<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/common/TRIS Suite/feeds/RSS.aspx?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzcxIiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSIyeWVhcnMiIC8%2BPHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycyAvPjxyYW5nZXMgLz48c29ydHM%2BPHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8%2BPC9zb3J0cz48cGVyc2lzdHM%2BPHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg%3D%3D" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>A startle and surprise management method for cabin crew</title>
      <link>https://trid.trb.org/View/2736335</link>
      <description><![CDATA[Startle and surprise are known to potentially incapacitate professionals who respond to emergency situations. In the aviation domain, self-management methods to prevent such incapacitation have been introduced for pilots. This study aims to explore the relevance of similar self-management methods tailored for cabin crew, using subjective evaluation by cabin crew and cabin crew instructors. First, a method was designed and refined using interviews and a focus group with nine subject-matter experts, consisting of four steps: protect, reset, check and act. Second, relevance of the method was quasi-experimentally tested by letting 15 cabin crew members apply it in a simulated firefighting scenario. After performing the scenario, they rated whether the method had positive or negative effects, was difficult or easy to use, and they retrospectively rated their perceived stress before and after applying the method. Participants positively rated the method’s effectiveness and usability, and reported the method to significantly reduce perceived stress. Participants provided several suggestions for improving the method, and for approaches to integrate the method more effectively into existing protocols. The outcomes of this study provide direction on the design and use of self-management methods that can help manage startle and surprise in teams.]]></description>
      <pubDate>Mon, 05 Oct 2026 11:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736335</guid>
    </item>
    <item>
      <title>Sim-to-Real Control for Triple Active Bridge Converters in Electric Aircraft: An Online-Trained Deep Reinforcement Learning Method</title>
      <link>https://trid.trb.org/View/2780993</link>
      <description><![CDATA[The triple active bridge (TAB) enables three-port bidirectional transfer with high-frequency isolation, making it highly suitable for electric aircraft demanding efficient and lightweight power systems. However, TAB control is challenged by inherent port coupling, where disturbances at one port propagate throughout the system; meanwhile, current stress and efficiency constraints further complicate the control design. Recent advances in deep reinforcement learning (DRL) enable adaptive, model-free control but face sim-to-real limitations. To address these challenges, this article proposes a physics-informed online-trained DRL (OT-DRL) framework for TAB converters: physical constraints are embedded into the reward function and action space, while real-time hardware data are integrated into the replay buffer to bridge the sim-to-real gap. The proposed online-trained DRL approach improves learning efficiency, transient dynamics, and ensures zero voltage switching under all conditions. Experiments demonstrate 11.3% higher episode efficiency, 2.6% greater final reward, and significant enhancements in dynamic response, 15% current stress reduction, and overall efficiency across various operating scenarios.]]></description>
      <pubDate>Mon, 05 Oct 2026 11:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780993</guid>
    </item>
    <item>
      <title>Impact of Atmospheric Boundary Layer Turbulence on Noise from Helicopter and Advanced Air Mobility Aircraft</title>
      <link>https://trid.trb.org/View/2784757</link>
      <description><![CDATA[A major barrier to public acceptance of commercial rotorcraft operations is noise. The emerging advanced air mobility (AAM) market is expected to use well-designed, quieter electric vertical takeoff and landing (eVTOL) aircraft to avoid current limitations on helicopter operations in urban areas caused by their acoustic impact. This paper introduces the conceptual Penn State University Reference Aircraft 3 (PSU-RA3) to understand the potential acoustic impact of tiltrotor eVTOL aircraft, such as the Joby S4. A new quasi-steady implementation of the Penn State Noise Prediction System (PSU-NPS) is presented. PSU-MulTINoise, a novel time-domain broadband noise prediction code, was developed and integrated into the system to model broadband noise from atmospheric boundary layer (ABL) turbulence interacting with the rotors. The ABL forms in the near-surface flow as the wind passes over the terrain and as thermal gradients drive convective motion. The system was validated against previously published flight test data for the Airbus AS350 and a Joby preproduction prototype. Variations in arrival and departure maneuver noise were studied around three heliports in New York City (NYC): the Downtown Manhattan Heliport (JRB), LaGuardia Airport (LGA), and John F. Kennedy International Airport (JFK). Predicted noise levels increased when ABL turbulence was included. Significant variation in average day noise level (DL) contours was observed between heliports owing to differences in adjacent urban terrain. The variation was greater for the PSU-RA3 eVTOL than for the AS350 helicopter. The results show that changes in noise levels owing to AAM operations in urban areas should be evaluated using site-specific terrain and atmospheric conditions.]]></description>
      <pubDate>Sat, 03 Oct 2026 18:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784757</guid>
    </item>
    <item>
      <title>Numerical study of unsteady aerodynamic interference in dual-helicopter/ship flow fields</title>
      <link>https://trid.trb.org/View/2782418</link>
      <description><![CDATA[To clarify the unsteady aerodynamic interference in simultaneous shipboard dual-helicopter operations, a high-fidelity Computational Fluid Dynamics (CFD) model based on Improved Delayed Detached-Eddy Simulation (IDDES) was established for a Wasp-class ship under headwind, Green 30° wind, and Red 30° wind conditions. The flow evolution, aerodynamic-force histories, spectral characteristics, and coherence of the coupled flow fields were systematically analyzed. The results show that the coupled flow is governed by the interaction of the bow vortex, deck-edge vortex, superstructure wake vortex, and rotor-induced vortex, while wind direction changes their relative importance. Under headwind conditions, the fore-aft asymmetry is most evident, and the maximum rotor-lift difference reaches approximately 25 kN, corresponding to a 17.6% reduction for the trailing helicopter due to the leading-helicopter wake and downwash. Spectral analysis shows dominant low-frequency components from 0.4 to 1.2 Hz, together with a persistent blade-passing frequency peak near 23.87 Hz. Under Green 30° and Red 30° winds, the dominant interference shifts toward the deck-edge vortex and leading-helicopter wake, producing more discrete multi-band spectral and coherence characteristics. Overall, inflow direction and deck position significantly affect dual-helicopter aerodynamic interference and should be considered in deck scheduling and safe operating limit assessment.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782418</guid>
    </item>
    <item>
      <title>Artificial Intelligence (AI) for Integrated Safety Assessment Model (ISAM)</title>
      <link>https://trid.trb.org/View/2780457</link>
      <description><![CDATA[This project aims to advance risk-informed decision-making at the Federal Aviation Administration (FAA) by developing machine learning techniques for extracting safety-relevant information from incidents and accident reports and enhancing large language model (LLM) capabilities for navigating aviation regulatory text. The work addresses two primary challenges: the labor-intensive process of manually extracting safety data from Aviation Safety Reporting System (ASRS) and National Transportation Safety Board (NTSB) narratives for use in the Integrated Safety Assessment Model (ISAM), and the inability of general-purpose language models to accurately interpret the hierarchical structure and cross-references within Title 14 of the Code of Federal Regulations (CFR). To improve safety data extraction, the project developed two datasets. The first is a question answering (QA) dataset designed to enable precise supervision for fine-tuning LLMs to extract events, contributing factors, and operational conditions from narrative text. The second consists of synthetic safety reports generated using a fine-tuned LLM informed by a domain-specific knowledge graph (KG) built from FAA aircraft data to eliminate aircraft specification errors and improve multilabel classification performance. To enhance regulatory reasoning, the project built a regulation-aware knowledge graph and developed a retrieval- augmented generation (RAG) system with improved retrieval precision for semi-structured regulatory content. An evaluation framework was also implemented by adapting retrieval-augmented generation assessment (RAGAS) metrics for regulatory contexts and introducing a custom answer completeness metric to address the “missing zone problem” of incomplete retrieval. Finally, the FAA-provided Daedalus multi-agent RAG system was migrated to local infrastructure using the Model Context Protocol (MCP) and extended with a completeness-evaluation agent that provides actionable diagnostics by distinguishing between retrieval and generation incompleteness.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780457</guid>
    </item>
    <item>
      <title>Emergency Life Saving through Multi-Modal Mobility (ELSa-M3) Southern Alleghenies Planning &amp; Development Commission</title>
      <link>https://trid.trb.org/View/2780506</link>
      <description><![CDATA[The Southern Alleghenies Planning & Development Commission (SAP&DC) region has a high concentration of overburdened, underserved communities that struggle with nutrition and drug addiction: hence our focus on the real and immediate problems of Opioid overdose, cardiac arrest, and anaphylactic shock. These problems are compounded by economic constraints (limited tax base to extend emergency services and surface transport), workforce and recruiting challenges (chronically understaffed and short-staffed emergency services), and geography (a mountainous region that limits straight-line surface response). To improve lives and reduce harm through transit in the SAP&DC region, we have to find creative ways to respond to medical emergencies both faster and cheaper. The Emergency Life Saving through Multi-Modal Mobility (ELSa-M3) project is an innovative use of uncrewed aircraft systems (UAS, or drones) to enhance emergency services logistics in the region through a public ground-based sensor network integrated with airspace management. ELSa-M3 will utilize UAS to improve emergency response time and resiliency by responding to emergency calls with drones delivering NARCAN (Opioid overdose), EpiPen (allergic shock), Tourniquet (bleeding), and AEDs (cardiac arrest). The prototype system developed during the first stage of our SMART grant addresses real world issues in this region and can improve access to healthcare services and the quality of life for the underserved communities within the project area and the broader Stage 2 area. Stage 1 of the ELSa-M3 project was located in a historically income, health, housing, pollution and transportation disadvantaged community straddling the border of Cambria and Somerset Counties, centered on Johnstown, Pennsylvania, and nearby communities.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780506</guid>
    </item>
    <item>
      <title>Study on the Modernization and Improvements to Aircraft Evacuation: FAA Reauthorization Act of 2024 (Public Law 118-63) — Section 365</title>
      <link>https://trid.trb.org/View/2780507</link>
      <description><![CDATA[The Federal Aviation Administration (FAA) completed this report pursuant to Section 365(a) of the FAA Reauthorization Act of 2024 titled “Modernization and Improvements to Aircraft." This section mandates the FAA Administrator to study enhancements to evacuation standards for manufacturers and carriers of transport category aircraft, as specified in parts 25 (Airworthiness Standards: Transport Category Airplanes) and 121 (Operating Requirements: Domestic, Flag, And Supplemental Operations) of Title 14, Code of Federal Regulations. This report summarizes the results of the FAA’s analysis of evacuation procedures under these parts. The FAA finds that evacuation safety levels remain very high and that evacuation incidents are exceedingly rare relative to the total number of flights.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780507</guid>
    </item>
    <item>
      <title>What Aircrews Should Know About Their Occupational Exposure to Ionizing Radiation, 2026 Edition</title>
      <link>https://trid.trb.org/View/2780505</link>
      <description><![CDATA[Through advisory materials such as this report, AC 120-61 and its successors, resources offered from its Radiobiology Services website, and other related work, the Federal Aviation Administration (FAA) assists and advises those seeking to understand and minimize the hazards associated with ionizing radiation sources encountered in aviation, including recommendation of occupational exposure limits for aircrews and providing computer software for estimating the amounts of solar and galactic cosmic radiation received on flights. This report updates report DOT/FAA/AM-03/16 and is intended as an introductory briefing on the topic of occupational exposure to in-flight ionizing radiation in the form of a series of questions and answers. Air carrier crewmembers and others who regularly fly, while not classified in the U.S. as radiation workers, are considered occupationally exposed to ionizing radiation. The occupational exposures come from both natural and technological sources. The most consistent source of this exposure is naturally occurring galactic cosmic radiation, which increases as altitude increases and with increasing proximity to either geomagnetic pole. On very infrequent occasions, the Sun and other sources also contribute significantly to the ionizing radiation received during air travel. At the low doses and dose rates typically encountered in aviation, the principal health concern is not acute radiation induced injury but rather is the small increases in lifetime risks of radiation-induced diseases such as cancers and genetic defects in future generations.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780505</guid>
    </item>
    <item>
      <title>The Impact of General Aviation on State and Local Economies, State Reports 2026, 4th Edition</title>
      <link>https://trid.trb.org/View/2780223</link>
      <description><![CDATA[This report, a joint effort of the Alliance for Aviation Across America, the National Association of State Aviation Officials, and the American Association of State Highway and Transportation Officials (AASHTO), highlights the economic and public value of general aviation and local airports to communities across the United States. This fourth edition updates each state profile with the latest available economic impact, airport, pilot, and aircraft data, while examining the many ways general aviation supports businesses, agriculture, health care, emergency response, workforce development, and community connectivity. The report also features state-led initiatives that involve emerging aviation technologies, infrastructure, sustainability, and other evolving priorities, and connects readers with additional online resources for exploring aviation’s economic impact at the state and local levels. This fourth edition of the report includes the following major revisions since the previous edition of this report, published in 2023: (1) Updated economic-impact information for all 50 states, drawing on the most recent state aviation economic-impact studies and other authoritative data sources available at the time of publication; (2) Revised state profiles describing the employment, payroll, economic output, infrastructure, and public-service benefits associated with general aviation; (3) A new executive summary placing state-level findings within the broader economic and transportation context; (4) New feature sections examining state leadership in areas such as advanced air mobility, uncrewed aircraft systems, electric and alternatively powered aircraft, and sustainable aviation fuel; (5) Expanded discussion of the role general aviation airports play in supporting rural access, health care, disaster response, agriculture, business connectivity, tourism, education, and other essential services; (6) Updated examples of state aviation programs, infrastructure investments, public-private partnerships, and emerging-technology initiatives; (7) A redesigned, online-first format intended to make state information easier to find, use, and share; and (8) Integration with supplemental online resources, including airport- and community-level economic-impact information available through the Alliance for Aviation Across America’s economic-impact tool.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780223</guid>
    </item>
    <item>
      <title>The impact of climate degradation originating from the aviation sector on financial development and ESG index: EU member states’ case</title>
      <link>https://trid.trb.org/View/2720997</link>
      <description><![CDATA[This study examines the impact of aviation-induced climate degradation on financial development and environmental, social, and governance (ESG) performance in European Union member states. To capture sector-specific environmental pressure, a Climate Degradation Index (CDI) is constructed from aviation-related landing and take-off emission indicators, while a country-level ESG index is derived from environmental, social, and governance indicators through Principal Component Analysis (PCA). Drawing on financial development theory, the Environmental Kuznets Curve (EKC) hypothesis, stakeholder theory, and the growth-environment trade-off framework, the analysis employs robust panel data estimators that account for cross-sectional dependence, autocorrelation, heteroskedasticity, and potential dynamic endogeneity. The results reveal a consistent dual pattern: aviation-related climate degradation is positively associated with the Financial Development Index (FDI), but it weakens ESG performance. This suggests that aviation activity can deepen financial systems through mobility, trade integration, tourism, logistics, investment demand, and capital flows, while simultaneously creating transition and environmental risks that reduce sustainability performance. The study therefore contributes to the literature by showing that aviation-induced climate degradation operates not only as an environmental externality but also as a structural channel through which financial expansion and sustainability pressures interact. Policy implications point to aviation-specific carbon pricing, sustainable aviation fuels, green aviation technologies, stronger ESG disclosure, and financial regulations that align sectoral growth with low-carbon transition objectives.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:48:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720997</guid>
    </item>
    <item>
      <title>Integrating behavior-based safety and human factors analysis for FOD prevention in Philippine airports</title>
      <link>https://trid.trb.org/View/2720981</link>
      <description><![CDATA[Foreign Object Damage (FOD) remains a critical safety concern in airport ground operations, often resulting from human error and procedural non-compliance. This study investigates the behavioral dimensions of FOD incidents in the Philippine aviation sector through a document-based qualitative analysis of 47 incident reports, 12 audit summaries, and 15 safety manuals and standard operating procedures (SOPs). Guided by the Human Factors Analysis and Classification System (HFACS) and the Dirty Dozen framework, the analysis identified recurring patterns of unsafe acts, inadequate supervision, and systemic weaknesses. Unsafe acts, including improper tool handling and failure to conduct FOD checks, were evident in 78% of incidents. Supervisory lapses and organizational deficiencies were present in 61% and 22% of cases, respectively. The Dirty Dozen elements most frequently observed were complacency, distraction, and lack of communication. The findings reveal a significant gap in the integration of Behavior-Based Safety (BBS) strategies within institutional documentation, with only four manuals referencing behavior observation or feedback mechanisms. A typology was developed to illustrate the interplay between latent organizational factors and observable unsafe behaviors. The study proposes a behavior-based FOD prevention framework and recommends policy enhancements, including mandatory behavioral audits, FOD-specific BBS training, and improved alignment with ICAO safety management principles. These insights have practical implications for Maintenance, Repair, and Overhaul (MRO) facilities and airport authorities seeking to embed human factors into proactive risk management systems. Future research should extend this inquiry through field observation or simulation-based validation of document-based findings.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:48:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720981</guid>
    </item>
    <item>
      <title>Safe skies begin with safe voices: Leadership and reporting in aviation</title>
      <link>https://trid.trb.org/View/2720982</link>
      <description><![CDATA[Safety reporting is essential to hazard detection in high-risk transport operations, yet frontline aviation personnel often hesitate to disclose errors or near misses. This study examines how safety-specific transformational leadership (SSTL) and ethical leadership (EL) are associated with safety reporting performance (SRP), and investigates psychological safety (PS) as a mediator underlying these relationships. Data was collected from frontline aviation employees across multiple operational aspects. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the hypothesized connections. The findings revealed that SSTL is positively associated with SRP, while EL is indirectly associated with SRP through PS. The mediating role of PS between SSTL and SRP, however, was not supported. These results indicate that while SSTL may be more directly linked to safety reporting, EL appears to be associated with SRP through the development of PS. Aviation enterprises should develop and evaluate leaders based on SSTL and EL competencies. Embedding these traits into training and performance systems may help support a culture of trust and openness, encouraging employees to report hazards and near misses without fear of reprisal.]]></description>
      <pubDate>Fri, 02 Oct 2026 15:56:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720982</guid>
    </item>
    <item>
      <title>Dynamic modeling and motion control of a novel multi-navigation-mode vehicle for stereo synchronous observation at the air-sea interface</title>
      <link>https://trid.trb.org/View/2782532</link>
      <description><![CDATA[The Long-endurance Sea-air-cross-domain Vehicle (LSV) is designed for long-endurance, high-maneuverability, and stereo synchronous observations (SSO) at the air-sea interface. To address the challenges of diverse actuators, and mission requirements, three operating modes are developed: surface ocean energy driven mode, surface rudder-propeller driven mode, and underwater gliding motion mode. A full-process simulation model for the LSV is established by analyzing actuator-induced changes in vehicle forces, particularly those associated with variations in submerged volume. To achieve SSO at the air-sea interface, a Task-Driven Multi-Level Motion Control (TDMMC) method is proposed. This method ensures orderly switching among different operating modes while enabling precise, controllable navigation within each operating mode. Finally, the key model parameters of “Yulang IV” were identified and calibrated by comparing the simulation results with the water tank experimental results. The relative error in the surface turning radius was 0.7%, while the relative errors in the zigzag maneuver period and yaw angular velocity were 0.2% and 1.9%, respectively, demonstrating good agreement between the simulation and experimental results. Furthermore, simulations of SSO missions during a typhoon passage demonstrate the effectiveness of the proposed control method.]]></description>
      <pubDate>Thu, 01 Oct 2026 11:44:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2782532</guid>
    </item>
    <item>
      <title>Predicting and preventing unruly passenger behaviour: a proactive framework for aviation security and crew training</title>
      <link>https://trid.trb.org/View/2711901</link>
      <description><![CDATA[Unruly passenger behaviour is a persistent and escalating challenge in global aviation, undermining safety, operational continuity, and crew well-being. While recent research documents a rise in disruptive incidents, most studies focus on post-incident responses, neglecting the underlying antecedent behaviours that precipitate escalation. This conceptual article addresses this critical gap by advancing a proactive, behaviour-based framework designed to predict and prevent unruly passenger behaviour throughout the entire passenger journey. Informed by behavioural threat assessment principles, best practices in aviation security, and the latest post-pandemic behavioural trends, the framework integrates predictive indicators, early detection cues, proactive intervention strategies, and robust response protocols. Notably, current training programmes often overlook essential behavioural science components, thereby limiting aviation personnel’s ability to identify and respond to early warning signs. Recent evidence highlights the significant role of intoxicants, including alcohol, psychoactive substances, and misused medications, in precipitating disruptive behaviour. These factors are implicated in a substantial proportion of unruly passenger incidents. Furthermore, emerging post-COVID-19 neurological considerations, although rare, underscore the need for greater behavioural awareness in high-stress aviation environments. These evolving influences reinforce the necessity for a proactive, behaviour-based prevention model. The framework presented herein provides a rigorous, structured model to enhance training curricula, elevate situational awareness, and facilitate coordinated response mechanisms among ground and in-flight teams. By shifting the paradigm from reactive management to proactive prevention, the article contributes substantively to the academic literature on aviation security. It offers actionable guidance for airlines, airports, regulatory bodies, and training organisations.]]></description>
      <pubDate>Thu, 01 Oct 2026 09:25:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711901</guid>
    </item>
    <item>
      <title>Understanding Air-Sea Freight Allocation Patterns at the Commodity Level: Empirical Evidence from South Korea</title>
      <link>https://trid.trb.org/View/2711944</link>
      <description><![CDATA[This study examines air–sea freight allocation at the commodity level using a data-driven framework based on observed trade data. Focusing on South Korea, a geographically isolated economy where international freight transport is effectively constrained to air and maritime modes, the framework combines fuzzy c-means clustering with a fractional logit model to capture heterogeneity in modal use across commodities. The clustering analysis identifies five distinct transport regimes, ranging from maritime-dominated commodities to structurally air-dependent goods with air shares exceeding 80%. Within the identified competitive regimes, the fractional logit results confirm that higher value-to-weight ratios are the primary determinants of air freight allocation. While geographical distance is accounted for, its influence remains secondary to the commodity's unit value and does not reach statistical significance in the baseline specification. Model calibration and destination-level assessments demonstrate high predictive accuracy across diverse product groups and geographical routes. Overall, the findings demonstrate that commodity-level characteristics explain the modal allocation of international freight in geographically constrained trade environments, highlighting the limitations of aggregate or purely categorical approaches.]]></description>
      <pubDate>Thu, 01 Oct 2026 09:25:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711944</guid>
    </item>
  </channel>
</rss>