating transformers have been also reported in this paper, and possible solutions to improve the electrical safety of operators are proposed.

Collision course? The North Airfield Safety Study at Los Angeles International Airport (LAX)

The LAX North Airfield Safety Study was undertaken by an Academic Panel consisting of the present authors, and was based in large part on a simulation that was conducted at FutureFlight Central at NASA Ames Research Center. The primary aim of the study was “to estimate as specifically as possible the level of future safety associated with several geometrical configurations of the LAX North Airfield.” This paper describes the study, and how it combined information from human-in-the-loop simulations at NASA with historical data from LAX and other US airports about runway incursions and collisions. The analysis indicated that, even under its existing physical layout, LAX North would experience very low risk of runway collisions at traffic levels projected for 2020. That risk could be reduced by about half if the North Airfield runways were reconfigured, and some reconfigurations would also add appreciably to the operational efficiency of the airport. But because the “baseline” level of risk is so low, the Study concluded that “it would be difficult to construct a compelling case on safety grounds alone for reconfiguring the North Airfield.”

The risk of an air accident as a result of a serious incident of the hybrid type

Safety in air traffic is a multilayered concept and consists of many safety barriers. The practical side of increasing safety is mainly based on analysing the causes of accidents and incidents. This analysis leads to finding gaps in the safety structure and to developing corrective recommendations in order to eliminate them. In this paper we indicate that this practice is insufficient. Most incidents could transform into accidents with fatalities. The standard method of investigating incidents does not answer the question as to whether safety barrier is permanent or whether it was activated accidentally. This paper proposes a new method for analysing incidents aimed at finding their consequences rather than their causes. This makes it possible to find areas that need improvement. Stochastic, timed, coloured Petri nets were used for the analysis. There are three types of air traffic incidents, distinguished according to events that lead to a transformation of an incident into an accident: causal and temporal. The hybrid case, in which both types are important, has been discussed in detail. The method is useful in evaluating the current level of safety in air traffic. Applicability of this method has been shown on the example of the runway incursion problem.

Modelling runway incursion severity

Analysis of the causes underlying runway incursions is fundamental for the development of effective mitigation measures. However, there are significant weaknesses in the current methods to model these factors. This paper proposes a structured framework for modelling causal factors and their relationship to severity, which includes a description of the airport surface system architecture, establishment of terminological definitions, the determination and collection of appropriate data, the analysis of occurrences for severity and causes, and the execution of a statistical analysis framework. It is implemented in the context of U.S. airports, enabling the identification of a number of priority interventions, including the need for better investigation and causal factor capture, recommendations for airfield design, operating scenarios and technologies, and better training for human operators in the system. The framework is recommended for the analysis of runway incursions to support safety improvements and the methodology is transferable to other areas of aviation safety risk analysis.

A Conflict Detection Method for Runway Incursion

The runway incursion detection algorithm (RIDA) was developed by CASRI in support of the National Science and Technology Pillar Program of China. Detection algorithm was developed as a component of the A-SMGCS, an experimental software system for terminal area and surface operations. The detection algorithm is the primary function for implementing A-SMGCS level II. The RIDA makes use of ADS-B surveillance technology, Global Positioning System (GPS), ground surveillance systems, and data links. The RIDA was tested and demonstrated at the Shuangliu International Airport (CTU) since 2012 with highly successful results. The advanced capabilities of RIDA provide ATC controllers with enhanced situational awareness, supplemental guidance cues, a real-time display of traffic information, and warnings of runway incursions in order to reduce the possibility of runway incursions while also improving operational capability.