Categorization of Near-Collision Close Calls Reported to the Aviation Safety Reporting System

Tiller, Lauren N.; Bliss, James P.

doi:10.1177/1541931213601947

Cited by 5 publications

(2 citation statements)

References 3 publications

(3 reference statements)

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“…The concept of a near-miss in risk perception has been used to explain how people respond to events that could have happened, but by chance did not. This framework has been used in risk perception research addressing avoidance and mitigation responses in disaster management (Cui, Rosoff, & John, 2018;Dillon & Tinsley, 2008;Dillon, Tinsley, & Burns, 2014;Dillon, Tinsley, & Cronin, 2011;Dupey & Smith, 2019;Madsen, Dillon, & Tinsley, 2016;Phimister, Oktem, Kleindorfer, & Kunreuther, 2003;Tiller & Bliss, 2017).…”

Section: Near-miss Effectmentioning

confidence: 99%

Behavioral Determinants of Target Shifting and Deterrence in an Analog Cyber‐Attack Game

et al. 2019

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This study examines how exploiting biases in probability judgment can enhance deterrence using a fixed allocation of defensive resources. We investigate attacker anchoring heuristics for conjunctive events with missing information to distort attacker estimates of success for targets with equal defensive resources. We designed and conducted a behavioral experiment functioning as an analog cyber attack with multiple targets requiring three stages of attack to successfully acquire a target. Each stage is associated with a probability of successfully attacking a layer of defense, reflecting the allocation of resources for each layer. There are four types of targets that have nearly equal likelihood of being successfully attacked, including one type with equally distributed success probabilities over every layer and three types with success probabilities that are concentrated to be lowest in the first, second, or third layer. Players are incentivized by a payoff system that offers a reward for successfully attacked targets and a penalty for failed attacks. We collected data from a total of 1,600 separate target selections from 80 players and discovered that the target type with the lowest probability of success on the first layer was least preferred among attackers, providing the greatest deterrent. Targets with equally distributed success probabilities across layers were the next least preferred among attackers, indicating greater deterrence for uniform‐layered defenses compared to defenses that are concentrated at the inner (second or third) levels. This finding is consistent with both attacker anchoring and ambiguity biases and an interpretation of failed attacks as near misses.

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Section: Near-miss Effectmentioning

confidence: 99%

Behavioral Determinants of Target Shifting and Deterrence in an Analog Cyber‐Attack Game

et al. 2019

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“…Despite the fact that aviation has a high level of safety, accidents and incidents continue to occur [1]. It is a good way to learn from accident and incident reports, which show why accidents happen and why they do not, and can help identify both the dangers and the precautions [2]. A few machine learning studies have been carried out to analyze these reports [3−6].…”

Section: Introductionmentioning

confidence: 99%

Classification of Aviation Incident Causes using LGBM with Improved Cross-Validation

Ni,

Wang,

Chen

et al. 2024

J. of Syst. Eng. Electron.

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Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm. To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM) based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed: one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBM-HSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model's accurate identification of civil aviation incident causes can assist to improve civil aviation safety.

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An Examination of Close Calls Reported Within the International Association of Fire Chiefs Database

Bliss

Tiller

2018

Advances in Intelligent Systems and Computing

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Categorization of Near-Collision Close Calls Reported to the Aviation Safety Reporting System

Cited by 5 publications

References 3 publications

Behavioral Determinants of Target Shifting and Deterrence in an Analog Cyber‐Attack Game

Behavioral Determinants of Target Shifting and Deterrence in an Analog Cyber‐Attack Game

Classification of Aviation Incident Causes using LGBM with Improved Cross-Validation

An Examination of Close Calls Reported Within the International Association of Fire Chiefs Database

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