Runwaysafety is one of the most challenging issues in aviation. It is giventhe utmost priority in most agencies across the globe, including theUnited States. The extent of the issues can be illustrated by theamount of incidents that happen on a daily basis. For example, thethree runway incursions occur daily in the United States. Thepotential impact of this incidents is rather severe hence, need tobe addressed with utmost care. They pose threats to the air carrieraircraft, general aviation, military aircraft, and pedestrianvehicles. As a matter of fact, many of these incidents result incollisions that culminate to fatalities. Fatalities have beenexperienced at non-towered and towered airports. Runaway incursionscan emanate from just a few seconds of inattentiveness.
Therefore,research has been focused on examining the issue of runway incursionsat various airports. The main aim is to reduce the number ofaccidents emanating from the incidents. This can be done viaestablishment of risk factors, including human errors. Once the riskfactors are obtained, different mitigation measures can beintroduced. Present data suggests that lowering the number of runwayincursions needs a comprehensive outlook on the instances that leadto the issues. In general, more research ought to be undertaken tooutline the various challenges and causes of runway incursions aswell as the mitigation measures that should be applied to prevent theescalation of the problem.
Thecauses of runway incursions are more than just incorrect presence ofvehicle, person, or aircraft on the runway. Instead, human errors,particularly pilots contribute significantly to the runwayincursions. As such, they pose significant challenges to the safetyoperations of the airport surface.
Goodheart,Benjamin Jeffry (2014). Identification of Causal Paths and Predictionof Risk by Means of Bayesian Belief Networks. 9-20.
Thisarticle is based on identification of runway incursion risks via theBayesian Belief Networks (BBN). Runway incursions are a key area ofconcern in the global perspective. Even with significant efforts toalleviate the same, occurrences of these events are steadilyincreasing. As such, Goodheart strives to provide a solution thatwould utilize the development of a BBN to a compact model in order toascertain the idea and offer a baseline to support othercomprehensive standards. The study focuses on developing andvalidating a universal, system-level model for connection of theUnited States RI occurrences that form the foundation for futuredevelopment of similar or better models to reduce RI, especially bygoverning agencies.
TheBayesian belief networks (BBNs) are normally utilized to modelecological predictions thus assisting in the decision-making process.Goodheart provides a systematic model for developing, testing, andrevising the tool. In this study, author reviewed data for the UnitedStates for a period of five years that is between 2008 and 2012. RIsamples were constrained within the stated time in alignment with theFAA description of an RI to the International Civil AviationOrganization standards. The research process followed three broadphases. The first stage involved the collection of quantitative andqualitative RI data followed by the creation and validation of theBBN model. The final phase entailed the elicitation and combinationof the designed expert judgment-based probabilistic data to back upthe model.
Joslin,R. (2014). Validation of New Technology Using Legacy Metrics:Examination of SURF-IA Alerting for Incidents.Journalof Aviation Technology and Engineering,2-10.
Inthis research article, the author attempted to illustrate theinnovative mechanism of using expert raters as well as actualhigh-risk events to establish the limitations of utilizing legacymetrics in measuring the efficiency of novel technologies modeled toavoid hazardous events. The expert raters validated the EnhancedTraffic Situation Awareness on the Airport Surface with Indicatorsand Alerts (SURF-IA) system for offering alerts to the pilots so asto lessen the events of pilot deviation kind runway incursionregarded as severe (Category A or B) by the legacy FAA/ICAO RunwayIncursion Severity Classification (RISC) model.
RobertJoslin is an experienced person in this field serving as the FAA’schief scientific and technical consultant for the Flight DeckTechnology. He is also an assistant professor at the College ofAeronautics at Embry-Riddle Aeronautical University-Worldwide. Joslinis also experienced having served as a FAA flight test pilot. Apartfrom the experience that includes flying over 80 aircraft, he alsopossesses the academic qualifications including a PHD in aviation. Assuch, he is well acclimatized to the aspects entailed in this study.As per the author, before the certification of the SURF-IA to beutilized on aircrafts, it is important to conduct further studiesinvolving runaway incursion incidents categorized as serious by theRISC model must be done to establish other factors that do not causea SURF-IA alerting result.
SabineWilke, Arnab Majumdar, and Washington Y. Ochieng (2015). Modellingrunway incursion severity. Journalof Accident Analysis and Prevention,88-99.
Asentailed in this article, it is paramount to comprehend the causes ofrunway incursions so as to develop an efficient mechanism to mitigatethe issues. The author further acknowledges the considerableweaknesses surrounding the current device utilized to model thefactors. The authors support a systematic framework to model thecausal factors and their relations in this article it is paramountto comprehend the causes of runway incursions so as to develop anefficient mechanism to mitigate the issues. The authors support asystematic framework to model the causal factors and their relationto severity that is inclusive of the definition of the airportsurface system architecture and description of terminologies. It alsoinvolves identification and collection of the relevant data.
Thearticle presents the current status of the incursion assessment. Thearticle further gives recommendations that are based on analysis,stating that it is vital for the ATC and the pilots to be addressedvia consistent training. The document further implies that enoughanalytical framework is lacking to rightly illustrate the potentialof runway incursion accidents in relation to loss of life.
Schonefeld,J., and Moller, D.P.F (2012). Runway incursion prevention systems: Areview of runway incursion avoidance and alerting system approaches.Journalof Progress in Aerospace Sciences,31-49.
Inthis paper, the authors provide an introduction to the runwayincursions and how it leads to conflicting situations. As elucidatedby the article, the National Transportation Safety Board (NTSB) hasplaced mitigation of runway incursions as one of its top mostpriorities. The main aim of this article is to enlighten readersabout runway incursions. As such, they define and give differentcauses of the same, while elaborating on the role of aviation. Apartfrom that, the article outlines some statistics surrounding theaspect and the most prone areas.
Thearticle also reviews some of the systems used in railway incursionand the standard technologies. In general, the document is a valuableone to readers who would like to be enlightened on the variousaspects of runway incursions that is causes, detection and avoidancesystems, and other technologies used. Through this article, one cancomprehend most of the elements entailed in runway incursions. Thedocument gives a visual representation of the topic enhancing itsviability.
Yu-HernChang, Kin-Meng Wong (2012). Human risk factors associated withrunway incursions. Journalof Air Transport Management,25-30.
Inthis credible article, the authors strived to examine the human riskfactors linked to the pilots in runaway incursions. As such, theyutilized a human factors model to classify the risk elements basingon the views of 112 airline pilots. To further enhance the validityof document, the authors focused on civil aviation authority and theTaiwan’s airlines as well as integrating views from experts. In thediscussions, they obtained opinions on the enhancement potential offour approaches for airline pilots to lessen runway incursions.
Asdetermined by the article, most of the runway incursions areprimarily caused by human errors, particularly pilots. As such,establishing the risky aspects in runway incursion accidents wouldreduce the fatality levels as well as the amounts of financial lossesrelated to airlines. As elaborated by the authors, mitigating theaccidents in the runway ought to start by identifying and dealingwith the human errors. The four zones prioritized by the articledistinguish the risk factors providing an important basis where theissues can be established and dealt with.
Goodheart, B. J. (2014). Identification of Causal Paths and Prediction of Risk by Means of Bayesian Belief Networks. 9-20.
Joslin, R. (2014). Validation of New Technology Using Legacy Metrics: Examination of SURF-IA Alerting for Incidents. Journal of Aviation Technology and Engineering, 2-10.
Sabine Wilke, Arnab Majumdar, and Washington Y. Ochieng (2015). Modelling runway incursion severity. Journal of Accident Analysis and Prevention, 88-99.
Schonefeld, J., and Moller, D.P.F. (2012). Runway incursion prevention systems: Areview of runway incursion avoidance and alerting system approaches. Journal of Progress inAerospaceSciences, 31-49.
Yu-Hern Chang, K.-M. W. (2012). Human risk factors associated with runway incursions. Journal of Air Transport Management, 25-30.