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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Immersive learning in aviation: Integrating virtual, augmented, and mixed reality into cabin crew training</title>
      <link>https://trid.trb.org/View/2686283</link>
      <description><![CDATA[This study examines how immersive technologies, including virtual, augmented, and mixed reality, can enhance cabin crew training and operational readiness in civil aviation. Drawing on established learning theories, the research investigates how immersive environments improve procedural accuracy, situational awareness, crisis management, and teamwork compared with traditional classroom and simulator-based instruction. A phenomenological design based on in-depth semi-structured interviews with 30 active cabin crew members served as the primary methodological approach, supported by a systematic review of prior studies on extended reality applications in aviation. The interviews provided detailed insights into participants’ perspectives on immersive training, revealing how these technologies foster confidence, coordination, and preparedness for high-pressure flight scenarios. The findings show that immersive environments enable realistic and repeatable crisis simulations that strengthen both cognitive and behavioral learning outcomes, while also highlighting challenges related to equipment access, user comfort, and simulation fidelity. Sustainable integration requires ongoing instructor development, investment in high-quality infrastructure, and clear regulatory guidance. The study contributes to aviation management by demonstrating that incorporating immersive technologies into cabin crew education strengthens safety culture, enhances workforce digital proficiency, and supports the long-term professional development of aviation personnel.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686283</guid>
    </item>
    <item>
      <title>Bayesian best-worst method for cognitive reliability assessment in ballast water treatment systems on oil tankers</title>
      <link>https://trid.trb.org/View/2695315</link>
      <description><![CDATA[Operational safety and compliance with environmental regulations in the maritime sector are vital, particularly for Ballast Water Treatment Systems (BWTS) mandated by the Ballast Water Management (BWM) Convention. However, human errors during the operation of the complex systems significantly compromise their effectiveness and performance reliability. This article presents a robust approach to predict ship’s crew cognitive reliability and error systematically in tanker ship BWTS operations. The proposed approach integrates the Bayesian Best-Worst Method (BBWM), which probabilistically incorporates expert opinions to yield highly consistent CPC common performance condition (CPC) weights, which model the causal dependencies among cognitive performance factors using the weights. While the BBWM is employed to weight the CPC and to enhance the accuracy and robustness of the results, the Cognitive Reliability and Error Analysis Method (CREAM) systematically predict cognitive failure probability of ship crew. The findings show that “Verify BWTS readiness status" is having the highest cognitive failure probability value with 5.24E-02. This article provides practical insights to shipowners, superintendents, HSEQ managers, ship inspectors, Masters and chief engineers to enhance the safe and reliable operation of ballast water treatment systems on tanker ships.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2695315</guid>
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    <item>
      <title>Decoding digital burnout in the maritime workforce: An explainable ai approach to digital ageing and deprivation</title>
      <link>https://trid.trb.org/View/2712654</link>
      <description><![CDATA[Background The rapid digitalisation in the maritime sector has introduced new psychological risks; however, the non-linear pathways of digital burnout remain under-researched. Objective This study aims to investigate the multifaceted nature of digital burnout among seafarers by identifying key demographic drivers and latent risk profiles. Methods A hybrid analytical framework was employed, integrating traditional ANOVA with K-Means clustering and explainable artificial intelligence (XAI) tools, specifically SHAP and Decision Tree classifiers, to analyse survey data from Turkish seafarers. Results While ANOVA identifies age, income, and experience as significant drivers, machine learning reveals complex risk pathways. K-Means clustering identified three distinct profiles, with the high-risk group (Cluster 0) exhibiting critical Digital Ageing (45.12) and Emotional Exhaustion (21.90) scores. Decision Tree analysis indicates medium income as the primary root node for burnout stratification. SHAP analysis pinpoints mid-to-late career experience (16–20 years) and being a Deck Officer as the strongest catalysts for exhaustion, whereas younger age (21–25) and lower experience (6–10 years) act as protective buffers. Conclusions Digital burnout in the maritime sector is a spectrum condition driven by professional hierarchy and career midpoint pressures, rather than mere technology use. Findings underscore the necessity for human-centred digital policies and targeted resilience initiatives to safeguard the global maritime workforce's mental well-being.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712654</guid>
    </item>
    <item>
      <title>Simulating a new crew planning process at Netherlands Railways</title>
      <link>https://trid.trb.org/View/2694558</link>
      <description><![CDATA[Netherlands Railways is considering a shift towards a novel crew planning process featuring individual Sharing-Sweet-and-Sour rules, designed to provide each crew member with a fair and varied work schedule. This approach replaces the current, crew base-level fairness mechanisms. In the new process, template-based rosters specify generic time windows, to which personalised duties are assigned in the operational phase. To evaluate the feasibility of the novel process, we develop a scalable simulation framework that accurately models the operational crew planning phase. The framework features a column generation heuristic to construct personalised duties, a network decomposition strategy to reduce computing times, and stochastically generated disruptions to simulate daily operations. Simulating the process for 3265 guards throughout the year 2024, we find high conformance with the proposed individual Sharing-Sweet-and-Sour rules, with over 93% compliance for four out of six attributes. Our results yield valuable inputs for ongoing discussions with the works council and provide company experts with a powerful strategic tool. These insights are relevant to other transport operators, showing that fair and attractive individual work schedules can be constructed and highlighting the practical benefits of tailored simulation tools.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:50:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694558</guid>
    </item>
    <item>
      <title>A Human Factors Bibliography on Display, Communication, Controller Workload, Aircrew Activity, and Man-Machine Relationships</title>
      <link>https://trid.trb.org/View/2719326</link>
      <description><![CDATA[The purpose of this task was to develop a bibliography covering recent technical literature in selected human factors areas for the Federal Aviation Administration's System Design Team. Some references, although somewhat less recent, were nevertheless included because of their importance. The bibliography should acquaint the team members of the state of the developments in their required areas.]]></description>
      <pubDate>Mon, 13 Jul 2026 09:39:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2719326</guid>
    </item>
    <item>
      <title>Reliability-Centered Assessment of Misreporting-Related Accident Risks in Turkish Straits Passages</title>
      <link>https://trid.trb.org/View/2724829</link>
      <description><![CDATA[Ship traffic through the Turkish Straits occurs in a highly constrained navigational environment, where inaccurate prepassage reports can significantly increase the risk of maritime accidents. Vessels are required to submit Sailing Plan declarations (SP-1 and SP-2) before entry, but these reports often fail to reflect the actual technical and operational condition of the ships. Commercial pressure, time constraints, and intentional misreporting can create discrepancies between declared and actual readiness, raising the likelihood of collisions, groundings, or loss of maneuverability in congested waters. This study develops a reliability-centered framework to assess accident risks associated with reporting, contributing to the literature on human reliability and uncertainty management in maritime traffic. Risk criteria were identified by reviewing regulatory requirements, accident records, and relevant literature, and refined through expert input from Vessel Traffic Services (VTS), pilotage, Port State Control (PSC), and ship operations. The framework combines the Fine–Kinney method with an Intuitionistic Fuzzy TODIM (an acronym in Portuguese for interactive and multicriteria decision-making). The results indicate that undisclosed propulsion deficiencies, steering problems, and nontransparent withdrawal from passage queues are the most critical accident precursors. These findings highlight the importance of reliable reporting and provide practical and transferable guidance for reducing the risks of maritime accidents and improving risk management for vessel traffic. In particular, the results support the prioritization of propulsion and steering system checks, as well as the closer scrutiny of vessels withdrawing from passage queues, offering actionable insights for VTS operators, PSC authorities and marine pilots.]]></description>
      <pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724829</guid>
    </item>
    <item>
      <title>Large-Scale Airline Crew Recovery Using Mixed-Integer Optimization and Supervised Machine Learning</title>
      <link>https://trid.trb.org/View/2686231</link>
      <description><![CDATA[Airlines take a variety of actions to recover schedules of their aircraft, crew, and passengers from operational disruptions. Aircraft are typically recovered first, followed by crew recovery, and then passenger recovery. This paper aims to repair disrupted crew schedules while ensuring the feasibility of previously decided aircraft recovery plans and indirectly accounting for passenger disruption costs. We develop a fast solution approach that effectively combines mixed-integer optimization and supervised machine learning (ML) methods to find high-quality solutions to large-scale recovery problems. Our approach reduces the solution space by adding constraints based on the patterns discovered in the solutions to offline instances. The model with the added constraints is solved using a mixed-integer optimization solver. To account for the fact that the available time for airlines to handle disruptions may vary during the day of operations, our solution approach allows parameter tuning to flexibly match the extent of solution space reduction to the available runtime. This helps the proposed method to effectively navigate the trade-off between solution quality and runtime. Extensive computational experiments with actual flight and crew schedules of a major U.S. airline with more than 2,800 daily flights show that our approach consistently generates solutions of significantly higher quality than benchmarks and is estimated to provide tens of millions of dollars of reduction in annual operating costs. Moreover, our ML models have interpretable structures that are critical to enhance end-user trust in the ML recommendations. Finally, our approach yields solutions that are more robust to uncertainty in delay prediction than those found by direct optimization..]]></description>
      <pubDate>Tue, 30 Jun 2026 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686231</guid>
    </item>
    <item>
      <title>Joint Rolling Stock and Crew Scheduling with Multi-Train Composition in Urban Rail Networks</title>
      <link>https://trid.trb.org/View/2686230</link>
      <description><![CDATA[Rolling stock scheduling and crew scheduling are two fundamental problems that arise in the planning of urban rail operations and that are especially important in the case of flexible operations in real-world networks. These problems are often solved separately and sequentially in different planning stages, resulting in limited options to adjust crew schedules after rolling stock decisions have been made. To better adjust these two decision-making processes and achieve better solutions, this paper studies a joint rolling stock and crew scheduling problem in urban rail networks. A novel optimization model is formulated with the aim of reducing the operational cost of rolling stock units and crew members. In addition, the multi-train composition mode is considered to adequately match different frequency requirements and rolling stock transport capacities. To solve the model, a customized branch-and-price-and-cut solution algorithm is proposed to find the optimal schedule schemes, in which Benders decomposition is used to solve the linear programming relaxation of the path-based reformulation. Two customized column generation methods with label correcting are embedded to solve the master problem and pricing subproblem for generating paths (columns) corresponding to rolling stock units and crew groups, respectively. Finally, a branch-and-bound procedure with several acceleration techniques is proposed to find integer solutions. To demonstrate the computational performance and the robustness of the proposed approaches, a series of numerical experiments are performed in real-world instances of the Beijing urban rail network under different settings. The computational results confirm the high efficiency of the solution methodology and the benefits of the flexible operation schemes based on the solutions found by the proposed methods.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686230</guid>
    </item>
    <item>
      <title>Evaluation of flight crew risk factors in aviation occupational health and safety: Application of Fermatean Fuzzy DEMATEL approach and machine learning</title>
      <link>https://trid.trb.org/View/2708988</link>
      <description><![CDATA[BackgroundFlight crews operate in an environment surrounded by a wide array of physical, chemical, biological, ergonomic, and psychosocial risks. It is inevitable that flight crews working under such challenging conditions face numerous unresolved issues, which pose significant threats to occupational safety. To prevent workplace accidents and occupational diseases in this sector, detailed studies in this area are essential.ObjectiveCivil aviation operates on a strict timetable with no tolerance for delays, and it is a sector where the costs of errors are exceedingly high. For flight crews, who are among the most critical stakeholders in this industry, mitigating all identified risks and developing appropriate strategies incurs additional costs and processes for the organization. Therefore, it is imperative to prioritize these risks and focus on addressing the most significant ones first.MethodsThis study employs a novel approach and proposes a framework for identifying flight crew risk factors from an aviation safety perspective and develops the Fermatean Fuzzy Decision-Making Trial and Evaluation Laboratory (FF-DEMATEL) method to analyze the interrelationships among these factors. Study offers a comprehensive view of flight crew risk factors, facilitating decision-making and strategy development to enhance effectiveness in an uncertain and interconnected environment. The rankings of the risks were determined using the FF- DEMATEL method, with expert opinions serving as the primary data source. Expert weights were determined through machine learning based on their age, education level, and professional experience. This methodology presents a unique contribution to existing literature, offering fresh insights into this critical area of study.ResultsEvaluation of 11 factors reveals personnel-related risks, route-based risks and time-related risks as primary concerns, underscoring the multifaceted nature of these challenges.ConclusionsTo effectively control the risks in the working environment of flight crews and enhance occupational safety, it is essential to conduct detailed analyses of flight routes, increase awareness initiatives, provide comprehensive safety training, enhance improvement monitoring, and encourage the sharing of near-miss incidents.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708988</guid>
    </item>
    <item>
      <title>Railway Crew Rostering: Tailored Optimization Solutions in Daily Use in Berlin</title>
      <link>https://trid.trb.org/View/2709140</link>
      <description><![CDATA[We are presenting a crew planning optimization project for the train drivers of an urban rail transportation company. The core is about crew rostering, i.e., defining over a certain period of time (e.g., one year) for each train driver sequences of working days and rest days, and also specifying whether working days shall contain either some early, late, or night shift. But the project was not only about solving just one classical crew rostering problem. Rather, during the last three years, the entire process of crew rostering had been investigated. Apart from having modeled, solved, and implemented two different variants of crew rostering—a cyclic and an acyclic one—there have been designed and used three further optimization models for less typical surrounding sub-processes, as well as a simple crew assignment model. During the design and implementation process of these six optimization models, the focus had been put on their annual and daily usability, respectively. In particular, many practical requirements had been collected and implemented in rather straightforward ways. The result is a family of mathematical optimization models, whose results cover the valid annual crew rosters from the year 2024 on, as well as the daily assignment of specific duties to train drivers from May 2025 on at S-Bahn Berlin GmbH.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709140</guid>
    </item>
    <item>
      <title>AIRCREWSCHED: A meta review of aircrew scheduling and its research challenges</title>
      <link>https://trid.trb.org/View/2625935</link>
      <description><![CDATA[Aircrew scheduling is an important part of the aviation industry. As airline networks expand and operational models evolve, crew scheduling becomes more complex and challenging, requiring the development and application advanced optimization techniques. Given the diverse nature of this field, covering aspects of air transport management, computer science, and operations research, synthesizing the state of the art and identify important research gaps, is often problematic. In this study, we provide a meta review on survey and overview papers in the field. As part of our comprehensive dissection of the extant literature, we introduce the AIRCREWSCHED framework, which encapsulates twelve essential research challenges that have emerged through our meta review on aircrew scheduling, covering, among others, aspects of artificial intelligence, integration, resource sharing mechanisms, crew individuality, and sustainability considerations. We believe that our meta review aids the community to make orchestrated efforts in advancing aircrew scheduling.]]></description>
      <pubDate>Wed, 24 Jun 2026 11:31:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625935</guid>
    </item>
    <item>
      <title>Assessing Radiation Exposure, Health Outcomes, and Mitigation Strategies for Flight Crewmembers</title>
      <link>https://trid.trb.org/View/2714408</link>
      <description><![CDATA[Flight crewmembers are occupationally exposed to cosmic ionizing radiation. Although the radiation dose received during any individual commercial flight is generally low, dose accumulates over the course of a career and varies based on flight altitude, latitude, duration, and solar activity. Ionizing radiation is a known cause of certain cancers and other adverse health outcomes, yet important questions remain regarding the long-term health implications of cumulative radiation exposure. This report examines scientific evidence related to occupational radiation exposure among flight crews, evaluates radiation dose estimation models, assesses the feasibility of future epidemiologic studies, and identifies opportunities to strengthen monitoring, communication, and mitigation strategies. A key message from this report is that radiation exposure in aviation warrants attention as an occupational hazard. Estimated annual occupational doses for flight crew generally fall well below recommended limits; however, exposures over a career and cumulative dose received warrant monitoring and tracking. Additionally, the tools for monitoring exposure and educating workers are already available but are not uniformly utilized or deployed. The report presents a coordinated path forward for improving occupational radiation safety in commercial aviation. Recommendations include implementing structured radiation safety programs, developing a centralized dose-tracking system for flight crew, improving communication and considerations for pregnant workers, strengthening radiation dose estimation models, and supporting long-term research infrastructure. These actions would help airlines, regulators, and flight crewmembers better understand and manage cumulative radiation dose received over a flight crewmember's career.]]></description>
      <pubDate>Tue, 16 Jun 2026 16:11:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714408</guid>
    </item>
    <item>
      <title>Noise exposure, circadian misalignment, and fatigue in maritime operations: A case study on a Ro-Pax vessel</title>
      <link>https://trid.trb.org/View/2676854</link>
      <description><![CDATA[Fatigue represents a critical human-reliability concern in maritime operations and can undermine the safety performance of ship systems. This exploratory case study examines associations between onboard noise exposure, work–rest patterns, circadian typology, and seafarer fatigue aboard a Spanish-flagged Ro-Pax vessel. Objective noise measurements were collected following IMO guidelines, and crew fatigue and mental workload were assessed using the NASA-TLX, SOFI-SM, and rMEQ instruments (n = 22, representing 95.6% of the crew). Engine-room noise exceeded 96 dB(A), and cabin noise, while compliant with IMO limits, surpassed World Health Organization recommendations for undisturbed sleep. Higher role-based noise exposure was associated with higher levels of occupational fatigue, particularly in dimensions related to physical fatigue and physical discomfort, while no significant association was observed with perceived mental workload. Fatigue levels varied descriptively across occupational roles and circadian profiles, with higher fatigue observed in duty schedules misaligned with individual circadian preferences. The findings indicate that compliance with existing noise regulations does not necessarily ensure conditions conducive to adequate recovery or sustained human reliability. While causal relationships cannot be inferred, the study provides context-specific evidence supporting the integration of noise management, circadian-aware scheduling, and improved accommodation insulation into maritime safety and reliability frameworks.]]></description>
      <pubDate>Tue, 09 Jun 2026 14:43:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676854</guid>
    </item>
    <item>
      <title>Quantitative Assessment of Language and Cultural Diversity as Human Reliability Factors in Maritime Safety</title>
      <link>https://trid.trb.org/View/2667964</link>
      <description><![CDATA[Reliable shipboard operations depend on effective communication and situational awareness among multinational crews. Language barriers and cultural diversity, if unmanaged, can degrade operational reliability and compromise safety-critical decision-making. Yet quantitative evidence on how language and cultural diversity shape accident risk remains limited. This study applies a Bayesian Network (BN) framework to assess the reliability and safety implications of language and cultural diversity using 550 maritime accident investigations. Natural Language Processing (NLP) extracted indicators, including crew nationality composition, language diversity (Shannon Index), cultural diversity (Hofstede Index), and adherence to Standard Maritime Communication Phrases (SMCP), which were integrated into the BN model. Parameters were estimated using the Expectation Maximization (EM) algorithm, allowing the model to quantify pathways to communication errors, conflict, and accident severity. Results show that high language diversity and low proficiency substantially increase communication failures: miscommunication and misinterpretation together account for 68% of observed errors (43% and 25%), with high language diversity the most frequent state (∼48%). Verbal exchanges remain the most failure-prone mode (47%), and container ships account for the largest share of communication-related accidents (42%), with collision and grounding comprising 41% and 24% of incidents. At the consequence level, major repairs are the modal outcome (∼40%), but scenario analysis shows that under highly adverse diversity and proficiency configurations, the combined probability of major repair and total loss can exceed 60%. Strength-of-influence results identify vessel size, nationality, and language diversity as structural drivers (0.54), while language training (0.26), SMCP use (0.24), and cultural familiarization (0.27) are the most effective levers to improve proficiency and reduce conflicts. Scenario analysis further demonstrates that aligning these levers can cut total loss probability on large container ships from about 23% under stressed human-factor conditions to 3% under best-practice communication settings, while shifting incident severity toward minor outcomes and containing casualties. These results provide a quantitative basis for setting language-training thresholds, enforcing SMCP, and designing crew-mix and cultural familiarization policies in maritime safety management.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667964</guid>
    </item>
    <item>
      <title>Managing unruly passengers in commercial aviation: cabin crew perspectives on escalation, intervention, and flight safety</title>
      <link>https://trid.trb.org/View/2666702</link>
      <description><![CDATA[Unruly passenger behavior represents a persistent challenge for aviation safety, yet existing research has largely emphasized incident outcomes and severity classifications rather than the processes through which such events unfold and are managed in practice. This study examines how airline cabin crew experience, interpret, and manage unruly passenger incidents, with particular attention to escalation dynamics, intervention strategies, and their implications for flight safety. Drawing on qualitative data from semi-structured interviews with 21 actively working cabin crew members, the study analyzes 48 unruly passenger incidents using an incident-level thematic approach. The findings show that unruly passenger incidents are rarely perceived as sudden or unpredictable. Instead, escalation typically develops through early interactional warning signs, including persistent non-compliance, dismissive attitudes, and repeated boundary testing. Cabin crew rely on experiential judgment to interpret these cues and to anticipate escalation before formal safety thresholds are crossed. Interventions functioned as active risk-containment practices rather than reactive responses, with graduated communication strategies and coordinated crew action playing a central role in de-escalation. Authority emerged as a negotiated and situational resource, with cockpit coordination serving to reinforce legitimacy and shared situational awareness as perceived risk increased. Beyond operational considerations, the study highlights the cumulative emotional labor involved in managing unruly passenger behavior, extending across incidents of varying formal severity. By foregrounding the interactional and processual dimensions of unruly passenger management, this study contributes to transportation security research by demonstrating how flight safety is actively produced through judgment, coordination, and emotional regulation within constrained operational environments.]]></description>
      <pubDate>Mon, 11 May 2026 08:50:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666702</guid>
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