Effect of HFACS and Non-HFACS-Related Factors on Fatalities in General Aviation Accidents Using Neural Networks

Liu, Dahai; Nickens, Tiffany; Hardy, Leon; Boquet, Albert

doi:10.1080/10508414.2013.772831

Cited by 15 publications

(9 citation statements)

References 3 publications

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“…In this method, human factors in accidents are examined systematically, and the associated causes and sub-causes are detailed. HFACS has a large field of applications (Wiegmann and Shappell, 2001;Wiegmann et al, 2005;Dambier and Hinkelbein, 2006;Li and Harris, 2006;O'Connor, 2008;Rashid et al, 2010;Olsen, 2011;Liu et al, 2013). Therefore, it forms the foundation of this study.…”

Section: Systemic Techniquesmentioning

confidence: 98%

Modified human factor analysis and classification system for passenger vessel accidents (HFACS-PV)

et al. 2018

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Section: Systemic Techniquesmentioning

confidence: 98%

Modified human factor analysis and classification system for passenger vessel accidents (HFACS-PV)

et al. 2018

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“…The Florida Traffic Crash Records Database consists of more than 250,000 crashes a year, and using this systematic selection process, and taking into account the 17 input variables, the total refined sample should total approximately 680 files. The 680 records are suggested because this study employed the use of the 40 to 1 cases to independent variables (Liu et al, ). After those files were extracted, the files containing more than two motor vehicles or that involved pedestrians and bicycle accidents were removed.…”

Section: Methodsmentioning

confidence: 99%

“…ANN has been used widely in transportation research as well (Liu, Nickens, Hardy, & Boquet, ). For example, Mussone and Rinelli () used ANN to predict accident probability of vehicular flow; Chiou () trained a feed‐forward back‐propagating network to emulate the decision‐making process of an accident appraisal committee.…”

Section: Introductionmentioning

confidence: 99%

Investigating Injury Occurrence in Motor Vehicle Collision Using Artificial Neural Networks

Liu

Solomon

Hardy

2014

Hum Ftrs & Erg Mfg Svc

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Vehicle collisions amount to a significant loss of life in America. Upward of 30,000 lives are lost each year in the United States alone. Hundreds of millions of dollars are spent each year on vehicle safety and roadway design in the United States, and many studies have been conducted on the causal factors for vehicle collisions. This study used artificial neural networks as a means to predict the occurrence of injury of a vehicle collision. The objective was to classify the levels of the “injury occurrence” in vehicle collisions, given certain input variables, including demographic and environmental factors involved in crashes. Using Neural Ware's Predict software, a back‐propagation neural network model was constructed, trained, tested, and validated using data from the 2006 and 2007 Florida Traffic Crash Database. The neural network's computational power was iteratively increased by adding hidden layers to boost its predicting power until the performance began to plateau. Classification rates as well as Kolmogorov‐Smirnov (K‐S) statistical analysis were used to validate the model. A sensitivity analysis was then applied to find the level of contribution that the input variables had on the injury occurrence. Accident severity, number of lanes, and road surface conditions were found to have the most positive impacts on the injury occurrence. The implications of the study are discussed at the end of the article.

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“…Neural Networks (NNs) can cope with such complexities. However, previous authors that have used HFACS data as an input into a NNs (Liu, et al, 2013) have not reflected the underlying theoretical structure of errors and precursors in their analytical approach. In this case HFACS data were simply used as an input into the prediction of accident category (fatal/non-fatal) rather than a validation of the underlying theoretical structure or Reason's error model.…”

Section: Neural Networkmentioning

confidence: 99%

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts

Harris

2017

Ergonomics

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Human Factors Analysis and Classification System (HFACS) is based upon Reason's organizational model of human error which suggests that there is a 'one to many' mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 preconditions and level 1 unsafe acts was modelled using an artificial neural network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS. The NN solution produced an average overall classification rate of ca. 74% for all unsafe acts from information derived from their level 2 preconditions. However, the correct classification rate was superior for decision- and skill-based errors, than for perceptual errors and violations. Practitioner Summary: A model to predict unsafe acts (HFACS level 1) from their preconditions (HFACS level 2) was developed from the analysis of 523 military aircraft accidents using an artificial NN. The results could correctly predict approximately 74% of errors.

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Effect of HFACS and Non-HFACS-Related Factors on Fatalities in General Aviation Accidents Using Neural Networks

Cited by 15 publications

References 3 publications

Modified human factor analysis and classification system for passenger vessel accidents (HFACS-PV)

Modified human factor analysis and classification system for passenger vessel accidents (HFACS-PV)

Investigating Injury Occurrence in Motor Vehicle Collision Using Artificial Neural Networks

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts

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