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    <title>Transport Research International Documentation (TRID)</title>
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    <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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Coupled Computational Fluid Dynamics–Agent-Based Modeling of Air-borne and Fomite Infection Risk in Gate-to-Gate Air Travel</title>
      <link>https://trid.trb.org/View/2742774</link>
      <description><![CDATA[The COVID-19 pandemic exposed how vulnerable air travel is to infectious diseases and prompted stronger federal coordination and integrated research (Sun et al., 2022; GAO 2024). Boeing, the Federal Aviation Administration, Centers for Disease Control and Prevention, and National Research Council Canada formed a multidisciplinary team to tackle communicable disease challenges in aviation and enable rapid public health responses. This collaboration produced the Travel Risk In Pandemics (TRIP-X) framework to assess risk controls and evaluate mitigation strategies for different aviation and travel settings. We present the computational model architecture and methods used in TRIP-X to simulate airborne and fomite transmission in a gate-to-gate air travel scenario.]]></description>
      <pubDate>Wed, 26 Aug 2026 15:53:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742774</guid>
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    <item>
      <title>Transmission Routes of Respiratory Viruses: Understanding the Role of Shedding and Infectious Doses, Dispelling Myths and Exploring Knowledge Gaps Where An Evidence-Base Does Exist</title>
      <link>https://trid.trb.org/View/2742773</link>
      <description><![CDATA[Global pandemics and seasonal epidemics have significant economic and health impacts, yet there remain contradictory views on transmission routes and gaps in the evidence-based knowledge on the mechanistic dynamics related to respiratory disease. In preparation for the next pandemic, the US Centers for Disease Control and Prevention (CDC), the Canadian National Research Council, the US Federal Aviation Administration's (FAA’s) Office of Aerospace Medicine and Boeing have been in a multi-year collaboration to develop a systematic and holistic framework that examines the routes for disease transmission and the associate risk of exposure and infection, with this framework being adaptable to multiple pathogens and settings. This presentation will include field data, modelling and literature reviews for transmission dynamics across differing viruses. We present novel considerations to variables such as vaccine efficacy pertaining to immune status versus shedding, cumulative dosing effects, diagnostics testing applicability for infectious status versus viral remnants, as well as the physics of viral transfer via fomite, human-to-human touch and aerosol versus droplet. Such variables will also be related to the associated complex dynamics of human behaviour and the excretion/entry route of a specific disease.]]></description>
      <pubDate>Wed, 26 Aug 2026 15:53:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742773</guid>
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    <item>
      <title>Simulating the spatial spread and containment of a pandemic in a multimodal tourism district using agent-based modeling</title>
      <link>https://trid.trb.org/View/2717910</link>
      <description><![CDATA[The spatial dynamics of human mobility play a critical role in shaping disease transmission and containment policies, particularly in densely populated urban areas with significant tourism and transport activity. This study presents an Agent-Based Model to simulate the spread and containment of COVID-19 in Waikiki, Hawaii, a high-density resort district characterized by diverse transport modes and pedestrian interactions. The model simulates movements of pedestrians, motorists, and public transit users, using high‑resolution built-environment data to capture where and when contacts occur, and links interactions among infectious and susceptible individuals to infection risks under varying interventions. Intervention scenarios, including mask usage, testing/contact tracing, and vaccination, are simulated, and model outcomes are validated against observed case data (R2 = 0.864). Results demonstrate that enhanced contact tracing was the most effective intervention, reducing cumulative reported cases by 18.9 % and community transmission by 44 % compared to the realistic baseline. Higher vaccination coverage yielded a 4.5 % reduction in cases and a 15.8 % reduction in hospitalizations, highlighting the vaccine’s role in mitigating severe outcomes. Conversely, reducing contact tracing below baseline levels increased cumulative cases by 22.5 % and community cases by 53.2 %, while low mask use increased overall cases by 4.8 %, highlighting risks of relaxing interventions. Persistence of transmission, even with high vaccination rates, highlights the importance of policy measures, particularly rapid case identification and isolation in high‑mobility resort districts. These findings demonstrate the value of ABM as a decision‑support tool bridging transportation planning, urban epidemiology, and public health policy, an integration essential for resilient community management.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:49:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717910</guid>
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    <item>
      <title>The simulation of large-sized pathogen-laden respiratory droplet spread inside a passenger ferry</title>
      <link>https://trid.trb.org/View/2604950</link>
      <description><![CDATA[This paper presents computational fluid dynamics (CFD) of Reynolds-Averaged Navier Stokes (RANS) simulations to analyse the flow movement of pathogen-laden particles/droplets with relatively large diameter range of 50−150μm inside an air-conditioned ferry cabin. Three major cases are investigated, the first cases is when one infected passenger sits on a passenger chair that is located close to the edge of the cabin in the centre row; the second case is when the infected passenger is standing in the middle of the cabin; and the third case is for a passenger who sits on the opposite edge of the cabin, and located at the front row. For each of these three positions, three air conditioner velocities were investigated. The results indicate that as the air conditioner velocities velocity increases, the spread of the particles is also getting wider and they are elevated further from the floor. The simulation results also indicate that passenger location is also an important aspect in influencing the spread of the particles, particularly for those who are directly blown by the air conditioner’s airflow. Our results also show the effectiveness of 1.5-meter (minimum 1-meter) social distancing rule by the WHO (World Health Organisation) for particles with diameter range of 50−150μm, however there are certain cases in which this separation distance may be insufficient. Such as in which the infected patient is directly blown by the air conditioner or when the particle diameters are smaller than 50 μm in which it could be suspended in the air longer. Finally, this report also discuss RANS validation technique with respect to experiment and the weakness of RANS in simulating the movement of particles inside a room. Hence the result of the simulation should be treated as an approximation only.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604950</guid>
    </item>
    <item>
      <title>Diversity and Potential Environmental Risks of DNA Viruses on International Ships' Ballast Water at Shanghai Port, China</title>
      <link>https://trid.trb.org/View/2691583</link>
      <description><![CDATA[Over 10 billion tons of ballast water is transferred annually globally, transporting many microorganisms such as bacteria and viruses into new environments and harming local ecology, economy and human health. Numerous studies on bacteria in ballast water have shown its remarkable bacterial diversity and potential risks in recent years. However, the diversity of viruses present in ballast water and their potential environmental implications remain extremely limited in our understanding. In this investigation, we utilized viral metagenomic sequencing to evaluate the viral diversity and distribution in ballast water sourced from five distinct shipping routes that docked at Shanghai Port. Additionally, we compared the distribution of antibiotic resistance genes (ARGs) and virulence factors (VFs) harbored by viruses in ballast water from different routes. The viral diversity and community composition exhibited significant differences in the ballast water samples collected along distinct shipping routes. The virus diversity index varies across different routes, and notably, for the Shannon and Pielou indices, the DJ and GG routes were significantly lower than other routes. Although Caudoviricetes was the dominant viral group, the abundances of Megaviricetes and Faserviricetes were also relatively high, albeit showing variations among different routes. 224 ARGs were identified, including 143 single resistance genes (SARGs) and 81 multiple resistance genes (MARGs), and their distribution varied across different shipping routes. Similarly, certain types of VFs also exhibit differences across various routes, including nutritional/metabolic processes, biofilm formation, and exotoxins. Moreover, the comparison of viral environmental risks showed that ballast water from routes through the Indian Ocean and the South China Sea posed a greater environmental risk. In summary, the results of our research have uncovered the virus diversity and potential risks associated with different routes. This underscores the imperative of implementing targeted management strategies for each individual route.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691583</guid>
    </item>
    <item>
      <title>Modelling the spread of infectious diseases in public transport systems under varying demand patterns and capacity constraints</title>
      <link>https://trid.trb.org/View/2604542</link>
      <description><![CDATA[Understanding the dynamics of passenger interactions and their epidemiological impact across public transportation systems is crucial for both service efficiency and public health. High passenger density and close physical proximity have been shown to accelerate the spread of infectious diseases. During the COVID-19 pandemic, many public transportation companies took measures to slow down and minimize the spread of the disease. One of these measures was introducing spacing and capacity constraints on public transit vehicles. The objective is to explore the effects of changes in demand and transportation measures from an epidemiological point of view, offering alternative measures to public transportation companies to keep the system operational while minimizing the epidemiological risk as much as possible. The findings show that restricting vehicle capacity can significantly reduce the spread of infections, while demand-related measures have an even stronger effect. Combining these approaches offers the best solutions for balancing public health and operability.]]></description>
      <pubDate>Mon, 20 Apr 2026 11:14:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604542</guid>
    </item>
    <item>
      <title>SARS-CoV-2 Spread and Infection Risk in Public Transit Scenes: Simulation Study Featuring a Hybrid Crowd Dynamics and Disease Spreading Model</title>
      <link>https://trid.trb.org/View/2691699</link>
      <description><![CDATA[Two years ago, a new virus named Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) emerged. In the ensuing race to contain the virus, many non-pharmaceutical interventions (NPIs) have been introduced. Yet questions like “What is the risk of SARS-CoV-2 infection in a particular scenario?” and “Which NPIs limit virus transmission most effectively?” remain. Crowd and epidemiological simulation models can help formulate an answer to these questions. This paper studies virus spread and infection risk using a newly developed hybrid virus spread model PeDViS (Pedestrian Dynamics–Virus Spread model), which links an existing validated crowd movement dynamics model (NOMAD) with a new virus spread model (QVEmod). In particular, five common public transit scenarios are simulated: walking through a corridor, buying a ticket, moving through the ticket gates, waiting at a platform, and traveling by train. The relative impact of four variables (i.e., demand, waiting time, facial masks, and ventilation) was studied. This study illustrates that PeDViS can provide comprehensive insights into virus spread and the relative differences in infection risk. Furthermore, it corroborates the impacts featured in literature for all public transit scenarios. That is, ventilation and facial masks limit the probability of infecting other individuals. Moreover, waiting time and higher demand levels increase the probability of infecting other travelers. Second, especially large impacts of the NPIs facial masks and ventilation are found for the more “dangerous” scenarios; that is, long queues, delays, or waiting times coincide with high demands and crowd densities.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:26:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691699</guid>
    </item>
    <item>
      <title>Modeling the infection risk in vehicles of local public transport for aerosol transmitted viruses</title>
      <link>https://trid.trb.org/View/2681522</link>
      <description><![CDATA[As a first step to creating a resilient local public transportation system, the infection risk in the vehicle must be identified. Subsequently, measures to reduce the infection risk can be undertaken and communicated. This paper presents a model to identify the infection risk on local public transportation through both contact and infection probability. A simulation of the air movements inside two example vehicles under differing environmental conditions enabled the infection probability to be estimated. However, the contact probability links the infection probability to the current spread of a disease. The model includes trip parameters (the number of contacts and contact time), virus parameters (aerosol particle size, virus titer, minimal dose of infection), and epidemiologic parameters (incidence and estimated number of unreported cases), as well as environmental parameters (speaking behavior, mask wearing).The model enables the average infection risk for a certain time and area to be calculated, and also special cases of transmission in local public transportation vehicles to be investigated. It provides the present infection risk for a person using local public transportation and can be used as an objective tool for comparing the impact of measures meant to reduce infection risk (e.g., masks) by transit associations as well as policy makers. This in turn could benefit the health and wellbeing of all local public transport passengers.]]></description>
      <pubDate>Tue, 07 Apr 2026 15:36:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681522</guid>
    </item>
    <item>
      <title>Aviation Infectious Risk and Safety: A Collaborative Program for Modeling Infectious Disease Transmission in Air Travel</title>
      <link>https://trid.trb.org/View/2683243</link>
      <description><![CDATA[The Aviation Infectious Risk and Safety (AIRS) program addresses the U.S. Government Accountability Office (GAO) recommendation for stronger federal leadership and coordination on communicable disease transmission in air travel. Led by the Federal Aviation Administration (FAA), AIRS integrated The Boeing Company, the Centers for Disease Control and Prevention, the National Institute for Occupational Safety and Health, National Research Council Canada, and other interdisciplinary partners to develop Travel Risk In Pandemics (TRIP-X), a modular, simulation-based tool estimating disease transmission risk in air travel. This commentary describes how TRIP-X filled GAO-identified gaps and highlights procedural enablers and challenges of multinational, intergovernmental, and industry collaboration: shared problem framing, iterative development, stakeholder engagement, and cross disciplinary trust building. TRIP-X offers a reproducible, operational decision support platform for evaluating mitigation strategies and informing policy. TRIP-X produces FAA-owned infection risk estimates intended to support Safety Risk Management, policy development, and aviation pandemic preparedness.]]></description>
      <pubDate>Mon, 30 Mar 2026 08:55:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683243</guid>
    </item>
    <item>
      <title>Analysis on effectiveness of subway epidemic prevention measures considering subway passenger flow</title>
      <link>https://trid.trb.org/View/2604564</link>
      <description><![CDATA[Since its discovery in 2019, the characteristics of COVID-19 have also changed, increasing pressure on subway authorities to control the spread of the disease. Most scientific contributions discussed the correlation between the movement of people in the subway and the spread of the virus, and provided ways to control the spread of the virus in the subway. However, in order to balance production and safety under the easing of the epidemic, it is necessary to establish a relatively common method to evaluate the effectiveness of subway epidemic prevention management measures. This paper investigates subway ridership data after the epidemic has eased, taking the subway of Z city as the case study. SEIA model, adapted from the SEIR (susceptible-exposed-infected-recovered) framework, was developed. The model divides infected people into symptomatic infections and asymptomatic infections, and simulates the movement of subway passengers to show the spread of COVID-19 in subways. In this study, the SEIA model was used to simulate the results of the three protection measures implemented by the subway management department of Z city, namely social distancing, reducing station stops and vaccination. The results showed that the measures of vaccination, while the measures of reducing station stops were the least effective.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604564</guid>
    </item>
    <item>
      <title>A comparison of HIV pre-exposure prophylaxis accessibility by public transit- and drive-time in Dallas-Fort Worth, Texas, 2024</title>
      <link>https://trid.trb.org/View/2601909</link>
      <description><![CDATA[This article reports on a study that compared HIV pre-exposure prophylaxis accessibility by public transit- and drive-time in Dallas-Fort Worth, Texas, 2024.  The authors found that disparities exist between public transit- and driving-based HIV PrEP accessibility. Bayesian spatial modeling is used to link transit-based PrEP accessibility and SDOH. Residents in areas with higher new HIV diagnosis rates or socioeconomic deprivation have better PrEP accessibility by transit. Residents in areas with higher percentages of Black or Hispanic populations have lower PrEP accessibility by transit.]]></description>
      <pubDate>Tue, 18 Nov 2025 09:30:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601909</guid>
    </item>
    <item>
      <title>How do fears influence public transport behaviors during the peak of the pandemic?</title>
      <link>https://trid.trb.org/View/2604995</link>
      <description><![CDATA[The peak of the COVID-19 pandemic, marked by the rapid spread of the highly contagious Delta variant, presents a paradox: public transport activity not only persisted but increased, contrary to expectations of decline. This study investigates how individuals’ transport behaviors shifted and what factors drove these changes in response to fears of COVID-19 and financial instability. To address this question, the authors examined public transport behaviors from a novel perspective by introducing two dependent variables: changes in individuals’ actual use of public transport and their short-term transport plans. The theoretical framework draws on the fear appeal theory, incorporating two primary independent variables—fears for health (COVID-19 related) and fears for financial conditions (pandemic-induced). Logistic regression analysis was conducted using data from the U.S. Census Bureau’s Household Pulse Survey, collected between June and July 2021. The results demonstrate significant impacts of both health-related and financial fears on public transport behaviors. An important insight is that risk-related pressures during the Delta wave were dynamic over short periods: indicators of health risk and pandemic-related financial insecurity predicted recent transit use differently than they predicted short-term trip planning. The authors interpret this pattern through a fear-appeal informed lens, treating variables as contextual proxies for threat exposure and resource constraints rather than direct measures of emotional states. To capture this dynamic, the authors introduce the concept of “threat (fear) salience transition,” which underscores the shifting nature of perceived risks. These findings offer practical implications for transit policy, particularly in managing evolving public responses under simultaneous health and financial pressures.]]></description>
      <pubDate>Tue, 18 Nov 2025 09:30:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604995</guid>
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    <item>
      <title>No fare, no fear? Exploring the rationales and actors behind the rise of fare-free public transport during the COVID-19 pandemic in the United States</title>
      <link>https://trid.trb.org/View/2612155</link>
      <description><![CDATA[The COVID-19 pandemic prompted significant changes in public transport (PT) policies, with fare-free public transport (FFPT) emerging as a widely adopted measure in the United States. While FFPT was previously limited to specific contexts, such as college towns and small urban communities, the pandemic spurred its implementation across 259 transit agencies. This study investigates the factors driving this shift, examining the role of decision-makers, knowledge dissemination, and underlying rationales for fare suspension. Drawing on interviews with transit officials, the findings confirm that prior familiarity with FFPT, amplified by formal and informal knowledge-sharing networks, played a critical role in its widespread adoption. Key justifications for fare suspension included ensuring physical distancing and providing financial relief to social groups dependent on public transport, particularly essential workers and low-income riders. Federal COVID-19 relief funding, such as the CARES Act, was instrumental in enabling transit agencies to offset fare revenue losses and sustain operations. Despite initial unanimous support for FFPT, the study highlights emerging debates about its long-term feasibility, with equity and financial considerations converging in several FFPT cases. The analysis contributes to the broader discourse on policy change, illustrating how crises can accelerate the adoption of policies already circulating within knowledge networks. By unpacking the interplay of actors, rationales, and financial mechanisms, the study offers insights into the dynamics of policy mobility and the future prospects of FFPT in urban public transport systems.]]></description>
      <pubDate>Tue, 18 Nov 2025 09:30:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612155</guid>
    </item>
    <item>
      <title>Risk vs. Benefit Analysis of Ultraviolet-C Advanced Aircraft Disinfection</title>
      <link>https://trid.trb.org/View/2611119</link>
      <description><![CDATA[This work provides details and references that help to quantify the benefits of using ultraviolet-c (Uv-c) light for air disinfection in aircraft vs. the risk of overexposure to Uv-c for passengers and crew. the analysis estimates that due to the combined transmission of Severe Acute Respiratory Syndrome coronavirus 2 and influenza A aboard commercial aircraft in the United States over the 3 yr through May 2023, there were on the order of 10,000 annual deaths, declining to 3,000/yr going forward, with an estimated annual economic burden of $200 billion. Up to 80% of the deaths and economic burden might be saved by supplementing the typical 30 air changes per hour of the aircraft ventilation system with a presently available 120 air changes per hour, using a Uv-c disinfection system. The risks due to accidental overexposure to Uv-c are orders of magnitude lower than the benefits. The 0.00003% risk of acute (one-time) overexposure for any given passenger may (or may not) result in a 1–2-day skin or eye irritation, with no long-term effects or risks, compared to the 15,000 times greater risk, at 0.5%, of contracting coronavirus disease 19 or influenza A that persists for several days to weeks, and carries a risk of hospitalization or death. The estimated risk of non-melanoma skin cancer is virtually nil.]]></description>
      <pubDate>Tue, 21 Oct 2025 10:29:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611119</guid>
    </item>
    <item>
      <title>LSTM model for high-risk COVID-19 transmission among Thailand’s mass rapid transit purple line passengers</title>
      <link>https://trid.trb.org/View/2594500</link>
      <description><![CDATA[Modern epidemiological research increasingly integrates machine learning and data-driven methods to enhance the prediction and mitigation of COVID-19 and other respiratory virus outbreaks. Herein, a long short-term memory (LSTM)-based classification model was developed to predict high-risk COVID-19 transmission zones on Thailand’s Mass Rapid Transit Purple Line platforms. Using sequential passenger flow data and temporal patterns, platform areas were classified into low- and high-risk areas based on key inputs including station, date, time, and crowd density. Hyperparameter optimization using RandomizedSearchCV yielded an optimal configuration of 64 LSTM units, a learning rate of 0.001, a batch size of 32, and 30 epochs. The model achieved 98% test accuracy, 98.22% cross-validation accuracy, and 99.11% peak validation accuracy. For high-risk detection, it obtained precision and recall of 0.95 and 0.96, respectively. The results highlight the robustness and real-time applicability of the approach in urban transit systems. The findings offer actionable insights for targeted interventions such as dynamic crowd management and optimized resource allocation, thereby reducing exposure risks and strengthening preparedness for future public health crises.]]></description>
      <pubDate>Mon, 13 Oct 2025 13:52:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594500</guid>
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