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The accurate detection of seafarers' unsafe behaviors is of great significance to improve the ship navigation safety. This study proposes an improved deep learning framework with a self-made dataset to detect the unsafe behavior of seafarers on duty. In order to increase the detection speed, the improved Cross Stage Partial connections (CSP) module is proposed to replace the original CSP module in the neck network of traditional algorithm. The efficient channel attention (ECA) module is also introduced to the backbone network of conventional algorithm as the attention mechanism network. In addition, the learning and representation capacities of the improved deep learning framework are promoted by redesigning the sizes of anchor boxes. The experiment results show that the proposed framework outperforms traditional object detection algorithms (e.g., YOLOv5s, R-CNN) in detecting seafarers' unsafe behaviors because of the much higher detection speed and detection accuracy. [ABSTRACT FROM AUTHOR]

This article explores the effects of human, technical, and operating factors on the safety record of 50 airlines over the period 2004 to 2015. The factors examined include (1) pilot salary, (2) fleet age, (3) International Air Transport Association - Operational Safety Audit (IOSA) certification, (4) International Civil Aviation Organization safety parameters (i.e., legislation, organization, licensing, operations, airworthiness, accident investigation, air navigation service, and aerodromes), and (5) cultural-related indices (such as power distance, degree of uncertainty avoidance, and language for communication). Among the important factors such as organization, licensing, and operations performance, pilot salary is identified as the most important factor differentiating airlines with or without crashes. Within the group of airlines that encountered air accidents during the study period, fleet age, airworthiness, accident investigation, air navigation services and aerodromes scores, and salary are found to be significant factors on accident rate. IOSA certification, aerodromes, language of communi-cation, and uncertainty avoidance appear to be totally immaterial. Identifying the important factors is important to assist airlines investigate and improve their safety records. [ABSTRACT FROM AUTHOR]

Human factor- (passengers and operators) related accidents in the complex metro system can affect the metros' operation resulting in catastrophic failures. This research aims to present the causal chains of Beijing Metro operation risks initiated by human factors and discuss the uncertain logic relationship between events in the chain and possibility fluctuation of event's occurrences. The research methodology combines Cause Consequence Analysis (CCA) and Fuzzy Causal Model (FCM). CCA expresses the interactions between multiple failures and protective mechanisms whereas FCM models the uncertain incidence relationship between events and possibility fluctuation of human-factor event occurrence. Nine causal chains of metro operation risks were drawn out based on the historical incident data and procedures for operation and staff communications during an emergency. The calculated possibility for the top event occurrence of several types of incidents shows similar trends with historical operations' incident data. The results show that the time intervals with the highest possibility of a risk event occurrence of type "passenger is clamped by door," influenced by operator fatigue, are 13:00 to 15:00 and 21:00 to 24:00. Also, the results show that the proposed methodology is applicable to the prediction of metro operation risk. [ABSTRACT FROM AUTHOR]

Fatigue is a major contributor to transportation accidents. Shift workers are particularly prone to fatigue, and organizations increasingly rely on fatigue risk models to evaluate work schedules. In this article, the authors empirically examine the relationship between fatigue risk and human errors in railway traffic control. Despite their safety-critical role, research on railway traffic controller fatigue has remained limited. The authors evaluate the predictive validity of a commonly used fatigue risk tool (the Risk Index) and investigate the effect of additional risk factors (age, gender, part-time work, and day of week). In close cooperation with Belgian railways, the authors analyze a unique full-year data set, containing more than 11,000 work shifts. By adopting a Tobit regression for censored data, the authors account for work shifts with zero error occurrence. Results validate the applied fatigue risk model under real-world circumstances and reveal risk predictors above and beyond shift schedule design: significant day-of-week effects are observed. The probability of making at least one error is highest on Saturdays (+6% compared to Mondays), and lowest on Tuesdays, Wednesdays, and Thursdays. Thus, results suggest that safe work schedule design should also take into account the day of the week and not exclusively rely on fatigue risk scores. [ABSTRACT FROM AUTHOR]

The diversity of drivers’ speed selection in free-flow conditions has been assumed to originate from various human factors, mainly differences in driver characteristics and preferences. This study uses a stated-preference web-based survey with a sample of 290 participants to investigate the diversity of speed selection in relations to driver characteristics. The survey included newly developed scales of estimating driving risks and estimating personal difficulty of performing vehicle-related technical tasks. Also included were items on performing spatial tasks and drivers’ own self-assessments. The analysis of the survey results revealed that newly developed latent driver characteristics, such as risk awareness and technical aversion, were found to strongly affect individual drivers’ speed selection in a daily trip–daily speed selection. The perceived speed of the average driver, or average driver perception, also had a significant effect on drivers’ own speed selection. In addition, some latent characteristics were found to have stronger effects in certain demographic groups. Implications for further speed-selection researches and road safety policies are discussed. [ABSTRACT FROM AUTHOR]

This article aims to provide a clearer explanation for the conditions of air-crash occurrence in the world during the 1950 to 2008 period. The authors use the modeling techniques of the vector auto-regression and the vector error correction (VEC) models. The authors seek to shed light on the dynamics and understand the long-run interactions between the explanatory factors of air crashes and the probable correlation with their growing number and provide clues to the following issues: What are the most striking factors in the occurrence of air crashes in the world? How do the accident factors interact within the short-, medium-, and long-run to bring about an air crash? What are the (direct and indirect) links of influence between the recorded accidents and their causes? The results show that there are many causal relationships between the investigated factors. The air-crash factors converge and are complementary and interdependent. For example, weather conditions are responsible for about one third of human error. Factors considered in the literature as “others” because of their unknown character are identified in this study as human factors that account for 75% of the key factors in air-crash research such as pilots, air traffic controllers, chief maintenance staff, engineers, and survey offices. [ABSTRACT FROM PUBLISHER]

The number of related fatalities is one of the most essential socioeconomic characteristics of a natural disaster as death is the most serious and irreversible consequence of a disaster. Texas leads the nation in flash flood fatalities. From 1959 through 2008 the flood fatalities in Texas (839) were more than three times the fatalities in the next leading state, Pennsylvania (265). Flood fatalities in Texas represent a serious public health problem. Flood fatality statistics were extracted from National Climatic Data Center Storm Data publications. Review of the flood fatalities, where the death circumstances are provided, reveals that most fatalities are motor vehicle related (77%). Data analysis indicates that, in Texas, males are much more likely to be the victims of motor vehicle-related flood accidents than are females. Most motor vehicle-related fatalities happened at night (56%). Most fatalities resulted from flash floods. Spatial analysis indicates that the highest numbers of fatalities occur in counties having major urban areas. Flood fatalities in Texas can be reduced through a combination of improved hydrometeorological forecasting, educational programs aimed at enhancing the public awareness of flood risk and the seriousness of flood warnings, and timely and appropriate action by local emergency and safety authorities. [ABSTRACT FROM AUTHOR]