A human error analysis of general aviation controlled flight into terrain accidents occurring between 1990-1998

Evaluation and error analysis of machine translation output are important but difficult tasks. In this article, we propose a framework for automatic error analysis and classification based on the identification of actual erroneous words using the algorithms for computation of Word Error Rate (WER) and Position-independent word Error Rate (PER), which is just a very first step towards development of automatic evaluation measures that provide more specific information of certain translation problems. The proposed approach enables the use of various types of linguistic knowledge in order to classify translation errors in many different ways. This work focuses on one possible set-up, namely, on five error categories: inflectional errors, errors due to wrong word order, missing words, extra words, and incorrect lexical choices. For each of the categories, we analyze the contribution of various POS classes. We compared the results of automatic error analysis with the results of human error analysis in order to investigate two possible applications: estimating the contribution of each error type in a given translation output in order to identify the main sources of errors for a given translation system, and comparing different translation outputs using the introduced error categories in order to obtain more information about advantages and disadvantages of different systems and possibilites for improvements, as well as about advantages and disadvantages of applied methods for improvements. We used Arabic–English Newswire and Broadcast News and Chinese–English Newswire outputs created in the framework of the GALE project, several Spanish and English European Parliament outputs generated during the TC-Star project, and three German–English outputs generated in the framework of the fourth Machine Translation Workshop. We show that our results correlate very well with the results of a human error analysis, and that all our metrics except the extra words reflect well the differences between different versions of the same translation system as well as the differences between different translation systems.

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Human Error Analysis Based on a Semantically Defined Cognitive Pilot Model

Lecture Notes in Computer Science - Computer Safety, Reliability, and Security ◽

10.1007/978-3-540-75101-4_14 ◽

2007 ◽

pp. 134-147 ◽

Cited By ~ 3

Author(s):

Andreas Lüdtke ◽

Lothar Pfeifer

Keyword(s):

Error Analysis ◽

Human Error ◽

Pilot Model

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Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model

Applied Sciences ◽

10.3390/app9235049 ◽

2019 ◽

Vol 9 (23) ◽

pp. 5049 ◽

Cited By ~ 2

Author(s):

Tao Lyu ◽

Wenbin Song ◽

Ke Du

Keyword(s):

Human Factors ◽

Traffic Safety ◽

Traffic Control ◽

Human Error ◽

Air Traffic ◽

General Aviation ◽

Objective Data ◽

Factors Analysis ◽

Four Levels ◽

Human Factors Analysis

Air traffic control (ATC) performance is important to ensure flight safety and the sustainability of aviation growth. To better evaluate the performance of ATC, this paper introduces the HFACS-BN model (HFACS: Human factors analysis and classification system; BN: Bayesian network), which can be combined with the subjective information of relevant experts and the objective data of accident reports to obtain more accurate evaluation results. The human factors of ATC in this paper are derived from screening and analysis of 142 civil and general aviation accidents/incidents related to ATC human factors worldwide from 1980 to 2019, among which the most important 25 HFs are selected to construct the evaluation model. The authors designed and implemented a questionnaire survey based on the HFACS framework and collected valid data from 26 frontline air traffic controllers (ATCO) and experts related to ATC in 2019. Combining the responses with objective data, the noisy MAX model is used to calculate the conditional probability table. The results showed that, among the four levels of human factors, unsafe acts had the greatest influence on ATC Performance (79.4%), while preconditions for safe acts contributed the least (40.3%). The sensitivity analysis indicates the order of major human factors influencing the performance of ATC. Finally, this study contributes to the literature in terms of methodological development and expert empirical analysis, providing data support for human error management intervention of ATC in aviation safety.

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