AHFE Open AccessAdvances in Human Factors of Transportation
Editors: Dimitrios Ziakkas, Riccardo Patriarca, Amit Sharma, Steven Mallam
Topics: Transportation Engineering
ISBN: 979-8-950676-00-0
DOI: 10.54941/ahfe1007243
Table of Contents
Characteristics of Changes in Body Composition Measurements Among Japanese Alpine Skiers
This study aimed to clarify changes in body composition among young Japanese alpine skiers. The subjects are 11 skiers (7 males and 4 females). Their ages are 15 to 18 years old. No studies have measured the same skiers over the long term and described their characteristics. Therefore, in this study, body composition measurements were conducted three times, June 2024, November 2024, and July 2025. Measurements were taken using a multi-frequency measuring instrument (MC-780A-N, TANITA corporation, Japan). The data items of this measurement included weight, body fat percentage, fat mass, lean mass, muscle mass, and estimated bone mass. The average of fat mass was highest for both males and females in the measurements taken in November 2024. Consequently, the average of body fat percentage also peaked in November 2024. The average of muscle mass increased with each measurement for both males and females. A male skier was found to have gained 6.3 kg of muscle mass over the course of about one year. In addition, it was found that female skiers experience a fluctuation of 1–2 kg in body fat over the course of a year. Understanding the skier's body can be considered beneficial not only for maximizing performance but also for preventing injuries. The subjects of this study were a very small number of Japanese alpine skiers. Therefore, it is necessary to expand the sample size. I will examine the developmental characteristics of Japanese skiers while conducting international comparisons in the future.
Kazusa Oki
Open Access
Article
Conference Proceedings
The Role of Fatigue Risk Management Systems (FRMS) in the Implementation of Human -AI teaming in the Aviation Ecosystem.
The emergence of Human-AI teaming within the aviation ecosystem introduces profound implications for fatigue management, operational resilience, and safety assurance. As artificial intelligence systems become increasingly embedded in flight operations, predictive analytics, crew scheduling, and cockpit decision support, the integration of Fatigue Risk Management Systems (FRMS) takes on an expanded and critical role. This paper examines how FRMS principles can support, and must be adapted to support, the safe and effective implementation of human-AI teaming, ensuring that technological innovation enhances rather than undermines human performance in safety-critical environments. The paper situates fatigue as a persistent human factor risk that continues to shape error pathways, cognitive performance, and decision-making under operational stress. AI-enabled systems hold the potential to augment fatigue management by providing real-time physiological monitoring, predictive fatigue modelling, adaptive workload distribution, and decision-support cues during periods of reduced alertness. However, the introduction of AI also reshapes the operational landscape: as tasks shift between human and machine, the cognitive workload profile of pilots may fluctuate unpredictably. In highly automated or single-pilot contexts, AI systems may inadvertently increase fatigue risks by imposing vigilance demands, imposing excessive monitoring burdens, or providing poorly calibrated levels of assistance. These emerging risks underscore the need for FRMS frameworks capable of recognising and governing the unique human-machine interactions associated with advanced automation. The paper examines how each core component of FRMS, policy, risk assessment, data-driven monitoring, and training, contributes to the governance of human-AI teaming. FRMS Policy must explicitly acknowledge AI as both a potential fatigue mitigator and a fatigue hazard, emphasising a human-centric philosophy that prioritises pilot wellbeing alongside operational efficiency. Fatigue Risk Assessment processes must incorporate new hazard categories, including over-reliance on algorithmic alerts, cognitive underload resulting from task redistribution, and increased monitoring pressures associated with supervising autonomous functions. Safety Assurance within FRMS must transform into a continuous, adaptive process capable of monitoring AI performance, evaluating AI-human workload balance, and detecting early signs of fatigue-induced system drift. The paper proposes an integrated FRMS-AI governance model tailored to the future of human-AI teaming. This model incorporates predictive fatigue analytics, adaptive automation strategies, human-machine workload harmonisation, and AI-specific fatigue indicators within FRMS oversight. The findings emphasise that the success of human-AI teaming in aviation will depend not solely on technological progress but on the robustness of FRMS to manage evolving human cognitive vulnerabilities in increasingly automated operational ecosystems.
Debra Henneberry, Dimitrios Ziakkas, Eleftherios Bokas, Anastasios Plioutsias
Open Access
Article
Conference Proceedings
Human Factors Analysis and Classification System (HFACS) Applications in Transportation Human Factors: Review Study
As transportation systems become increasingly complex, human factors remain critical to safety performance. The Human Factors Analysis and Classification System (HFACS) has become one of the most widely adopted frameworks for accident causation analysis, providing a structured approach to analyzing unsafe acts, preconditions, unsafe supervision, and organizational influences. This paper reviews HFACS applications in transportation research over the past 15 years. First, the theoretical foundations and core structure of HFACS are introduced, along with representative analytical methods commonly employed in accident analysis studies. Subsequently, HFACS applications are reviewed and compared across major transportation sectors, including aviation, rail transit, maritime transport, and road traffic. Finally, certain persistent challenges are identified, including ambiguous hierarchical boundaries, limited representation of dynamic risks, insufficient quantitative rigor, and relatively low information utilization efficiency. Future research may emphasize strengthening quantitative integration, enhancing multi-scenario adaptability, and incorporating data-driven and intelligent analysis techniques to strengthen proactive risk management in transportation systems.
Yuanming Lv, Can Yang, Jundong Pei, Junmin Du
Open Access
Article
Conference Proceedings
Implementation of human teaming in aviation industry: The Turkish Airlines case study
The rapid digital transformation of commercial aviation has shifted organisational emphasis toward human–AI teaming models capable of enhancing operational efficiency, safety, and resilience. While global carriers are investing in artificial intelligence to optimise decision-making, training, and operational planning, the practical implementation of human–AI collaboration varies significantly across organisations. This paper presents an in-depth case study of Turkish Airlines, examining how one of the world’s largest network carriers has approached the integration of human–AI teaming across flight operations, training systems, and organisational decision structures. The study evaluates both the opportunities unlocked by AI-enabled capabilities and the human performance, cultural, and regulatory considerations that shape successful implementation.The analysis begins with an overview of Turkish Airlines’ digital transformation strategy, highlighting its investment in predictive maintenance, flight operations optimisation algorithms, crew rostering systems, passenger behaviour modelling, and data-driven safety programmes. While these systems are not yet fully autonomous, they increasingly act as collaborative partners—providing complex probabilistic forecasts, adaptive recommendations, and real-time decision-support inputs. This dynamic has begun to redefine the roles and cognitive demands placed upon flight crews, dispatchers, safety analysts, and operational managers, prompting the organisation to rethink how humans and AI systems jointly contribute to operational outcomes.The paper then examines the human factors and training implications associated with this transition. Interviews and document analysis reveal that the success of AI implementation hinges predominantly on the human element—specifically, trust calibration, mental model alignment, interpretability of algorithmic outputs, and the integration of AI-generated insights into high-stakes operational decisions. Within Turkish Airlines’ operational ecosystem, pilots and dispatchers express a dual dependency: appreciation for AI-driven efficiency gains and heightened concern regarding transparency, explainability, and potential loss of authority. These findings underscore the need for training approaches that go beyond procedural instruction and cultivate deeper cognitive skills in critical evaluation, cross-checking of AI outputs, and adaptive cooperation with intelligent systems.Furthermore, the study highlights organisational and cultural considerations unique to large network carriers. Turkish Airlines, operating in a highly multicultural and rapidly expanding environment, illustrates how cultural factors influence trust in automation, communication patterns, and acceptance of AI-driven recommendations. Organisational interviews indicate that a human-centric implementation requires harmonisation between technological innovation, training design, safety culture, and regulatory compliance. The absence of standardised human–AI teaming competency frameworks across regulators presents an additional challenge, particularly for multinational carriers operating across ICAO, EASA, and national oversight environments.The paper concludes with a proposed model for the aviation industry that draws on lessons from the Turkish Airlines case: (1) implementing explainable AI tools to support transparency and trust; (2) integrating AI-focused competencies within CBTA/EBT frameworks; (3) aligning training with human cognitive strengths; and (4) fostering organisational cultures that promote shared responsibility between humans and AI systems. The case study demonstrates that successful human–AI teaming in aviation is not driven by technology alone, but by the ability to adapt training, communication, and organisational culture to ensure safe and resilient collaboration.
Ibrahim Sarikaya, Dimitrios Ziakkas, Fatih Rustu Altunok
Open Access
Article
Conference Proceedings
Training Challenges in Human -AI Teaming in Aviation
Human–AI teaming is rapidly emerging as a defining paradigm in next-generation aviation operations, reshaping pilot roles, altering cockpit task distribution, and challenging established assumptions regarding expertise, decision-making, and training. As artificial intelligence systems evolve from deterministic support tools into adaptive, autonomous teammates capable of perception, prediction, and intent-driven action, the aviation training ecosystem faces a suite of unprecedented challenges. These challenges extend beyond purely technical skills and encompass deeper questions of trust calibration, cognitive adaptation, workload redistribution, ethical responsibility, and sustained human performance. This paper examines the central training challenges associated with preparing pilots, instructors, and organisational systems for effective human–AI teaming across current and expected future aviation environments.First, the paper analyses the shifting cognitive and operational landscape introduced by AI-enabled systems, including adaptive automation, predictive analytics, natural-language interfaces, and mixed-initiative control architectures. Whilst these technologies promise enhanced situational awareness, reduced workload, and strengthened predictive safety nets, they simultaneously introduce risks such as automation complacency, algorithmic over-reliance, erosion of manual competencies, and emergent forms of mode confusion. Training organisations must therefore rethink curriculum design to cultivate appropriate levels of trust in AI agents while strengthening pilots’ abilities to monitor, interrogate, and, where necessary, override AI behaviour during uncertainty or system drift. Traditional training paradigms based on linear automation logic are insufficient to address the probabilistic and at times opaque behaviour of modern AI systems.Second, the paper explores the pedagogical complexities inherent in developing joint human–AI decision-making skills. Effective teaming requires robust communication transparency, alignment of mental models, and the formation of shared situational awareness between human operators and algorithmic agents. Yet many AI systems operate as “opaque teammates,” offering outputs without interpretive depth or explainable reasoning. Training must therefore introduce strategies for evaluating machine-generated recommendations, identifying algorithmic bias, integrating AI insights with experiential human judgement, and managing discrepancies between human and AI interpretations. Scenario-based training, explainable AI (XAI) tools, and structured failure-mode exploration are presented as essential approaches for mitigating these challenges.Third, organisational, regulatory, and standardisation constraints are evaluated. The absence of harmonised human–AI competency frameworks, variability in AI system behaviour across aircraft types, and ambiguities regarding accountability pose obstacles for both initial and recurrent training. A critical need exists for evidence-based human factors methodologies that define the skills required for pilots operating in mixed-initiative or partially autonomous environments. Emerging competency-based training and assessment (CBTA/EBT) methodologies offer a promising foundation but require expansion to incorporate AI teaming competencies, error management strategies, and resilience-building mechanisms.The paper argues that training for human–AI teaming must remain fundamentally human-centric, preserving pilots’ adaptive expertise, situational awareness, and critical thinking while ensuring that AI systems remain compatible with human cognitive strengths and limitations. It concludes by proposing an integrated training model to support safe, resilient, and ethically aligned human–AI cooperation in future aviation operations.
Dimitrios Ziakkas, Ibrahim Sarikaya, Debra Henneberry
Open Access
Article
Conference Proceedings
Implementation of Human - AI teaming in the Single Pilot Operations Era.
Human–AI teaming emerges as the defining paradigm for Single Pilot Operations (SiPO), challenging aviation's foundational assumptions about cockpit redundancy, expertise, and training. This paper synthesises three complementary analyses: (1) historical decrewing patterns establishing phased implementation imperatives; (2) empirical validation of prompt engineering as a core Human–AI interaction competency improving task success by 64% in SiPO scenarios (Pechlivanis & Ziakkas, 2026b); and (3) a human-centred framework mapping SiPO competencies to observable SiPODigiComp behaviours across Professional Standards, Situational Awareness, Communication, Leadership, and Workload Management. Replacing social redundancy with algorithmic support demands interaction-centred training beyond traditional CRM/EBT/CBTA paradigms. Key challenges include trust calibration, AI interpretability, cognitive resilience, Human–AI communication, and ethical judgement under reduced social feedback. Scenario-based simulation incorporating validated prompt patterns (instructional, scenario-based) emerges as critical for developing adaptive expertise. Regulatory frameworks must mandate systematic workload validation and phased certification mirroring successful historical transitions. SiPO success hinges not on AI sophistication, but human-centred design preserving pilot authority as final moral agent within resilient Human–AI teams.
Konstantinos Pechlivanis, Dimitrios Ziakkas, Ioanna Lekea, Debra Henneberry
Open Access
Article
Conference Proceedings
The role of workforce planning in the implementation of Human - AI Teaming in Transportation
The increasing adoption of Human–AI teaming across the transportation sector is reshaping operational roles, organisational structures, and future workforce competencies. As artificial intelligence systems evolve from decision-support tools to collaborative teammates capable of perception, prediction, and autonomous action, transportation organisations must fundamentally rethink how they design, train, allocate, and sustain their workforce. Workforce planning—traditionally centred on staffing levels, qualification pipelines, and operational forecasts—now plays a pivotal role in ensuring that human capabilities remain aligned with the cognitive, technical, and ethical demands of future human–AI teams. This paper examines the strategic function of workforce planning in supporting the safe and effective implementation of human–AI teaming across aviation, rail, maritime, and ground transportation environments.The analysis begins by outlining the systemic transformations introduced by AI-driven operations. In contrast to earlier waves of automation, contemporary AI systems introduce non-deterministic behaviour, dynamic adaptability, and shared decision-making responsibilities. These characteristics challenge legacy workforce models premised on stable task distributions and predictable human roles. Across transportation modes, the shift toward human–AI teaming requires workforce planners to anticipate emerging competencies such as AI oversight, interpretability skills, mixed-initiative collaboration, and algorithmic risk assessment. The alignment of these competencies with recruitment, selection, training, and career progression becomes essential to preventing skill gaps, cognitive overload, and mismatches between human capabilities and system demands.The paper further examines workforce planning as a human factors instrument that supports organisational readiness. Effective planning requires an integrated understanding of future task redesign, shifts in workload dynamics, and the redistribution of responsibilities between humans, AI agents, and remote support structures. In aviation, for example, workforce planners must prepare for mixed-crew configurations, single pilot operations with AI copilots, and remote supervisory roles; in rail and maritime systems, the emergence of autonomous navigation and predictive maintenance similarly redefines operator roles. Planning must therefore incorporate scenario-based forecasting, human reliability analyses, and long-term modelling of human–machine performance interactions to ensure that human resource strategies align with technological evolution.Training and competency development are examined as critical components linking workforce planning to operational implementation. As human–AI teaming becomes central to safety-critical operations, organisations must develop training pathways that cultivate adaptive expertise, trust calibration, interpretability awareness, and resilience under automation. Workforce planning provides the structural basis for identifying training populations, sequencing AI-focused skills acquisition, and embedding competency-based training and assessment (CBTA/EBT) methods across the transportation ecosystem. The paper argues that overlooking this alignment risks creating workforces that are operationally certified but cognitively unprepared for high-automation environments.The study also addresses regulatory, cultural, and organisational constraints. Many transportation regulators have yet to establish competency frameworks for human–AI teaming, leaving workforce planners without standard definitions of required skills or acceptable performance thresholds. Organisational cultures may further influence AI acceptance, trust, and reporting behaviours, requiring workforce planning to integrate cultural readiness assessments and targeted change-management strategies.The paper concludes by proposing a workforce planning model designed to support large-scale adoption of human–AI teaming in transportation. This model integrates technological forecasting, human factors analysis, competency mapping, and AI-focused resilience strategies. The findings emphasise that technological innovation alone is insufficient; the success of human–AI teaming depends on a strategically designed workforce that is cognitively, operationally, and organisationally prepared for the transport systems of the future.
Dimitrios Ziakkas, Ibrahim Sarikaya, Konstantinos Pechlivanis, Debra Henneberry
Open Access
Article
Conference Proceedings
The Role of Safety Management Systems (SMS) in the implementation of Human - AI teaming in Aviation Ecosystem.
The integration of Human–AI teaming within the aviation ecosystem represents a transformative evolution in safety-critical operations, demanding robust organisational frameworks capable of managing emerging risks, validating new operational concepts, and sustaining human performance. As artificial intelligence becomes increasingly embedded in flight operations, maintenance, training, and safety analytics, the role of Safety Management Systems (SMS) becomes central to ensuring that human–AI collaboration is introduced, monitored, and governed in a manner consistent with international safety expectations. This paper examines how contemporary SMS principles support—and in many cases must be adapted to support—the safe and effective implementation of human–AI teaming across the aviation industry.The analysis begins by framing AI integration as a socio-technical challenge that profoundly alters hazard identification, risk modelling, and safety assurance processes. AI-enabled systems introduce unique characteristics—opacity, non-determinism, continuous learning, and probabilistic behaviour—that challenge conventional safety assumptions. SMS, traditionally grounded in predictable system performance, must expand to accommodate risks arising from algorithmic drift, data quality variability, automation bias, and human–machine misalignment. The paper argues that SMS frameworks must evolve beyond compliance-driven practices to embrace dynamic, data-rich safety monitoring capable of detecting emergent patterns of human–AI interaction.The study further explores how each component of SMS—Safety Policy, Safety Risk Management, Safety Assurance, and Safety Promotion—contributes to the governance of human–AI teaming. Within Safety Policy, organisational commitments must reflect a human-centric philosophy ensuring that AI systems complement, not replace, human cognitive strengths. Safety Risk Management must incorporate new methodologies for identifying hazards associated with collaborative automation, including unintended consequences of predictive algorithms, mismatches between AI intent and pilot expectation, and reduced redundancy in single-pilot or high-automation environments. Safety Assurance processes must evolve to include continuous performance monitoring of AI agents, explainability audits, validation of training effectiveness, and mechanisms for detecting shifts in human–AI trust relationships.Safety Promotion is examined as a crucial enabler of cultural readiness. The introduction of AI into safety-critical operations requires transparent communication, cross-disciplinary literacy, and training programmes that cultivate both confidence and critical scepticism toward AI-generated outputs. Emphasis is placed on building a safety culture that encourages reporting of anomalies involving AI systems, fosters shared understanding between technical and operational personnel, and supports learning from human–AI interaction events. The Turkish Airlines, Lufthansa Group, and FAA/EASA regulatory developments are referenced as indicative of industry movement toward SMS-driven oversight of intelligent systems.The paper concludes by proposing a strengthened SMS framework tailored to human–AI teaming. This enhanced model integrates explainable AI within risk assessment processes, adopts resilience engineering principles to manage uncertainty, incorporates AI-specific safety indicators, and emphasises adaptive training frameworks aligned with CBTA/EBT approaches. The findings suggest that the long-term success of human–AI teaming in aviation will depend not solely on technological capability but on the ability of SMS to anticipate, govern, and continuously validate the evolving dynamics of human–AI collaboration.
Dimitrios Ziakkas, Eleftherios Bokas, Debra Henneberry
Open Access
Article
Conference Proceedings
Assessing Signal Detection Performance Under Operational Fatigue in Air Traffic Controllers
Fatigue and sleepiness are critical safety issues for air traffic controllers (ATCOs) that impair cognitive performance and vigilance (Bendak & Rashid, 2020; Wingelaar-Jagt et al., 2021). They typically result from insufficient or disrupted sleep, extended duty periods, and work schedules misaligned with circadian rhythms, common in ATCO operations (Bendak & Rashid, 2020 ; Eastman & Smith, 2012 ; Wingelaar-Jagt et al., 2021). This study quantified signal detection performance in ATCOs compared to healthy controls and examined how performance varies across day and night shifts and evolves over a shift. Seventeen licensed ATCOs working rotating shifts completed a 3-minute Behavioural Sleep Resistance Task (BSRT) before their shift, during a break, and after the shift. A laboratory reference group of 36 healthy young adults following 20 hours of wakefulness served as a fatigue benchmark. The BSRT measured processing speed, attentional lapses, neurobehavioral stability, and omission errors, completed by Karolinska Sleepiness Scale (KSS) ratings. Results showed progressive cognitive slowing, increased reaction times and reduced optimal responses. Attentional lapses accumulated over time and neurobehavioral stability declined, particularly during night shifts. Despite these effects, omission errors remained low, indicating maintained task engagement. Subjective sleepiness increased across shifts and was higher at night. Comparison with laboratory benchmarks indicated that some impairments were present pre-shift, suggesting sustained baseline fatigue from cumulative sleep restriction and incomplete recovery. Findings highlight the combined influence of time-on-task and circadian vulnerability on vigilance, with ATCOs employing compensatory strategies that trade speed for accuracy to maintain operational safety.
Clemence Drogoul, Berenice Delwiche, Fabrice Drogoul, Olivier Mairesse
Open Access
Article
Conference Proceedings
Action-Oriented Pilot Training
Pilot training aims to cultivate the knowledge and skills needed for proficient pilot performance. These include technical and nontechnical aspects. The technical aspects of performance are well defined and understood. The nontechnical aspects, however, are often defined in vague and subjective terms that make them difficult to reliably train and assess. Several frameworks have been developed to attempt to define the essential nontechnical aspects of pilot performance. These include the Joint Aviation Authority’s (JAA) Non-Technical Skills (NOTECHS), Crew Resource Management (CRM), Threat and Error Management (TEM), and to some degree, Competency-Based Training and Assessment (CBTA). To understand how airlines train and assess pilot performance, data were collected at four major U.S. Airlines and one major European Airline. A series of interviews were conducted in the form of discussion groups and one-on-one interviews with Pilots, Check Pilots, Flight Instructors and airline Training and Standards Managers. Observations of Line-Oriented Flight Training (LOFT) sessions and analyses of airline training materials were also conducted. The results suggest the core issues in training today lie with training and assessing nontechnical aspects of performance and integrating nontechnical with technical performance. While NOTECHS, CRM, TEM, and CBTA provide descriptions of nontechnical aspects of pilots’ work, they do not explicitly specify what the pilots need to do. The aim of this research is to make nontechnical performance explicit. In this paper, a pragmatic shift to action-oriented training is introduced. The action-oriented training framework was developed by applying modern cognitive theories to training and assessment.
Barbara Holder, Christopher Reed
Open Access
Article
Conference Proceedings
The Gold and the Failed Results of Artificial Intelligence in Aviation
When James Lee wrote his perceptive assessment of management theories in 1980, he described the achievements (gold) and failed results (garbage) as a caution to practitioners. Considered in the light of current applications of artificial intelligence (AI) in the aviation arena, this approach proves beneficial in examining the pros and cons currently and assessing the positive achievements as well as the less successful efforts. Following a brief review of the origins of AI, representative successes of AI applications in flight deck operations, air traffic management, maintenance, aircraft design, airport operations and airline management are provided with specific examples that demonstrate how AI is proving beneficial. Among these are uses in fleet operations, crew scheduling, safety risk management, fuel efficiency, and twelve other uses. Conversely, two seasons of AI growth and decline are explained and the ensuing resiliency which is propelling the current momentum of expectations. The Gartner Hype Cycle of AI is described with attendant promises and potential disappointments related to aviation. Then, an overlay of the AI roadmaps from aviation safety agencies are given to indicate timelines for expected development and growth. When the seasons, Hype Cycle, and roadmaps are considered together, patterns emerge indicating likely milestones and breakthroughs for AI in the aviation environment. These combine to show pathways that the gold and garbage may take. Illustrations of how AI fails are discussed with perspectives on operator trust in AI and limitations that are becoming more evident. Looking forward, technologies that are advancing AI in aviation are identified and the growing use of agenic AI is considered in the aviation context. Neural versions of AI and quantum computing are assessed as next generation AI applications.
Sam Holley, Mark Miller, Leila Halawi
Open Access
Article
Conference Proceedings
Cognitive reinforcement for aircrew coordination with autonomous collaborative platforms in next-generation fighters
High-tempo domains such as defense, medicine, and transportation have been transformed by AI-based systems that accelerate information processing and decision support. Modern air combat reflects this evolution, with automated systems continuously generating tactical options that require aircrews to decide more frequently under time pressure. This shift is exemplified by the deployment of Autonomous Collaborative Platforms (ACPs), i.e., AI-enabled teamed drones operating alongside crewed fighters and proposing actions such as target validation. This study examines whether the processing of target-validation situations proposed by ACPs can be improved through cognitive training. Forty military aircrew members participated in a virtual reality flight mission and were assigned to either a control or a training group. Both groups completed identical pretest and posttest separated by 24 hours, while only the training group performed a 45-minute computer-based training session focused on repeated target-validation tasks. Results show that training significantly reduced error rates and response times, in line with theories of expertise acquisition. In contrast, perceived workload decreased similarly in both groups, suggesting a dissociation between objective performance gains and subjective workload. These findings support the relevance of cognitive training for preparing aircrews for future collaborative combat involving ACPs.
Jean-christophe Hurault, Marianne Jarry, Grégory Froger, Anne-lise Marchand, Colin Blättler
Open Access
Article
Conference Proceedings
Pilot Acceptance of Reduced Crew Operations in Commercial Aviation: An Empirical Analysis of Human Factors, Trust, and Perceived Safety
The ongoing advancement of cockpit automation and the increasing shortage of qualified pilots have intensified discussions on Reduced Crew Operations (RCO) in commercial aviation. RCO comprises two main concepts: Extended Minimum Crew Operations (eMCO), which temporarily reduce cockpit crew during cruise, and Single Pilot Operations (SiPO), which envisage a single pilot on board throughout the entire flight. While technological progress suggests growing feasibility, pilot acceptance remains a critical human factors challenge.This paper investigates pilot acceptance of RCO from a human factors perspective, with a particular focus on trust in automation, perceived safety, and job-related concerns. An empirical mixed-methods study was conducted using an online survey among active, former, and prospective commercial pilots. Quantitative data were analyzed using descriptive and inferential statistical methods, while qualitative responses were examined through structured content analysis. The results indicate an overall low level of acceptance toward RCO, with particularly strong rejection of SiPO. Safety concerns, increased workload, and the perceived irreplaceability of a second pilot were identified as dominant barriers. Acceptance of eMCO was moderately higher but strongly conditional on reliable automation, transparent system behavior, and robust organizational safeguards. Statistical analyses reveal a significant positive relationship between trust in automation and acceptance of RCO, as well as a significant negative relationship between age and acceptance. Other factors, including flight experience, professional position, aviation sector, and perceived job insecurity, showed no significant effects. The findings highlight pilot acceptance as a decisive prerequisite for the implementation of RCO concepts and emphasize the importance of human-centered automation design, trust calibration, and transparent safety strategies in future cockpit systems.
Melanie Kranich, Sumona Sen, Patrick Poetters
Open Access
Article
Conference Proceedings
Understanding ownership effects within the human-centred XR co-design process for aircraft cabin concepts
In user-centred aircraft cabin design, Extended Reality (XR) co-design enables early, immersive user involvement, translating stakeholder needs in real time into novel design concepts. While prior research has focused on technical aspects, social and psychological dynamics, such as ownership effects, remain underexplored across different immersion levels in XR co-design.This study investigates how physical and virtual design elements influence ownership perceptions among aircraft passengers. As part of DLR's EXACT2 hydrogen aircraft project, 72 participants collaboratively designed a cabin space for passengers as additional area besides the own seat using two XR co-design variants: (1) Purely virtual (n = 43), using AR (Meta Quest 3, Gravity Sketch) for 1:20 to 1:1 virtual co-creation; (2) hybrid (n = 29), starting with physical 3D-printed 1:20-scale components before Augmented Reality (AR)-based refinement. Psychological and individual ownership were assessed post-design. Both XR co-design approaches were successfully performed and analysed.
Fabian Reimer, Sebastian Cornelje, Jessica Herzig, Line Winkler, Jörn Biedermann, Bjoern Nagel
Open Access
Article
Conference Proceedings
Physical vs Virtual Design: Advancing the XR+ Method for Facilitated Passenger Aircraft Cabin Co-Design
Developing aircraft cabin interiors requires balancing operational needs with evolving passenger expectations. Translating the lived passenger experience into early spatial concepts remains challenging. This paper describes a series of facilitated co-design workshops enabling non-designer stakeholders (passengers) to create aircraft cabin layouts from scratch. In 18 time-boxed group sessions lasting 120 minutes each, passengers were instructed to create concepts for a long-haul space beyond the seat, also known as a ‘third space’. These sessions utilised the XR+ methodology: a workflow that combines ideation on reduced-scale (1:20) with immersive full-scale (1:1) refinement in XR. While prior XR+ applications focused on professional stakeholder groups in cabin-related contexts, the present work extends XR+ to passenger-led co-design. Furthermore, two variants of the method are tested: an all-virtual approach (XR scale 1:20 → XR scale 1:1) against a hybrid approach (physical scale 1:20 → XR scale 1:1). Results show that novice passengers in both workflows produced concept artefacts within a single session, as evidenced by the completion of layout prototypes and facilitator observations. Facilitators observed that the 1:20 scale supports rapid layout creation and a shared negotiation space. This was followed by embodied spatial refinement at 1:1 scale. Key practical differences between the workflows were (i) where facilitation effort is needed most and (ii) continuity of artefacts across stages. In conclusion, practical workflow trade-offs to inform the adoption of XR+ variants in early aviation research and ideation are summarised.
Sebastian Cornelje, Fabian Reimer, Jessica Herzig, Line Winkler, Jörn Biedermann
Open Access
Article
Conference Proceedings
Explainable AI for Emergency Landing Decisions: A Comparative Study of Learning Classifier Systems and Neural Networks
During mid-flight emergencies, pilots must rapidly assess multiple operational and environmental factors to select a safe alternate landing airport. This Dynamic Alternate Airport Selection (DAAS) process requires fast, reliable decision-making under high cognitive load. Although established cockpit procedures such as those in the QRH provide essential guidance, additional data-driven support tools could further help pilots manage complex information under time pressure. Different Artificial Intelligence (AI) methods offer promising opportunities in this regard, however, for aviation applications, it is necessary that the applied methods are both, accurate and transparent, and that their decision logic is explainable to pilots. Accordingly, this paper investigates which AI methods are most suitable for modelling pilot behaviour in emergency airport-selection tasks while maintaining a high degree of explainability to foster trust in the system. Using a dataset derived from an online survey of professional pilots capturing their preferences across emergency diversion scenarios, and expanded through structured data augmentation to generate 7,140 labelled decision scenarios, the study evaluates two variants of interpretable Learning Classifier Systems (LCS), using Hyperellipsoid and Hyperrectangle conditions, whose decision-making is encoded in explicit, human-readable IF–THEN rules that enable direct inspection of how inputs lead to decisions. These models were contrasted with a more modern, non-interpretable baseline: a Feedforward Neural Network (FNN). The models were designed for single-instance classification using a scoring framework. The scores were used to label the augmented dataset by combining the scenario scores with the Euclidean distance between the original decision scenarios and the unique airport combinations generated within each scenario. Model performance was evaluated using accuracy and interpretability considerations, key factors for integration into cockpit decision-support systems. The Hyperellipsoid LCS achieved the highest accuracy (86.34%), demonstrating strong adaptation to multidimensional feature interactions. The Hyperrectangular LCS offered greater rule-level transparency but lower accuracy (78.33%), while the FNN achieved intermediate accuracy (82.20%) with limited inherent interpretability. Results show that the Hyperellipsoid LCS provides the best overall balance between predictive performance and transparency, outperforming the Hyperrectangle LCS and the FNN. These findings indicate that ellipsoid-based LCS models offer a promising foundation for trustworthy AI components in future pilot decision-support systems.
Evelyn Yamilet Quintero Guzman, Jakob Suchan, Boris Djartov
Open Access
Article
Conference Proceedings
Human Body Models for Crash Safety Analysis of Reclining Posture Occupants: Applicability and Adaptation
With the advancement of aviation, the low-altitude economy, and autonomous driving technologies, occupant postures are progressively evolving from traditional seated positions to reclining configurations. However, existing human body models and evaluation methods, developed based on seated postures, are not directly applicable to the analysis and assessment of occupant injuries in reclining postures. This paper focuses on the applicability and adaptability of human body models for crash safety analysis of occupants in reclining postures. It reviews the modeling characteristics, advantages, and limitations of common human body models. The suitability of these models for reclining postures is analyzed in terms of geometric configuration, joint range of motion, and biomechanical response. Furthermore, the study explores model types suitable for reclining postures, examines the fundamental reasons why existing models are difficult to apply directly to crash risk analysis of reclining occupants, and proposes targeted model adjustment strategies to more realistically simulate body configuration and dynamic responses in reclining postures. The results provide methodological guidance for enhancing the fidelity and reliability of crash simulations involving reclining postures, contributing to the development of more inclusive occupant protection systems and promoting safety design for multi-posture intelligent cabin environments.
Can Yang, Jundong Pei, Yuanming Lv, Huimin Hu, Junmin Du
Open Access
Article
Conference Proceedings
Human Factors Associated with Startle and Surprise Events in Aviation: A Large-Scale Analysis of NASA ASRS Reports
This study examined the prevalence and human factors correlates of startle and surprise events in commercial aviation using a large-scale analysis of NASA Aviation Safety Reporting System (ASRS) narratives. While startle and surprise effects have been identified as contributors to loss-of-control events and other critical incidents, prior empirical research has relied primarily on small-sample surveys, laboratory studies, or case analyses of individual accidents. The present study extends this work by analyzing the co-occurrence of pre-coded human factors with startle-related language across a decade of confidential incident reports. A keyword-matching algorithm was applied to 38,655 ASRS reports (2012–2022) to identify 2,642 reports (6.8%) containing startle/surprise-related language. Chi-square tests, odds ratio analyses with 95% confidence intervals, and logistic regression were used to compare human factors, flight phase distributions, anomaly types, and outcomes between startle-flagged and non-startle reports. All major human factors—including Fatigue (OR = 1.86, p < .001), Physiological factors (OR = 2.11, p < .001), Workload (OR = 1.60, p < .001), and Confusion (OR = 1.38, p < .001)—were significantly over-represented in startle reports. Logistic regression confirmed Physiological factors (β = 0.75) and Fatigue (β = 0.48) as the strongest independent predictors. Loss of aircraft control was 2.4 times more prevalent in startle reports. These findings provide large-scale empirical evidence that fatigue, physiological vulnerability, and high workload significantly amplify the risk and severity of startle reactions in operational aviation, supporting the development of targeted Crew Resource Management interventions and evidence-based training protocols.
Preeti Yogendra Sharma, Venkat Ram Reddy Ganuthula
Open Access
Article
Conference Proceedings
Software-based Evaluations of Cabin Processes in Civil Commercial Aircraft
In our research project, RECab (Resource Efficient Cabin), which focuses on the sustainable and resource-efficient design of aircraft cabins, resource-efficient cabin interior and process architectures have been explored. The solutions then support a high degree of design flexibility and usability within the cabin and, above all, in the associated operational processes. All three dimensions of sustainability — ecology, economy, and social acceptance — are considered. A function-oriented approach leads to optimized cabin architectures and process concepts for onboard services. Mission-specific constraints and conditions are considered when defining suitable process concepts. Through simulation — with the goal of creating a digital process twin — suitable process concepts are evaluated, and recommendations are provided for process design and resource allocation. Optimized cabin processes include the evaluation and optimization of directly upstream and downstream processes at the airport, as these are interdependent. There is significant potential for optimization in the aircraft cabin if, e.g., unnecessary variability and complexity can be eliminated from the cabin and shifted, for example, to upstream and downstream processes. In particular, the economic and ecological dimensions of sustainability are influenced by ergonomics, i.e., the appropriate design of the onboard processes for users. We asked ourselves how well work planning simulation software is suited for assessing and optimizing the ergonomics of cabin processes. As a result of a methodological evaluation, EMA Work Designer — originally developed for factory planning — was identified as suitable simulation software for cabin processes. It is used to determine suitable strain metrics, such as, e.g., the EAWS score, considering various users and operators, and to identify process steps critical in terms of strain.Using examples of aircraft cabin process evaluations, we conclude by demonstrating the possibilities and limitations of the simulation software and where further potential for optimization lies in its use. The use of simulation software — such as ema Work Designer in our case — leads to a better understanding of socio-technical systems through the creation of suitable digital process twins and offers the possibility of immediate evaluation, mitigation, and optimization of processes, in our case aircraft onboard service processes.
Leonard Erb, Gordon Konieczny
Open Access
Article
Conference Proceedings
AI Competency Model for Aerospace Engineering Managers
As crewed spaceflight and deep-space exploration accelerate, aerospace engineering organisations are adopting digital and AI-enabled management to meet higher mission cadence, stringent compliance, and evidence-based assurance needs. Yet generic competency frameworks do not adequately capture the role-specific competencies required under safety-critical and highly traceable governance. This study developed and validated an AI competency model and closed-loop assessment framework for aerospace engineering managers. Competency constructs were elicited via RepGrid interviews (n=30) and validated using PCA (n=217) and CFA (n=209). The resulting five-dimensional model showed good internal consistency (Cronbach’s α=0.900) and acceptable fit (RMSEA=0.026; SRMR=0.045). Expert judgements (n=14) were used to model interdependencies and derive system-aware weights via DEMATEL-DANP. In a recruitment scenario with five candidates (H1-H5) rated by four experts, an improved VIKOR method produced a compromise ranking and gap diagnosis; H5 remained top-ranked across the tested V settings (V=0.1-0.9). The framework provides interpretable ranking and diagnostic outputs to support human-centered, AI-enabled, traceable decisions for selection and development across the project lifecycle.
Huizhong Hu, Tian Ye, Ao Jiang, Mingzhao Xu, Rui Hou, Yong Su, Yong Ni, Shengchi Li, Chenyu Ouyang, Zhou Jun, Mulan Huang, Jinglin Zhang
Open Access
Article
Conference Proceedings
Multi-Parameter Optimization of Augmented Reality Display Interfaces for Lunar Extravehicular Activities: Impacts of Area, Shape, and Transparency
The lunar surface presents a challenging visual environment characterized by high glare, extreme dynamic range, and achromatic conditions, which severely compromise the visibility of augmented reality (AR) information overlays. This study systematically examines the synergistic effects of geometric (area and shape) and optical (transparency) parameters on AR display interfaces, assessing their impact on task performance and cognitive load in a simulated lunar setting. The investigation consists of two experimental series: Experiment 1 utilized a digit-matching paradigm to manipulate display area and shape, while Experiment 2 employed omnidirectional pointing and cube placement tasks to vary interface transparency. Conducted in a controlled laboratory replicating the low solar elevation angle illumination typical of the lunar south pole, the experiments incorporated the optical filtering properties of a manned lunar helmet visor to bolster ecological validity. Multidimensional analyses demonstrated that larger display areas led to prolonged reaction times, decreased accuracy, and reduced usability, indicative of an "area penalty effect." Conversely, lower interface transparency markedly improved task completion times, diminished subjective cognitive load, and enhanced emotional valence (pleasantness) and dominance. This research constitutes the inaugural comprehensive evaluation of multi-parameter AR interface optimization in a simulated lunar context, furnishing essential human factors insights for the parametric design and adaptive refinement of future AR helmet displays in lunar extravehicular activities (EVAs).
Ao Jiang, Guangxu Li, Tian Ye, Haoling Yang, Jinglin Zhang, Yuhuan Deng, Rui Yang, Can Chen, Shangdong Xu, Xiaocheng Li, Bernard Foing, Stephen Westland
Open Access
Article
Conference Proceedings
Effects of Acoustic Design Parameters of Aural Alerts on Reaction Time in Commercial Aircraft Cockpits
In high-workload and time-critical commercial aircraft cockpits, pilots must rapidly perceive aural alerts and initiate operational responses. However, systematic evidence on how specific acoustic design parameters influence operational reaction time remains limited. This study investigates the effects of alert temporal structure and prosodic modulation on reaction time. A laboratory-based auditory reaction experiment was conducted with 30 participants using a fully within-subject design. Three representative alert categories were tested: tonal alerts, voice alerts, and combined tonal–voice alerts. Signal duration, interval structure, and voice tone (monotone vs. urgent) were systematically manipulated. Reaction time data were analyzed using linear mixed-effects modeling. Results indicate that increasing acoustic temporal density—by shortening signal duration and intervals—significantly reduces reaction time. The most pronounced improvements were observed in combined tonal–voice alerts under temporal compression. However, diminishing returns emerged at higher compression levels. In the voice alert condition, urgent prosody further shortened reaction time compared with monotone delivery. Semi-structured interviews with ten active commercial pilots supported these findings, suggesting that temporally compact and clearly articulated alerts facilitate faster responses. This study proposes a reaction-time-based quantitative evaluation framework for cockpit aural alert design and provides empirical guidance for optimizing safety-critical auditory systems in commercial aviation.
Erzhuo Huang, Lin Du, Xiaohui Hao, Xiaodan Zheng, Chunling Zhao, Tan Hao
Open Access
Article
Conference Proceedings
Making Delays Glanceable: Color-Coded Status and Autogenerated Tickers for LED-Matrix Passenger Information Displays
Compared with departure monitors in other dynamic passenger information media, classic LED‑matrix displays at stops typically provide less information. This is largely due to their limited resolution and, in many legacy deployments, monochrome presentation. This paper describes selected extensions that can be implemented even within the constraints of LED‑matrix technology and examines them in more detail by means of an online survey. The extensions focus on (1) distinguishing between on‑time operation, delays, and missing real‑time data, and (2) automatically generating short information messages when a departure is no longer shown on the display because a delay has pushed it out of the fixed, chronologically sorted list of visible rows. Survey results indicate that both functionalities can be perceived as helpful and may increase users’ confidence in data quality. Additional information can reduce the need to consult apps on personal devices and can support decision‑making during irregular operations, potentially shortening perceived or actual travel times. In particular, automatic messages about ‘missing’ departures may reduce misunderstandings (e.g., confusing a delayed trip with a cancellation) and therefore appear especially relevant for legacy systems with a limited number of display lines.
Waldemar Titov, Thomas Schlegel
Open Access
Article
Conference Proceedings
Understanding the Voice of the Press. A KPI-driven Approach to Analyzing Press Requirements for Technology Development
Automotive press reviews contain rich descriptions of vehicle behavior, yet their qualitative nature limits their use in human-factors engineering. This paper introduces a concise and reproducible method for converting press narratives into structured criteria relevant to vehicle development. The approach transforms unstructured journalistic text into actionable human-factors insights through a structured qualitative analysis process including systematic statement extraction, thematic coding, sentiment tagging, and expert validation. A curated sample of review articles from a single automotive publication forms the dataset. Each article is screened for evaluative statements describing aspects of drivability, user experience, comfort, handling, braking, acoustics, and related vehicle attributes. Extracted statements are coded by sentiment, discipline, and thematic content. This process yields a large pool of unique descriptive expressions, which are then clustered into coherent themes through iterative qualitative analysis. The themes form the foundation of discipline-specific criteria that capture how journalists describe vehicle qualities such as steering precision, ride comfort, interface usability, and perceived performance. Sentiment tagging highlights which attributes are often criticized, which are praised only when exceptional, and which draw balanced commentary. Although no quantitative results are reported, the method shows how press perceptions can reveal asymmetries in user expectations and areas of potential dissatisfaction. Expert workshops refine the criteria to ensure technical consistency and reduce coder bias. The method provides a scalable framework for integrating experiential press language into early-phase requirements engineering, benchmarking, and user-centered product development, offering human-factors practitioners a structured way to interpret the press voice and derive criteria that reflect real-world perceptions of vehicle performance.
Gioele Micheli, Seda Aydogdu
Open Access
Article
Conference Proceedings
Investigating the impact of driving workload on fatigue and performance for the purpose of road safety
Driver fatigue is a major contributor to road accidents, yet it remains difficult to quantify due to its subjective nature and inconsistent reporting. This experimental study investigates how fluctuations in driving workload influence fatigue development and driving performance. Utilizing a high-fidelity car simulator, thirty non-professional drivers participated in a controlled experimental protocol comparing optimal driving scenarios with stressful driving conditions characterized by heavy traffic and adverse weather. To ensure a comprehensive assessment, an integrated multimodal approach was adopted, combining continuous physiological monitoring (electroencephalography - EEG, electrocardiography - ECG) with psychomotor tests such as Flicker Fusion test, and reaction times. Subjective workload was further assessed using the NASA-TLX questionnaire. Preliminary results indicate a statistically significant reduction in the Flicker Fusion metrics during stressful driving sessions, consistent with the onset of mental fatigue. These findings are corroborated by increased NASA-TLX scores and a decrease in EEG-derived attention and stress metrics. Other measured parameters showed no significant differences between the two sessions, suggesting the need for further experimentation to investigate the underlying causes and potentially refine the experimental design. The proposed holistic framework for assessing driver state can offer valuable insights for the design of targeted road safety interventions, infrastructure improvements, and tailored driver training programs aimed at enhancing overall drivers’ well-being and safety.
Patrizia Serra, Alessandro Murru, Simone Podda, Mattia Porta, Gianfranco Fancello
Open Access
Article
Conference Proceedings
The Technology Era, Truck Platooning, Truck Drivers’ Activity, and Risk Awareness
Technological development in the automotive industry is shaping the future of transport services to meet the mobility needs of the population and ensure the timely provision of the required goods, when and where they are needed. Thus, innovative solutions for decarbonization and economic improvement are being put into practice, imposing risk awareness and behavioral adaptation. In the field of Transport, the coming years will require a focus on providing transport systems with robust adaptability to rapid, unexpected changes, planning, and learning from each one. This requires human resources to be prepared for continuous learning, adapting their activity to the technological development, and stimulating creativity as a resource to anticipate, adapt, and improve. In this perspective, new needs and priorities have been identified to be met by anticipating and reinforcing adaptation capacity and by constructing innovative knowledge and tools to support the required adaptation to a changing world and rapid technological development.Automation appears to be an appropriate solution, particularly the introduction of truck platooning to improve efficiency in the European freight transport market. Driver-assistive truck platooning involves a convoy of two or more trucks, with a human driver in the lead truck and a partial automated system controlling the following trucks. These vehicles use connectivity technology, such as Vehicle-to-Vehicle (V2V) communication and Cooperative Adaptive Cruise Control (CACC), to automatically maintain a close, predefined distance. The technology offers benefits such as reduced fuel consumption through aerodynamics, improved traffic flow, and enhanced road capacity, while drivers remain in control and can intervene when necessary.
Anabela Simoes
Open Access
Article
Conference Proceedings
Effects of Multimodal Whole-Body Vibration Exposure in Automobiles on Occupants’ Tactile Perception
As automated vehicles develop, occupants will shift from drivers to passengers. They will use touch interfaces, mobile devices, and other systems inside vehicles. In these situations, tactile perception becomes important. At the same time, occupants are exposed to whole-body vibration (WBV) generated by vehicle motion. Thus, the effects of vibration on ride comfort have been studied. While there are some reports on the effects of whole-body vibration on human perception, such as visual and acoustic perception, its influence on tactile perception remains insufficiently understood. Participants performed a pairwise comparison task of tactile roughness under different vibration conditions and visual display conditions using a driving simulator. Three vibration conditions (large, small, and none), two visual display conditions (single-screen and triple-screen), and three time points (0, 30, and 60 min) were examined. Psychological scale values were calculated using Thurstone’s method of paired comparisons, and time-course changes in perceptual distance relative to baseline were analyzed. The results indicated that vibration exposure did not reduce tactile discrimination as initially hypothesized. Instead, perceptual distance tended to increase under small vibration conditions, while large vibration caused a slight decrease. A repeated-measures ANOVA revealed a marginal tendency for the main effect of vibration (p = 0.067), whereas no significant effects of visual condition or exposure time were observed. As the number of participants in the experiment is only 10, we will continue the experiment with more participants to develop a guideline for designing the interior of the fully automated vehicle in the future.
Junya Tatsuno, Tamaki Tsuchioka, Kazuma Ishimatsu, Setsuo Maeda
Open Access
Article
Conference Proceedings
Effects of Viewpoint Presentation Methods on Driving Workload in Vehicle Teleoperation Systems
The growing demand for logistics in Japan coupled with severe driver shortages has accelerated the development of teleoperational systems for autonomous vehicles. This technology allows a remote operator to supervise and intervene when autonomous driving is not feasible, thereby enabling a single operator to effectively manage multiple vehicles. Although previous studies suggest that alternative perspectives, such as bird’s-eye views or virtual-reality-based visualizations, can improve situational awareness, their specific impact on operator workload remains insufficiently explored. This study aimed to identify optimal viewpoint presentation methods by examining their influence on both mental and physiological workloads. In our experimental protocol, participants navigated courses modeled after standard driver’s license training routes, which required a moderate level of driving skill. We evaluated operator experience through validated questionnaires focusing on situational awareness and task difficulty. Physiological stress levels were analyzed using skin conductance to provide objective data. The findings clarify the viewpoints that are most effective under varying operational scenarios, offering critical insights for designing efficient remote driving interfaces and improving overall system safety.
Taiga Fukuzawa, Kazunori Kaede, Keiichi Watanuki
Open Access
Article
Conference Proceedings
Personalized modifications of vehicle interiors for social inclusion
Limitations in vehicle interior design restrict independent mobility for individuals with hand-related difficulties, reducing accessibility and social inclusion. This study presents a digitally driven methodology for developing personalized and inclusive vehicle interior solutions through the integration of 3D scanning, virtual ergonomics, and additive manufacturing. The research addresses geometric and ergonomic constraints in conventional vehicle interiors and translates user-specific anthropometric and functional requirements into optimized design interventions. The proposed workflow combines design thinking with ethnographic research, reverse engineering, ergonomics, and anthropometry. Following an analysis of existing inclusive solutions, qualitative user study identified task-specific limitations and usability requirements. Specific control elements in the vehicle interior are digitized using structured-light 3D scanning, while user anthropometric data and functional grip parameters are integrated into digital models. These datasets are evaluated in a virtual ergonomics environment to simulate user–vehicle interaction and assess alternative design configurations. Based on simulation outcomes, customized assistive interior components are developed and fabricated using additive manufacturing. The resulting digital-to-physical workflow reduces iteration cycles, development time, and cost while improving functional accuracy and user satisfaction. The methodology is applicable across a wide range of inclusive mobility and product design scenarios.
Elena Angeleska, Jelena Djokikj, Anita Vasileva Ljubotenska, Vasko Changoski
Open Access
Article
Conference Proceedings
Accident Guardian: A Low-Cost Intelligent Collision-Detection System for Enhanced Emergency Response
Traffic accidents remain a major global safety concern, particularly when emergency response is delayed due to driver incapacitation, remote crash locations, or the absence of witnesses. In the UAE, delayed accident reporting continues to contribute to avoidable injuries and fatalities, especially for vehicles lacking built-in emergency call systems. This paper presents Accident Guardian, a low-cost, aftermarket intelligent collision-detection system designed to automatically identify severe vehicle impacts and transmit real-time GPS location data to emergency services and designated contacts. The proposed system integrates impact and motion sensors, GPS tracking, and IoT-based communication within a compact in-vehicle module. To reduce false alarms, a driver verification mechanism is incorporated via the vehicle’s dashboard interface, enabling manual cancellation when assistance is not required. The system was developed using an interdisciplinary I-Team and design thinking methodology, combining user-centered design with technical feasibility analysis. Prototype development and simulated crash scenarios demonstrate reliable detection and timely alert transmission, while qualitative feedback from accident survivors, emergency responders, and medical professionals highlights the system’s potential to improve emergency response efficiency and enhance perceived safety. The results indicate that Accident Guardian offers a practical and scalable solution for improving post-crash response, particularly for older vehicles, and aligns with smart-city transportation and road safety initiatives in the UAE.
Maitha Alhosani, Rawdha Almarzooqi, Asma Alheera, Fatima Aldhaheri, Ahmed Shuhaiber
Open Access
Article
Conference Proceedings
Back Behind The Wheel: Musically Induced Emotions and Driving Behaviour in an Immersive Simulator
Music listening is one of the most common activities that people engage in when behind the wheel. The aim of the present study was to raise situational awareness of both the risks and benefits associated with in-vehicle music use via a highly engaging simulator experience. To do so, we offered participants an interactive experience in an immersive and high-fidelity simulator, where they were required to navigate a dynamic city centre environment. We collected data to ascertain the impact of the simulation task on users’ road safety awareness. We also assessed how participants responded to two contrasting music conditions in psychological (e.g. pleasure, mental workload, intention to use) terms. The trials were administered using a within-subjects design, with 3 × 3 min scenarios for each participant in a counterbalanced order. An urban driving simulation was employed with facilitative music, debilitative music and a no-music (urban traffic noise) control condition. Each experimental condition included one of three potential hazardous event – a delivery rider on the side of a road, a dog crossing or a parked car. Results showed no effect of the type of music or event on self-reported emotions and workload. However, both young and more experienced participants indicated that using an immersive driving simulator raised their awareness of safe driving behaviours vis-à-vis in-vehicle music.
William Payre, Saif Alatrash, Costas Karageorghis
Open Access
Article
Conference Proceedings
Does the testing environment matter? Carsickness across on-road, test-track, and driving simulator conditions
Carsickness has gained significant attention with the rise of automated vehicles, prompting extensive research across on-road, test-track, and driving simulator environments to understand its occurrence and develop mitigation strategies. However, the lack of carsickness standardization complicates comparisons across studies and environments. Previous works demonstrate measurement validity between two setups at most (e.g., on-road vs. driving simulator), leaving gaps in multi-environment comparisons. This study investigates the recreation of an on-road motion sickness exposure - previously replicated on a test track - using a motion-based driving simulator. Twenty-eight participants performed an eyes-off-road non-driving task while reporting motion sickness using the Misery Scale during the experiment and the Motion Sickness Assessment Questionnaire afterward. Psychological factors known to influence motion sickness were also assessed. The results present subjective and objective measurements for motion sickness across the considered environments. In this paper, acceleration measurements, objective metrics and subjective motion sickness ratings across environments are compared, highlighting key differences in sickness occurrence for simulator-based research validity. Significantly lower motion sickness scores are reported in the simulator compared to on-road and test-track conditions, due to its limited working envelope to reproduce low-frequency (<0.5 Hz) motions, which are the most provocative for motion sickness.
Georgios Papaioannou, Barys Shyrokau
Open Access
Article
Conference Proceedings
Perceived Thermal Comfort in Vehicles with Glass Canopies – A User Study in High Temperature Summer Conditions
Modern vehicles are increasingly designed with large glass surfaces, particularly panoramic canopies, raising questions about thermal comfort and user experience. This study, conducted near Barcelona during summer conditions, examined user perception of three sunroof technologies: (1) Standard canopy with a mechanical shutter in the closed position, (2) coated glass with infrared-reflective and low-emissive properties, and (3) coated glass with similar properties and switchable between clear and opaque states. The study revealed that, based on EN ISO 14505-3, thermal comfort was given across all roof configurations and the advanced thermal protection technologies (coatings, switchable) showed no statistically significant differences in perceived thermal comfort compared to a mechanical shutter. Additionally, participants preferred the switchable roof option, citing aesthetics and a cooler sensation at head level compared to the shutter solution. They also showed a higher willingness to pay for the switchable roof technology. These findings highlight the importance of integrating both physical and psychological factors in sun protection design for canopies and suggest that switchable technologies offer superior user experience. Future research should explore combinations with other coated glass surfaces such as windshields and investigate how subjective thermal perception and perceived comfort ratings vary across different regions.
Jan Philip Westerkamp, Lynn Katharina Lintzen-Hage, Sarah Irmer, Valentin Schulz, Nicolas Daniel Herzberger
Open Access
Article
Conference Proceedings
A novel EEG-driven multidimensional modelling of the Driver Performance Envelope for a user-centred human-vehicle interaction
The concept of Driver Performance Envelope (DPE), conceptualized from the theory of Human Performance Envelope developed in aviation, defines the boundary within which the driver can perform effectively. This study proposes a novel multidimensional formulation of the DPE based on EEG-derived neurometrics, aimed at providing a time-resolved estimation of the driver’s readiness to safely operate the vehicle. Fifteen participants completed five simulated manual driving scenarios designed to elicit different levels of difficulty, stress and thus overall performance, by manipulating context (highway vs. urban), traffic density, weather conditions (sun, rain, fog), time pressure, and the presence of a secondary task. EEG was continuously recorded to extract individual neurometrics of workload, vigilance, stress, and fatigue. These dimensions were integrated into a DPE index through a novel processing pipeline combining Principal Component Analysis and Mutual Information. Questionnaires were administered to validate the experimental design. Results showed that the proposed EEG-driven DPE successfully discriminated driving conditions characterised by different hypothesised performance envelopes. The DPE index significantly differentiated (p < .05) scenarios associated with high, intermediate, and—at least one comparison—low expected performance capacity. The robustness of the metric was supported by its comparison with an EEG baseline derived from a resting-state task, confirming its reliability and coherence across conditions. Beyond its methodological innovation, the proposed framework supports the development user-centred human–vehicle interaction strategies. Overall, this work demonstrates the feasibility and value of an EEG-driven multidimensional DPE as a key enabler for next-generation, human-centred mobility, contributing to safer and socially sustainable driving systems.
Andrea Giorgi, Vincenzo Ronca, Francesca Dello Iacono, Marianna Cecchetti, Rossella Capotorto, Stefano Menicocci, Dario Rossi, Pietro Aricò, Gianluca Borghini, Federica Biassoni, Manuela Bina, Fabio Babiloni, Gianluca Di Flumeri
Open Access
Article
Conference Proceedings
Learning from Drivers: A Case-Based Reasoning Framework for Takeover Control in Conditionally Automated Vehicles
The rapid evolution of intelligent transportation systems has positioned conditionally automated vehicles (CAVs, SAE Level 3) at the forefront of automotive innovation. These vehicles represent a critical transition between human-driven and fully autonomous systems, in which safety and reliability depend on effective management of takeover control (TOC) events. This paper introduces a Case-Based Reasoning (CBR) framework to model, evaluate, and improve decision-making during control transitions using empirical and contextual data from both human and vehicular agents. The framework follows the CBR cognitive cycle of retrieval, reuse, revision, and retention to compare new TOC scenarios with previously observed cases. Each case integrates multimodal information, including driver personal traits, non-driving-related tasks, traffic density, and takeover urgency, as well as temporal and spatial performance metrics such as takeover time and steering behaviour. The time budget to system limitation is used as the determining outcome variable. By capturing and reusing experiential knowledge, the proposed framework enables adaptive and interpretable decision-making for Level 3 automation. It supports bidirectional learning between drivers and automated systems and provides a foundation for future Levels 4 and 5 vehicles to incorporate human-like reasoning in safety-critical decisions.
Ali Mostafavi, Wenge Xu, Oliver Carsten, Foroogh Hajiseyedjavadi
Open Access
Article
Conference Proceedings
Driver Takeover Performance under Different Driving Contexts and Non-Driving Tasks in Level 3 Automated Driving
In Level 3 (L3) automated driving, drivers may disengage from continuous control but must resume control when a takeover request (TOR) occurs. Drivers’ takeover readiness may be reduced by engagement in non-driving-related tasks (NDRTs), especially in more complex urban environments. However, limited evidence is available on how driving context and NDRT demand jointly affect takeover performance in L3 automation. To address this gap, we conducted a driving-simulator study with 18 licensed drivers using a 2 × 3 within-subject design that combined two driving contexts (highway and urban) and three NDRTs (music listening, entertainment video viewing, and news summarization). Takeover performance was evaluated using takeover reaction time (TRT), eye-tracking measures, heart rate variability (HRV), and subjective ratings of workload and situational awareness. Both driving context and NDRT demand affected takeover performance. Urban scenarios were associated with longer TRT, higher workload, and more dispersed visual attention than highway scenarios. Increasing NDRT demand was associated with poorer takeover performance, and news summarization produced the longest delays, greater off-road attention, slower gaze return after TOR, and reduced situational awareness. HRV measures further indicated higher physiological stress under high-demand NDRTs, particularly in urban conditions. These findings highlight the need for context-aware takeover support that accounts for both environmental complexity and NDRT demand.
Meiqi Liu, Zhixian Zhu, Jie Chen, Yifan Zhang, Jiaqian Zhong, Yu Zhang
Open Access
Article
Conference Proceedings
Physiological Assessment of Driver Trust in Automated Vehicles under Distinct Driving Styles
With the rapid advancement of automated vehicle (AV) technology, drivers’ willingness to rely on AV systems has become a decisive factor in their broader deployment. Among the many psychological determinants, drivers’ trust has been consistently identified as a key condition influencing the acceptance and appropriate use of AVs. Consequently, understanding how trust evolves during real-time interaction with an AV is increasingly critical for determining whether drivers can engage with the system safely and effectively. Current research on trust assessment often relies on subjective measures, yielding limited temporal resolution and vulnerability to subjective bias. This study proposes a multimodal, event-driven experimental framework to investigate how different AV driving styles affect driver trust through objective physiological signals. First, a driving simulation experiment was designed with three distinct driving styles: a) a baseline scenario featuring smooth, normative driving to establish physiological reference physiological states; b) a hesitant scenario characterised by cautious and delayed decision-making; and c) an aggression condition characterised by late braking and assertive, high-speed cornering. Each participant experienced all conditions in counterbalanced order. Second, participants’ physiological responses were recorded using a synchronised multi-sensor setup, including electroencephalography (EEG) and eye-tracking. Subjective trust ratings were collected after each scenario to serve as the ground truth for trust evaluation. Finally, the collected signals were integrated to analyse the correlation between trust level and physiological features. The results suggest that aggressive and hesitant driving styles elicit distinguishable subjective, neural, and attentional responses, indicating different pathways of distrust. These findings provide preliminary evidence supporting the feasibility of physiology-based approaches for assessing driver trust in automated driving.
Yizheng Li, Zhilin Hu, Karl Proctor, Andrew Owens, Lisa Dorn, Yifan Zhao
Open Access
Article
Conference Proceedings
User Scenario-Based Interaction Design for Level 4 Autonomous Robo-Ride Systems
As interest in driverless mobility continues to grow, the importance of human-machine interface (HMI) design for Level 4 autonomous vehicles is becoming increasingly critical. This study investigates user interactions and expectations in autonomous robo-ride environments through a scenario-based approach. A user study was conducted to explore key factors influencing user experience, including safety, privacy, accessibility, and trust. Based on these findings, we designed an HMI concept tailored for shared, driverless mobility services, focusing on intuitive interaction and inclusive design. The proposed system incorporates features such as clear navigation guidance, interaction cues, and a face-to-face seating configuration to enhance communication and usability, particularly for users with mobility challenges. To evaluate the effectiveness of the proposed HMI, a prototype-based assessment was performed. The results indicate that the design improves perceived safety, usability, and user trust, while reducing uncertainty in autonomous ride situations. In addition, accessibility considerations contributed to a more inclusive user experience across diverse user groups. These findings highlight the importance of human-centered HMI design in autonomous mobility systems and provide design implications for future robo-ride services.
Jeeyoon Han
Open Access
Article
Conference Proceedings
Task-Based AR-HUD in Autonomous Driving: Enhancing Driver Agency, Engagement, Attention, and Takeover Performance
In SAE Level 3 autonomous driving, prolonged passive monitoring presents the severe challenge of drivers falling 'out-of-the-loop'. To address this, we propose a 'task-based AR-HUD', an interface that transforms vehicle motion planning into actionable task opportunities requiring driver approval, thereby maintaining the driver's cognitive flow within the driving loop. This research investigates the psychological and cognitive impacts of such task-based interactions to enhance driver takeover performance. Utilising a high-fidelity driving simulator, we compared three progressive AR-HUD visual feedback modalities: linear, dynamic, and task-based. Subjective metrics concerning compliance, perceived control, comfort, and emotional experience were analysed. Results indicate that the task-based AR-HUD significantly enhances driver agency and engagement, effectively redirects attention to the road, and sustains the driver's 'in-the-loop' state. This study offers theoretical foundations and empirical evidence for future high-level autonomous driving human-machine collaborative designs that balance psychological needs with functional safety.
Bingxin Sun, Danhua Zhao
Open Access
Article
Conference Proceedings
Balancing Time-Energy Trade-Offs in Long-Distance Electric Vehicle Driving: The Role of Subjective Appraisals in Multi-Goal Regulation
Multi-goal balancing is a core demand in long-distance electric vehicle (EV) driving, where time and energy objectives are coordinated in a dynamic environment. The present study is grounded in control-loop models of self-regulation, conceptualizing that drivers iteratively compare perceived states to reference values and adjust speed or charging strategies accordingly. Within this regulatory process, effective goal balancing depends on how drivers interpret task-relevant information and translate these appraisals into action. Two determinants are particularly relevant: informational support and subjective competence. Informational support is understood as system- or environment-provided cues that are timely, interpretable, and task-aligned within action-regulation processes. Subjective competence refers to drivers’ self-appraisal of their capability to meet task demands, grounded in task knowledge and usable strategies. An online survey of EV drivers (N = 57) assessed Perceived Support of Action Regulation (PSAR), Subjective Range Competence (SRC), and Subjective Goal-Balancing Competence (SGBC). Results showed that both PSAR and SRC were positively associated with SGBC, indicating that perceived informational support and range-related subjective competence systematically co-occur with higher subjective competence to coordinate time and energy goals under constraints. The findings highlight the relevance of driver-centered feedback as regulatory support for managing time-energy trade-offs.
Beate Stattkus-fortange, Vivien Moll, Thomas Franke
Open Access
Article
Conference Proceedings
Humanization or intellectualization? How autonomous vehicles’ mental capacities impact human’s driving behaviors
Compared to human-driver vehicles (HVs), people are less tolerant for autonomous vehicles (AVs) with abnormal behaviors and more inclined to conduct dangerous driving behaviors. Previous studies have shown that the anthropomorphic design of AVs influences drivers’ attitudes and behaviors, but no studies have explored the underlying mechanism. Moral sense may play an important role in human’s attitude toward different anthropomorphic AVs. By attributing different mental capacities to AVs, this study investigated the mediation effect of moral disengagement and subsequent behavior when facing different anthropomorphic AVs. An intellectualized AV with agency capacities and a humanized AV with experience capacities were set as a two level of mental capacities, an within-subject variable. 347 drivers imagined driving on the road alongside different AVs exhibiting abnormal driving behaviors and validly responded to the scales measuring moral disengagement, annoyance, anger, and following behaviors. The results showed that when facing AV with experience capacities, drivers reported less moral disengagement, negative emotions, and negative driving behaviors, compared to AV with agency capacities. Moreover, moral disengagement mediated the relationship between AVs’ mental capacities and all driving behaviors; while the mediating effects of negative emotions were significant in part of the models. Overall, this study revealed that compared with intellectualized AV, human driver showed more moral sense and safe behaviors when interacting with humanized AV, providing new insights for AV designers and manufacturers.
Jiayan Yu, Weina Qu, Yan Ge
Open Access
Article
Conference Proceedings
Exploring Passenger Experience on High-Speed Rail for Digital Engagement: A Case Study of Turkish High Speed Train Passengers
As rail travel expands, understanding how passengers experience these environments, particularly those who rely on continuous digital engagement, becomes increasingly important for user-centered design research. Digitally engaged passengers are those who actively use their digital devices for work, communication, or personal tasks during travel. These behavioral shifts highlight the need to examine how digital engagement intersects with usability, comfort, and productivity in long-distance travel settings. This study aims to investigate the train travel interior for digitally engaged passengers, specifically in Turkey, focusing on how the physical and service-related aspects of long-distance Turkish train journeys affect their user experience, digital engagement, multitasking behaviors, and comfort during travel. To uncover these gaps, the research adopted an online survey answered by 181 people who have traveled on Turkish high-speed trains. The survey questions were designed to evaluate the passenger experiences of people using Turkish high-speed trains, particularly regarding digital device use, productivity, multitasking habits, and perception of the physical environment. These constraints were synthesized into five core pain point categories that consolidate the most prominent environmental barriers affecting digital productivity and multitasking during travel, highlighting the design elements passengers perceive as essential for a comfortable and usable digital workspace. These insights support the conceptualization of digitally engaged travel behaviors and contribute to defining a framework that links environmental factors to digital engagement levels.
Gülara Mat, Ekrem Cem Alppay
Open Access
Article
Conference Proceedings
Development of Human Factors toolkit to inform behavioural and cognitive research in the railway domain
The application of automated systems in rail operations should account for its impact on train (traffic) operators. The introduction of automation is expected to transform the duties and responsibilities of operators, or more generally, the job description itself. The role of behaviour and cognition research is essential in supporting the shift toward more advanced interactive technologies, helping to maximize their benefits while mitigating potential human factors issues in its adoption. We synthesised information from academic literature on human factors research in railways and developed a toolkit that identifies a set of human factors constructs that were commonly measured by practitioners in the railway domain, particularly in human-in-the-loop (HITL) simulations. We then conducted 3 rounds of feedback consisting of a workshop and 2 survey rounds with a total of 23 responses from human factors experts We identified 8 main constructs that are commonly measured in behavioural rail simulation studies: task performance, workload, communication, situation awareness, attention (including vigilance and attention allocation), user experience and usability, fatigue/sleepiness, and trust in automation. Additionally, we also compiled 63 objective and subjective methods for measuring the constructs, We provide descriptions and examples of how each method was utilised in research in the toolkit.
Sarah Kusumastuti, Tom Kolkman, Julia Lo, Simone Borsci
Open Access
Article
Conference Proceedings
Design and Evaluation Methods for Non-Technical Skills Training for Shinkansen Train Crew
In recent years, several railway accidents and incidents have continued to occur due to human error, indicating that further safety improvements are required even in highly developed railway systems such as the Shinkansen. Analyses of recent incidents at East Japan Railway Company (JR East) have revealed that, in addition to technical skills (TS), non-technical skills (NTS)—particularly situation awareness—play a critical role in accident prevention. However, current training programs mainly focus on procedural compliance and do not sufficiently address individual differences in NTS, nor do they provide objective and consistent evaluation criteria for such skills. This study proposes a methodology for designing NTS-oriented training scenarios tailored to the individual characteristics of Shinkansen crew members and for developing quantitative evaluation indicators for NTS. First, crew members’ individual characteristics were classified into three error tendency types—lapse, automatic action errors, and decision errors—based on previous incident analyses and a self-assessment questionnaire. Next, the relationships between these individual characteristics and a JR East–specific NTS framework were systematically organized, and NTS elements requiring reinforcement were identified for each type. Based on this framework, NTS components were embedded into existing simulator-based training scenarios, using a rolling stock failure response by a train driver as an illustrative example. Additional scenario elements were designed to elicit observable NTS behaviors, such as situation awareness, decision making, and communication with dispatchers and conductors. Furthermore, behaviorally anchored, three-level quantitative evaluation indicators (“Adequately Demonstrated,” “Partially Demonstrated,” and “Not Demonstrated”) were developed to reduce subjectivity and inter-instructor variability in NTS assessment. The proposed approach enables the integrated training and evaluation of TS and NTS while accounting for individual differences among crew members. This framework is expected to enhance abnormal situation handling capabilities of Shinkansen crew and to contribute to further improvements in railway safety through more systematic and objective NTS training.
Eiko Takayasu, Hiroaki Fujishiro, Takeshi Chiba
Open Access
Article
Conference Proceedings
With Pixels & Timber: Application of Mixed Reality for Human-Centered Train Design & Engineering
Modern metro train design projects must balance evolving customer requirements, diverse stakeholder expectations, and the need for early, reliable engineering decisions. Conventional validation methods—document-based analyses, static CAD reviews, and late-stage physical mock‑ups—often reveal design issues too late, resulting in costly redesigns, schedule risks, and inefficient stakeholder involvement. To overcome these challenges, the Siemens Mobility project team developed an award‑winning Mixed Reality (MR) Human Factors (HF) and Customer Experience (CX) testing and validation approach—deployed in the ongoing Sydney Metro – Western Sydney Airport (SM-WSA) project—that merges physical prototyping with immersive Virtual Reality (VR) environments. The approach employs cost‑effective wooden or 3D‑printed structures augmented with a high-end Virtual Reality (VR) visualisation, enabling MR-based interactive evaluation of the train interior's user facing elements. This setup allowed rapid exploration of dozens of design variants and enabled evidence‑based design engineering decision making significantly earlier in the project. The method proved particularly effective for pre-validating a few design areas, such as stopping‑marker positioning, inclusivity & accessibility, and design options such as seat fabrics or headrests.The MR‑driven review workflow, combined with user performance- and behaviour-based system tests, demonstrated measurable benefits. First, evidence‑based Human Factors engineering became possible through precise spatial assessments using integrated haptic and visual feedback, enabling more confident design assurance and acceptance. Second, co‑creation and collaboration improved through intuitive, immersive engagements with train operators, accessibility committees, and end users, aligning with ISO 9241‑110 principles for human‑centred design. These sessions enhanced understanding, consensus-building, safety and engineering assurance and overall CX validation. Third, risk reduction and early design freeze were achieved by resolving issues before the high‑fidelity mock‑up stage, reducing Non‑Compliance Costs (NCCs), and preventing downstream costs from schedule disruptions.
Dalibor Andrijevic, Tara Kazi, Christopher Thrope
Open Access
Article
Conference Proceedings
Operational Transitions to Automation: A Scoping review with implications for future rail service
The increasing integration of digitalization and automation in transport domains requires an understanding of barriers and facilitators affecting operational transitions. This systematic literature review, following PRISMA guidelines, identifies factors influencing automation-related operational changes in multiple transport domains attempting to utilize the insights to serve the scope of the rail sector. Three databases (Scopus, Web of Science, IEEE) were searched for publications from 2000–2025, yielding 14 eligible articles after screening. Extracted data on facilitators, barriers, and lessons learned were thematically clustered into three categories: i) Organizational and human factors aspects, ii) process and risk mitigation strategies, and iii) infrastructure and system integration. Results indicate that organizational and human factors aspects dominate both barriers and facilitators. The most frequently reported barrier was system complexity and uncertainty (50%), followed by poor system adoption (29%) and stakeholder misalignment (21%). Stakeholder involvement emerged as the primary facilitator (43%), alongside utilization of centralized support tools (36%). Key lessons learned include the importance of operator-stakeholder alignment, active middle management engagement, and maintaining trust in automation (each 21%). Infrastructure-related factors were minimally reported, suggesting practitioners may treat technical integration as a design prerequisite rather than an operational concern. These findings highlight that successful operational transitions in public transport depend primarily on addressing human and organizational dimensions rather than technological capabilities alone. We adopted a multidomain perspective assuming transferability across transport domains to serve future railway research. Future research should incorporate grey literature and longitudinal studies to strengthen guidance for rail operators.
Tom Kolkman, Sarah Kusumastuti, Simone Borsci, Julia Lo
Open Access
Article
Conference Proceedings
A Human Factors Framework for Evaluating Digital Train Commands in Railway Operations
The digitalisation of operational train commands represents a major step in the transformation of railway operations and fundamentally changes safety-critical communication between dispatchers and train drivers. Traditionally, commands are issued through written or verbal procedures based on strictly standardised rules, ensuring clarity and reliability, particularly in disruption scenarios. With the introduction of digital train commands, these procedures are increasingly replaced by interface-based transmission and acknowledgement mechanisms, raising new questions regarding usability, comprehensibility, and human–system interaction.This paper presents a human factors–oriented methodological framework for the systematic evaluation of digital train commands in railway operations. The focus lies on research design and evaluation methods suitable for early deployment and transition phases, rather than on operational performance outcomes. The proposed approach combines simulator-based studies, task and process analyses, semi-structured interviews, questionnaires, mockups, and thinking-aloud techniques to investigate cognitive workload, acceptance, and safety-critical communication under realistic operating conditions. Particular attention is given to transitional environments in which traditional and digital command procedures coexist, potentially increasing cognitive demands and the risk of human error.The framework examines how factors such as stress, time pressure, prior experience, and interaction design influence user behaviour and trust in digital acknowledgement mechanisms. By systematically addressing both technical and human aspects, the approach supports early identification of usability issues and interaction risks before large-scale implementation. The paper contributes a transferable methodological basis for evaluating digital command systems in the railway domain and other safety-critical transportation contexts, highlighting the need to integrate human factors alongside technological innovation to maintain established safety standards.
Bekir Arslan, Birgit Milius
Open Access
Article
Conference Proceedings
Tracing Human Movement Through Mobile Signaling Big Data: New Possibilities for Mobility Analysis
Mobile signaling data provides extensive population coverage and high temporal resolution for large-scale mobility analysis, but privacy regulations have restricted access to individual trajectories, resulting in anonymized grid-based datasets. Although such data lack continuous movement paths and contain aggregation noise, they still capture meaningful collective mobility patterns. This study applies DBSCAN to identify dense spatial clusters and activity cores, followed by Random Forest analysis to assess the influence of spatial and contextual factors. Results show that blurred grid-based signaling data reveals stable spatial aggregations, temporal rhythms, and shifts in movement intensity. Rather than replacing trajectory-based data, this approach complements conventional mobility datasets by offering population-level insights into large-scale movement dynamics and regional mobility structures.
Jung Yeh, Xing Wei Liu, Shinichi Muto, Chia Yin Kuo, Su Pei Ling, Hsiang Chuan Chang
Open Access
Article
Conference Proceedings
Effect of Pedestrian Signal Display Methods on Perceived Waiting Time
This study investigates the influence of pedestrian signal display methods and traffic environments on perceived waiting time. While remaining time displays have become common to mitigate pedestrian impatience, the detailed mechanisms by which different display methods interact with environmental factors to modulate perceived waiting time remain insufficiently understood. Using immersive virtual environment technology, we conducted controlled experiments to quantitatively clarify these effects. Participants performed a prospective time reproduction task where they encoded the red signal duration in a virtual intersection and then reproduced it. Two metrics were calculated from the reproduced durations: length (ratio of reproduced duration to actual duration) and variability (coefficient of variation). The study consisted of two main experiments: Experiment 1 analyzed the effect of four Display Methods (Standard Signal, Signal with Progress Bar, Signal with Numerical Countdown, and Signal combining both Elements) and Traffic Presence (with-Traffic vs. without-Traffic), while Experiment 2 examined the interaction between numerical countdowns and three levels of Traffic Volume (High, Medium, Low). Results indicated that the Signal with Numerical Countdown condition significantly decreased length and variability compared to other conditions by acting as an accurate external clock. In Experiment 2, a significant interaction was found where the combination of the Signal with Numerical Countdown and Medium Traffic volume maximized the shortening of perceived waiting time. This suggests a synergistic effect between the reduction of uncertainty via numerical information and attentional distraction caused by traffic. These findings provide quantitative support for treating signal display methods as effective design parameters, recommending the implementation of numerical countdowns to reduce pedestrian stress and enhance safety.
Taichi Morioka, Yohsuke Yoshioka
Open Access
Article
Conference Proceedings
Impact of Digital Flat-rate Mobility Passes on User Exploratory Behavior: A Case Study of Strategic Multimodal Integration in Urban Residential Districts
Urban commuters are largely governed by behavioral inertia — the tendency to adhere to routine transit paths in order to minimize cognitive load and incremental cost. This rigidity systematically bypasses the peripheral "inter-hub" districts that exist between major rail nodes, suppressing local economic activity despite their cultural and commercial potential. This study investigates how a digital flat-rate mobility pass, deployed through a strategic consortium of three competing private railway operators and multiple micro-mobility providers in a high-density Tokyo residential district, disrupts this inertia. Drawing on Cognitive Load Theory (Sweller, 1988) and choice architecture principles (Thaler & Sunstein, 2008), we analyze behavioral data from 559 pass holders over a three-week experiment. Results demonstrate that eliminating marginal per-ride costs induced a measurable shift from efficiency-driven commuting to discovery-oriented exploration: 38.4% of users utilized three or more transport modes per journey, average local spending increased by 1,757 JPY per person, and 62.5% of respondents visited commercial destinations they had never previously accessed. These findings establish multimodal digital integration as a scalable instrument for regional revitalization and human-centered Smart City governance.
So Nishina
Open Access
Article
Conference Proceedings
Understanding the Formation Mechanisms of Fatigued Driving among Heavy-Duty Truck Drivers: A Mixed-Methods Study from a Human Factors Perspective
Heavy truck drivers operate under sustained high workload and tight delivery deadlines, making fatigue-related driving a persistent road-safety risk. Prior studies have emphasized crash outcomes or fatigue-detection technologies, yet provide limited explanation of why drivers continue to drive while fatigued and how multiple pressures jointly shape such behavior. This study models the mechanism of fatigued driving among heavy truck drivers and derives implications for human-centered intelligent cockpit interventions using a two-stage mixed-methods design. First, 30 semi-structured interviews were analyzed thematically to identify key determinants and construct a qualitative model. Second, a survey of 110 Chinese heavy truck drivers was conducted to test the model using structural equation modeling, with variables including time pressure, economic pressure, inadequate in-cab facilities, trip demands, self-perception bias, and fatigued driving behavior. Results show that time pressure is the strongest positive predictor of fatigued driving, substantially increasing the likelihood of continuing to operate a vehicle while fatigued. Self-perception bias also positively predicts fatigued driving, indicating that underestimating fatigue risk and overestimating one’s capability are important psychological drivers. Transport distance exhibits a negative association, suggesting potential self-regulation or experiential adaptation on longer trips. No significant moderating effects were observed, but the overall model supports a multi-factor pathway shaped by task stressors and subjective cognition. Based on these findings, we propose two cockpit directions for fatigue management: mitigating cognitive bias through driver-state monitoring with timely feedback, and alleviating time-pressure structures via schedule support and information assistance. This work provides empirical evidence on behavioral mechanisms underlying fatigued driving and informs intelligent cockpit design and broader transport safety interventions.
Yu Zhang, Zhixian Zhu, Yufei Wang, Haoyang Zheng
Open Access
Article
Conference Proceedings
Visual Perception of Roadside Advertisements by Diverse Urban Mobility System
In the cities of today, individuals have to constantly visually perceive their surroundings in order to move, be safe, and know where they are going. Advertisements have to grab people's attention even though there are plenty of other things to look at. This research investigates the responses of people to the billboard advertisements while they are on the move with an emphasis on what appeals both to their eyes and their minds. The paper examines the influence of different modes of movement, walking, driving or taking a bus, on people's visual perception and recall of various roadside adverts. This study wants to help advertisers, planners, and designers know how speed and where an ad is making a difference in real life. It also helps us know more about how people see things while moving.The study used several methods for large-scale experiments. People went through virtual reality simulations that copied a real city road. In the study, people moved in different controlled ways. Mobility mode and spatial location have a substantial impact on the attention and processing of visual information, with the study concluding that recall effectiveness can be improved by matching movement patterns and ad placement, while proposing further confirmation through real-world urban environment testing in the future.
Saptarshi Kolay, Udit Shivansh, Aman Raj, Sukalp Dabral
Open Access
Article
Conference Proceedings
Verification of effectiveness of Road Vibration Trough for ensuring safety of visually impaired individuals when crossing LRT tracks
Light Rail Transit (LRT) is a modern urban transportation system characterized by low noise and vibration. Unlike conventional railways, LRTs operate alongside regular traffic without crossing gates. Although the Ministry of Land, Infrastructure, Transport, and Tourism of Japan has provided guidelines for general railway crossings, standardized universal design measures for crossing LRT tracks on shared roadways have not yet been established. Consequently, each operator implemented an independent measure.Visually impaired individuals rely on acoustic signals to cross roads. However, these signals are often turned off early in the morning and at night because of noise concerns, forcing users to rely on ambient sounds and increasing the risk of crossing red lights. Advanced Pedestrian Information and Communication Systems (PICS), which utilizes mobile devices and Bluetooth, is one potential solution.Meanwhile, Road Vibration Troughs (RVTs) have been installed as tactile cues. Embedded in the pavement, it communicates signal information through vibrations, voice announcements, and LED lights. By adapting Advanced PICS technology to the pavement, assistance can be provided regardless of the user's device ownership. This tool also benefits deafblind individuals.This study aimed to identify issues surrounding LRT crossing areas and to verify the effectiveness of the RVTs. We conducted an experiment involving 20 visually impaired individuals in Japan. We established four crossing conditions by combining the presence or absence of acoustic signals and the RVTs. Each participant performed 24 crossings, which were recorded using a video camera. The usefulness of the RVT and appropriateness of the Tactile Walking Surface Indicators were demonstrated via interviews of participants.
Akinari Tahara, Tomoyuki Inagaki, Taiki Koseki, Yuuki Hashiba, Murao Mayuko, Taeko Tanida, Teppei Osada
Open Access
Article
Conference Proceedings
A Human Factors Engineering Approach to Feature Extraction and Safety Intervention in Intentional Vehicle-Pedestrian Collisions
Intentional vehicle-pedestrian collisions exhibit fundamental distinctions from conventional traffic accidents, potentially constituting serious criminal offenses. However, judicial practice faces significant challenges in investigation, evidence collection, and case characterization. Grounded in human factors engineering theory, this study integrates multi-source data from 26 authentic judicial cases—including EDR data, surveillance footage, and on-site investigation reports—to construct a heterogeneous dataset encompassing vehicle operational status, driver behavioral characteristics, and environmental contextual information. Through high-fidelity accident reconstruction and vehicle condition restoration via the PC-Crash simulation platform, coupled with spatiotemporal sequence analysis employing dynamic time warping algorithms, this research systematically elucidates the dynamic correlation mechanisms between driver operations and pedestrian trajectories. A feature-weighted risk classification model was developed, with weight allocation meticulously considering statistical analysis of accident cases and human factors principles, thereby highlighting the relative importance and interactive relationships among vehicle status, driver behavior, and environmental context. By establishing a three-level safety intervention strategy, a paradigm shift from passive forensics to proactive prevention has been achieved. Experimental validation demonstrates that the proposed method attains 83.5% recognition accuracy on test sets while maintaining a false alarm rate below 6.8%, providing scientific evidence for characterizing intentional collision cases and establishing a theoretical foundation for intelligent connected vehicles' active safety design. The primary innovation lies in integrating human factors engineering theory with judicial practice requirements, constructing a comprehensive technical framework from feature extraction to safety intervention, thereby advancing traffic safety management from post-hoc analysis to preemptive prevention.
Wei Ji, Quan Yuan, Gang Cheng
Open Access
Article
Conference Proceedings
Integrating Human-Centred Design Approach into the Safety Assurance of CCAM Systems: A Framework for STAC
The development of Connected, Cooperative, and Automated Mobility (CCAM) systems has focused on technical safety, but safety alone does not ensure adoption. Users need systems that align with their needs, preferences, and expectations. The European project CERTAIN addresses this by integrating Safety, Trust, Acceptance, and Comfort (STAC) into a user-centred design (UCD) framework across the CCAM lifecycle. This work applies UCD to Human–Machine Interfaces (HMI), defining STAC-related KPIs, scenarios, and use cases across automation levels (L2–L4), including mixed-level configurations and L4 delivery pods.A systematic literature review and stakeholder interviews refine STAC KPIs capturing human perceptions, behaviours, and needs. The UCD process operationalizes STAC dimensions iteratively in design and evaluation, emphasizing inclusivity, accessibility, and diverse cultural contexts. Three HMI use cases—complete journeys, unscheduled handovers, and software updates—test STAC KPIs in context, combining subjective (perceived safety, trust, comfort, acceptance) and objective (behavioural adaptation, trust calibration) measures.Grounded in human factors, this framework guides the design of HMIs that are not only technically safe but also perceived as safe, trustworthy, acceptable, and comfortable, supporting adoption and societal alignment of CCAM systems.
Turkan Hentati, Oana Moldovan
Open Access
Article
Conference Proceedings
A Taxonomy of Digital Twin Applications for Road Infrastructure Safety: A Human-Factors Perspective
Road transport systems are vital socio-technical infrastructures but still account for a large share of fatal and serious injuries worldwide. While vehicle technology and traffic management have improved safety on major roads, urban and secondary roads remain challenging owing to varied infrastructure conditions, mixed traffic, and reliance on human judgment. Improving safety in these areas requires approaches that focus on the interactions between humans, systems, and infrastructure. Digital twins (DTs), recognised as dynamic digital models of physical assets, processes, and conditions, are increasingly used for predictive modelling, simulation, and human-centred decision support in transportation. By integrating diverse data, representing evolving risks, and simulating behavioural responses, DTs offer new pathways to reduce human error, aid decision-making, and improve road asset and safety management. However, the variety of DT architectures, functions, and data capabilities creates challenges for their systematic adoption, evaluation, and human-centred design. This study introduces a taxonomy of digital twin applications for urban and rural road asset and safety management from a human factors perspective, developed within the CAMBER project. The taxonomy organizes DT systems according to four dimensions: functional scope, application scale, architectural layers, and integration level. These dimensions describe how DTs support asset and safety management, assess how road conditions affect advanced driver-assistance systems, scale from individual components to system and process levels, and mediate interactions among human decision-makers, infrastructure, and digital services. Developed through a qualitative, problem-driven design, the taxonomy is based on a structured review of recent literature, analysis of CAMBER pilot requirements and evaluation framework. Through examples and discussion of adoption challenges, this study demonstrates how the taxonomy can guide human-centred design, comparison, and scaling of DT-enabled safety applications on urban and secondary roads.
Katia Pagle, Anna Antonakopoulou, Angelos Amditis
Open Access
Article
Conference Proceedings
Impact of AR Head-Up Displays on Driver Performance and Safety Across Age Groups
In-Vehicle Information Systems (IVIS) can enhance driving performance, yet poorly designed interfaces may increase distraction and crash risk. This study investigated how different display modalities influence driving performance and user experience across age groups. A simulator-based experiment was conducted with younger and older drivers using head-down displays (HDD), head-up displays (HUD), and augmented reality head-up displays (AR-HUD), with and without auditory support. Results showed that AR-HUD improved overall driving stability, reduced collision risk, and enhanced navigation performance compared to conventional displays. The integration of auditory cues further improved performance, eliminating navigation errors. Notably, although older drivers exhibited lower baseline performance, they benefited substantially from AR-HUD in terms of vehicle control and lane-keeping stability. These findings highlight the effectiveness of spatially integrated and multimodal interface design in improving driving safety, particularly for older drivers.
Jin-yu Song, Yung-ching Liu
Open Access
Article
Conference Proceedings
Electromagnetic Compatibility Analysis Of Automotive Vehicles
Automotive safety systems can be broadly divided into two categories: active and passive. Active safety systems are designed for prevention – they prevent accidents by warning the driver of a potentially dangerous situation or by helping them maintain control of the vehicle. Passive safety systems, on the other hand, aim to limit injuries resulting from an accident should one occur. The first group includes, among others: The following systems are available: ABS (Anti-lock Braking System), which prevents the wheels from locking during braking; ACC (Adaptive Cruise Control) - cruise control with automatic speed adjustment depending on the road conditions and maintaining a safe distance from vehicles in front; ESC (Electronic Stability Control) - an electronic stability control system; BLIS (Blind Spot Information System), which informs about the presence of other vehicles in the blind spot; LDW (Lane Departure Warning), which warns against lane departure; AEB (Automatic Emergency Braking), which is an emergency braking system; NVS (Night Vision System), which assists the driver when driving at night; RSR (Road Sign Recognition), which is a road sign recognition system; and TPMS (Tyre Pressure Monitoring System), which monitors tire pressure. Passive safety systems, in turn, include systems that control the operation of airbags and seatbelts, protecting against whiplash injuries during impacts, the Child Safety System (CSS), and the Pedestrian Protection System (PPS), which reduce the severity of injuries sustained by children and pedestrians during accidents. This paper presents the results of research on the response of a selected type of airbag activation.
Marian Wnuk
Open Access
Article
Conference Proceedings
Investigating Driver Decision-Making in Pedestrian Crossing Scenarios
Pedestrians are considered one of the most vulnerable groups in the traffic environment. In recent years, pedestrian fatalities have shown an increasing trend, drawing substantial public attention to pedestrian safety. Drivers’ decision-making and behavioral responses when encountering pedestrians are critical determinants of pedestrian safety. Therefore, this study aimed to investigate drivers’ decision-making processes in pedestrian crossing scenarios. A total of 30 licensed adult drivers aged 18 and 40 years were recruited for this study. A driving simulator was used to present pedestrian crossing events under varying traffic density conditions. Drivers’ decision-making process was examined across varying traffic density conditions. In addition, different pedestrian warning lead times were introduced to investigate the effects of time budget on drivers’ decision-making. A significant interaction between traffic density and warning lead time was found to affect longitudinal acceleration variability. Under low traffic density, the shortest lead time (3s) resulted in the greatest variability in longitudinal acceleration. Conversely, in high traffic density, the variability was highest at the intermediate 5-second lead time, suggesting that intermediate time budgets may impair timely hazard perception under high-complexity conditions. Drivers in the medium EI group demonstrated improved lateral stability with a 3-second lead time, while no significant effects were found for low or high EI groups. The findings of this study provide valuable insights into drivers’ decision-making processes in pedestrian crossing situations and may serve as a reference for future research on driver–pedestrian interactions, as well as for the development of advanced driver assistance systems (ADAS). Ultimately, the results may contribute to improving safety for both drivers and pedestrians and provide a foundation for further investigations into driver–pedestrian interaction processes.
Chen-Wei Chang, Wei-Ru Chen
Open Access
Article
Conference Proceedings
Explainable decision support for icebreaker assistance estimation
Safe and efficient navigation in ice-covered waters often depends on timely and accurate estimations of the need for icebreaker assistance. Currently, icebreaker assistance needs are assessed by experienced icebreaker captains based on their own judgment, which can be subjective. Data-driven models have been developed to support this non-trivial estimation, which involves several interconnected factors, including traffic restrictions, ice and weather conditions, and vessel characteristics. The existing study has investigated black box models that achieve great decision accuracy. However, black-box models are limited by poor explainability for end users. This gap reduces end-users’ trust and hinders the adoption of intelligent models in ice navigation. Our previous work (Liu et al., 2025) developed a deep learning-based ensemble model for estimating the need for icebreaker assistance and primarily focused on model predictive performance. This study aims to enable the model’s explainability without compromising the predictive accuracy. Employing SHapley Additive exPlanations (SHAP), we investigate how individual features affect the predicted probability of requiring icebreaker assistance relative to the model’s average prediction at both local and global levels. At the local level, SHAP illustrates how different input features contribute to a single prediction, while at the global level, it summarizes the contributions of these features across all predictions. The explainable results are verified using historical data in the Baltic Sea. The findings indicate that the model can achieve high predictive performance while ensuring explainability through SHAP-based explanations. The outcomes of this paper have the potential to support human-comprehensible explanations, which will help in the evaluation of trust in intelligent decision support systems in the near future.
Cong Liu, Sibghat Asad, Mashrura Musharraf
Open Access
Article
Conference Proceedings
Human machine interaction failure modes in maritime grounding accidents: A decision-oriented data driven analysis
Grounding accidents in the maritime industry remain a critical safety concern despite advances in ship bridge automation. This study addresses the lack of structured, data-driven representations in maritime accident analysis by applying a dual geometric approach to a database of 30 grounding accident cases. Using Principal Component Analysis (PCA) as a dimensionality filter, the study demonstrates that grounding accidents cannot always be deemed random events but can be governed by a strong, low-dimensional structure. Subsequent Multiple Correspondence Analysis (MCA) identify two primary failure mechanisms as observed: a navigation phase characterized by a collapse in supervisory monitoring which is called “Passive Navigation” and a navigation phase driven by communication breakdowns and decision latency called an “Overloaded Navigation”. Hierarchical clustering reveals that while most situations reflect baseline variability, high-risk accidents emerge from extreme configurations of fatigue, authority gradients, and automation dependence. The findings are contextualized by referring to the classic Swiss Cheese Model (SCM) of human error and they provide a methodological foundation for the development of Decision Support Systems (DSS) and Bridge Resource Management (BRM) strategies aimed at detecting early warning signs of systemic degradation during maritime navigation.
Wassim Kaci, Amit Sharma
Open Access
Article
Conference Proceedings
Ship-to-Shore Interconnection for Yacht Passenger Safety
In this article, we present a ship-to-shore interconnection system aimed at supporting real-time monitoring of Yacht crew and passenger health. The system leverages broadband interconnection services provided by Low Earth Orbit (LEO) satellite systems and is composed of two software components. The first component is responsible for the acquisition of parameters describing both the health and safety conditions of passengers (e.g., blood pressure, heart rate, body temperature, movement, and disease-specific parameters for conditions such as diabetes) and the navigation conditions of the vessel (e.g., wind intensity, sea state, speed, response to wave motion, heel, and heading). The second component is a platform for tabular data synchronization. Rather than relying on a custom-designed software application, which would be inherently rigid or only limitedly configurable, the proposed solution exploits the ability of operators to develop application logic using spreadsheet-based tools. The synchronization platform ensures continuous consistency between onboard data and the spreadsheets used by the operators at the shore-based centers.
Massimo Maresca, Francesco Rea, Carlo Andreotti, Luca Gaggero
Open Access
Article
Conference Proceedings
Emergency Response for Uncrewed MASS Passenger Ferries: Coordination Between ROC, Rescue Services, and Passengers in Multi-Hazard Scenarios
The rise of Maritime Autonomous Surface Ships (MASS) introduces critical challenges for emergency preparedness and response. This study examines a multiple-emergency sequence involving man-overboard, onboard fire, passenger evacuation, and loss of ship-shore connectivity on an uncrewed autonomous passenger river ferry. A simulated Remote Operations Center (ROC) operator, simulated passengers, and professional rescue services participated in the scenarios. Field observations and interviews revealed challenges related to coordination, communication, situational awareness, and physical access. The findings highlight the need for clearly defined operational roles and ROC-responder collaboration; improved decision-support tools; robust fallback systems with standardized onboard emergency controls and equipment; and effective bidirectional passenger communication. Overall, these insights inform future regulatory, technical, and procedural developments for emergency-management frameworks in urban autonomous ferry operations.
Nicole Costa, Staffan Bram, Victor Fabricius, Ted Sjöblom, Erik Nilsson
Open Access
Article
Conference Proceedings
Marine accident investigation: is there a common approach to communication as a contributing factor to maritime casualties?
Learning from casualty investigations is a requisite to effect maritime safety and marine environment protection. Moreover, it helps improve training. Communication at sea is a broad concept, as it includes exchanges within the ship's bridge, among crew members, as well as between crew members and pilots, together with ship-to-ship and ship-to-shore conversations. Communication takes place in various contexts, from routine ship operations, such as pilotage, to emergency situations, such as Search and Rescue (SAR). Communication is a human capability that entails both technical and non-technical skills. Technical skills can be evaluated, but non-technical skills are more challenging for appraisal. In addition, ships operate in a highly dynamic environment which makes information retrieval far more difficult than for an operator seated in an office ashore. How do casualty investigators break down miscommunication as a contributor to maritime accidents? What do they look for? Do they evaluate deviations? Is there consistency among investigation bodies in this regard? This study aims to better understand how casualty investigation agencies identify, label and frame failures in communication, and whether there is a common approach to communication across casualty investigation agencies.
Anne Bouyssou Chen
Open Access
Article
Conference Proceedings
Remote Pilotage: The Profession of Maritime Pilot in a Changing Landscape
Maritime pilots are the key actors of a public service established by coastal States to facilitate maritime trade, protect the safety of life at sea, and ensure the integrity of the coastal marine environment. Once transferred onboard the ship, the pilot coordinates with the bridge team as an adviser whose local knowledge, expertise and independent judgement are officially recognized by the national authority. Since the time when merchant ships and pilot boats were propelled by wind and used sails, ships evolved and became more sophisticated, but the mission of the pilot remained the same: to help the bridge team to safely and swiftly move the vessel into and out of the port. Pilots operate in dangerous work contexts all year round, night and day, in all sea and weather conditions, onboard various types of ships. A new concept of ‘remote maritime pilotage’ has appeared and may change the working conditions of a profession whose existence dates to the beginnings of maritime trade. In remote maritime pilotage, the pilot assists the bridge team from the shore. The pilot is no longer transferred onboard the ship and has therefore no more physical contact with the ship’s bridge. While technology development is underway for remote pilotage, this study outlines the changes expected in the way maritime pilots perform their mission and to what extent this new concept may solve some of the challenges faced by the profession.
Anne Bouyssou Chen
Open Access
Article
Conference Proceedings
An Ontology-Based Human Digital Twin Framework for Proactive Shipboard Safety Intelligence
Maritime operations entail complex interactions between human operators, vessel systems, and dynamic environmental conditions, rendering shipboard safety management a formidable and persistent challenge. Despite advances in automation and monitoring technologies, severe onboard accidents—particularly those related to confined space entry, work at height, hazardous environments, and human error—continue to occur, while existing safety systems remain largely reactive. This paper presents an ongoing study on an ontology-based collaborative shipboard safety analysis framework that integrates artificial intelligence, Human Digital Twin (HDT) modelling, and digital twin–based visualization to support proactive and explainable safety intelligence. The framework is designed to acquire high-density onboard data through multi-source wearable, environmental, spatial, and operational sensors, and to formalize maritime safety regulations and human–environment interaction knowledge into an ontology-driven knowledge base for context-aware risk inference.A multi-layered system architecture encompassing data acquisition, edge-based processing, HDT modelling, AI-driven risk analysis, digital twin simulation, and feedback-driven learning is introduced. This study establishes a foundational architectural and methodological framework for next-generation shipboard safety intelligence and provides a basis for future experimental validation and real-world deployment.
Hongtae Kim and Hyunsoo Choi
Open Access
Article
Conference Proceedings