1,720,995 research outputs found
Using data mining techniques to predict the severity of bicycle crashes
Full text linkTo investigate the factors predicting severity of bicycle crashes in Italy, we used an observational study of official statistics. We applied two of the most widely used data mining techniques, CHAID decision tree technique and Bayesian network analysis. We used data provided by the Italian National Institute of Statistics on road crashes that occurred on the Italian road network during the period ranging from 2011 to 2013. In the present study, the dataset contains information about road crashes occurred on the Italian road network during the period ranging from 2011 to 2013. We extracted 49,621 road accidents where at least one cyclist was injured or killed from the original database that comprised a total of 575,093 road accidents. CHAID decision tree technique was employed to establish the relationship between severity of bicycle crashes and factors related to crash characteristics (type of collision and opponent vehicle), infrastructure characteristics (type of carriageway, road type, road signage, pavement type, and type of road segment), cyclists (gender and age), and environmental factors (time of the day, day of the week, month, pavement condition, and weather). CHAID analysis revealed that the most important predictors were, in decreasing order of importance, road type (0.30), crash type (0.24), age of cyclist (0.19), road signage (0.08), gender of cyclist (0.07), type of opponent vehicle (0.05), month (0.04), and type of road segment (0.02). These eight most important predictors of the severity of bicycle crashes were included as predictors of the target (i.e., severity of bicycle crashes) in Bayesian network analysis. Bayesian network analysis identified crash type (0.31), road type (0.19), and type of opponent vehicle (0.18) as the most important predictors of severity of bicycle crashes
User perspectives on AI explainability in aerospace manufacturing: a Card-Sorting study
Full text linkThe integration of AI technologies in aerospace manufacturing is significantly transforming critical operational processes, impacting decision-making, efficiency, and workflow optimization. Explainability in AI systems is essential to ensure these technologies are understandable, trustworthy, and effectively support end-users in complex environments. This study investigates the factors influencing the explainability of AI-based Decision Support Systems in aerospace manufacturing from the end-users' perspective. The study employed a Closed Card Sorting technique involving 15 professionals from a leading aerospace organization. Participants categorized 15 AI features into groups—enhances, is neutral to, and hinders explainability. Qualitative feedback was collected to understand participants' reasoning and preferences. The findings highlighted the importance of user support features in enhancing explainability, such as system feedback on user inputs and error messages with guidance. In contrast, technical jargon was consistently perceived as a hindrance. Transparency of algorithms emerged as the highest-priority feature, followed by clarity of interface design and decision rationale documentation. Qualitative insights emphasized the need for clear communication, intuitive interfaces, and features that reduce cognitive load. The study provides actionable insights for designing AI-based DSSs tailored to the needs of aerospace professionals. By prioritizing transparency, user support, and intuitive design, designers and developers can enhance system explainability and foster user trust. These findings support the human-centric development of AI technologies and lay the groundwork for future research exploring user-centered approaches in different high-stakes industrial contexts
Journey Attributes, E-Bike Use, and Perception of Driving Behavior of Motorists as Predictors of Bicycle Crash Involvement and Severity
No full textPrevious studies have revealed the relevance of e-bike use, perception of driving behavior of motorists, and instrumental and affective factors in work and leisure journeys among regular cyclists. However, the importance of these factors as predictors of bicycle crash involvement and severity is less well-known. The aim of the present study was to investigate the role of journey attributes, e-bike use, and perception of driving behavior of motorists in predicting bicycle crash involvement and severity, while controlling for sociodemographic factors, cycling levels, cycling environment, and purposes of cycling. We collected data from an online panel of 2,389 respondents from six European countries (Sweden, Netherlands, United Kingdom, Hungary, Italy, Spain). Using the generalized linear model, we found that both bicycle crash involvement and severity were related to lower age, being employed, using the bicycle for traveling to or from college/university, not using the bicycle for leisure/training, and using an e-bike. Bicycle crash severity was associated with lower affective attributes, higher instrumental attributes, and the perception of good driving behavior of motorists
Use of smartphone and crash risk among cyclists
Full text linkHigh percentages of cyclists admit using smartphone devices while cycling. Moreover, such use has been found to be associated with near crashes and crashes, representing a risk factor for cyclists. This study examines the relationship between such type of behaviours, comprising calling and manipulating the screen, and the frequency of near crashes and actual crashes among Italian cyclists. We administered an online survey measuring smartphone-specific violation, errors, near crash and crash to Italian cyclists (N = 298; age range: 19–72). We hypothesised that the relationship between smartphone use and near crashes would be explained by an increase in the number of errors committed, thus increasing the likelihood of being involved in near crashes. Moreover, we hypothesised that near crashes will predict actual crashes. Results of path analysis showed that smartphone-specific violations predicted crashes throughout their consecutive effects on errors and near crashes only in the subsample of men. These findings offer an explanation of how smartphone use contributes to incrementing the likelihood of getting involved in near crashes and actual crashes. To our knowledge, the present study is the first in building a path model explaining how smartphone-specific violations lead to more near crashes among cyclists
Industry 4.0 and Human Resource Management Processes: A Qualitative Study.
No full textIndustry 4.0 and Human Resource Management Processes: A Qualitative Study
Integrating collaborative robots in manufacturing, logistics, and agriculture: Expert perspectives on technical, safety, and human factors
Full text linkThis study investigates the implementation of collaborative robots across three distinct industrial sectors: vehicle assembly, warehouse logistics, and agricultural operations. Through the SESTOSENSO project, an EU-funded initiative, we examined expert perspectives on human-robot collaboration using a mixed-methods approach. Data were collected from 31 technical experts across nine European countries through an online questionnaire combining qualitative assessments of specific use cases and quantitative measures of attitudes, trust, and safety perceptions. Expert opinions across the use cases emphasized three primary concerns: technical impacts of cobot adoption, social and ethical considerations, and safety issues in design and deployment. In vehicle assembly, experts stressed the importance of effective collaboration between cobots and exoskeletons to predict and prevent collisions. For logistics, they highlighted the need for adaptable systems capable of handling various object sizes while maintaining worker safety. In agricultural settings, experts emphasized the importance of developing inherently safe applications that can operate effectively on uneven terrain while reducing workers’ physical strain. Results reveal sector-specific challenges and opportunities: vehicle assembly operations require sophisticated sensor systems for cobot-exoskeleton integration; warehouse logistics demand advanced control systems for large object handling; and agricultural applications need robust navigation systems for uneven terrain. Quantitative findings indicate generally positive attitudes toward cobots, particularly regarding societal benefits, moderate to high levels of trust in cobot capabilities and favorable safety perceptions. The study highlights three key implications: (1) the need for comprehensive safety protocols tailored to each sector’s unique requirements, (2) the importance of user-friendly interfaces and intuitive programming methods for successful cobot integration, and (3) the necessity of addressing workforce transition and skill development concerns. These findings contribute to our understanding of human-robot collaboration in industrial settings and provide practical guidance for organizations implementing collaborative robotics while considering both technological advancement and human-centered design principles
Helicopter Pilots’ Tasks, Subjective Workload, and the Role of External Visual Cues During Shipboard Landing
Full text linkHelicopter shipboard landing is a cognitively complex task that is challenging both for pilots and their crew. Effective communication, accurate reading of the flight instruments, as well as monitoring of the external environment are crucial for a successful landing. In particular, the final phases of landing are critical as they imply high workload situations in an unstable environment with restricted space. In the present qualitative study, we interviewed ten helicopter pilots from the Italian Navy using an applied cognitive task analysis approach. We aimed to obtain a detailed description of the landing procedure, and to identify relevant factors that affect pilots’ workload, performance, and safety. Based on the content analysis of the interviews, we have identified six distinct phases of approaching and landing on a ship deck and four categories of factors that may significantly affect pilots’ performance and safety of the landing procedure. Consistent with previous studies, our findings suggest that external visual cueing is vital for a successful landing, in particular during the last phases of landing. Therefore, based on the pilots’ statements, we provide suggestions for possible improvements of external visual cues that have the potential to reduce pilots’ workload and improve the overall safety of landing operations
Human factors and emerging needs in aerospace manufacturing planning and scheduling
Full text linkPlanning and Scheduling (P&S) are critical components of organizational management that influence efficiency, overall performance, and human factors in the workplace. The aerospace manufacturing industry is experiencing rapid changes, marked by heightened demands for new aircraft and the need for precise task execution to accommodate increasing air traffic and rigorous safety regulations. This study explores the human factors and emerging needs in the P&S processes within aerospace manufacturing. A qualitative research approach was employed, featuring semi-structured interviews with 15 professionals from a prominent European organization. The participants, actively engaged in P&S operations, were chosen to offer diverse perspectives on their roles and the industry’s specific requirements. Results indicate that planners/schedulers, IT experts, and operations team leaders are crucial in ensuring efficiency throughout the various stages of P&S operations. The findings reveal that emerging needs encompass workforce and customer management (i.e., allocating human resources, responding to client requests, and addressing workforce resistance to new technology adoption), prioritization (i.e., scheduling tasks based on urgency, error susceptibility, and cost efficiency), and contingency handling (i.e., machinery availability, time constraints, quality issues, human performance variability, and weather conditions). These needs highlight the importance of considering human factors and cognitive aspects when designing and implementing P&S systems. The study underscores the challenges the aerospace manufacturing industry faces as it adapts to technological advancements and evolving market conditions. The findings emphasize the necessity of advanced P&S systems that integrate innovative technological solutions with an understanding of human factors and cognition
Visual Scanning Techniques and Mental Workload of Helicopter Pilots During Simulated Flight
Full text linkThe visual scanning techniques used by helicopter pilots are a critical skill to accomplish safe and correct landing. According to the human information processing theory, visual scanning techniques can be analyzed as a function of fixation location, number, and duration of fixations.
This study assessed these techniques in expert and novice pilots during an open sea flight simulation in a low-workload condition, consisting of a daylight and good weather simulation, and in a high-workload condition of night-time, low visibility, and adverse weather conditions. Taking part in the study were 12 helicopter pilots. Mental workload was assessed through psychological measures (NASA-TLX). The pilots’ performance was assessed and eye movements were recorded using an eye-tracker during four phases of the flight simulations.
Overall, pilots made more fixations out of the window (OTW; 22.54) than inside the cockpit (ITC; 11.08), Fixations were longer OTW (830.17 ms) than ITC (647.97 ms) and they were shorter in the low-demand condition (626.27 ms). Further, pilots reported higher mental workload (NASA-TLX) in the high-demand condition compared to the low-demand condition, regardless of their expertise, and expert pilots reported a lower mental workload compared to novice pilots.
Pilots’ performance and perceived mental workload varied as a function of expertise and flight conditions. Pilots rely on instrument support during the cruise phase and external visual cues during the landing phase. The implications for a new visual landing system design are discussed
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