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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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    <item>
      <title>Approaches to handle disruption effects in transport infrastructure and logistics networks</title>
      <link>https://trid.trb.org/View/2683014</link>
      <description><![CDATA[Disruptions to transport infrastructure and logistics networks caused by extreme weather, geopolitical crises, pandemics and other shocks are expected to intensify in the future. This paper examines how stakeholders currently handle such disruptions and to what extent sustainability is integrated into these approaches. Drawing on a mixed-methods study comprising approximately 50 survey responses and 30 semi-structured interviews across multiple European countries we identify three main categories of handling approaches: information-, technology- and collaboration-based measures. Contrary to assumptions in much of the resilience literature, our findings show that stakeholders use hybrid and cross-phase approaches that cut across preparedness, robustness, recovery and adaptive phases, rather than disruption- or phase-specific strategies. While stakeholders employ a wide repertoire of handling approaches, environmental sustainability is generally deprioritized during disruptions, with continuity of service and safety taking precedence. Sustainability considerations are mainly integrated in long-term planning rather than in acute response. The results highlight the need to embed environmental goals more firmly into everyday operations and preparedness planning to strengthen alignment between resilience and sustainability. The study advances resilience research by providing an actor-oriented, multi-stakeholder analysis of how disruption handling unfolds in practice and where current approaches fall short in supporting sustainable transition pathways.]]></description>
      <pubDate>Mon, 06 Jul 2026 15:58:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683014</guid>
    </item>
    <item>
      <title>Health services use for transport injuries among children and youth in Ontario from 2015–2022: a population-based study</title>
      <link>https://trid.trb.org/View/2720255</link>
      <description><![CDATA[Transportation-related injuries remain one of the leading causes of mortality among children and youth in Canada. Factors such as age, sex, marginalization, and the COVID-19 pandemic may influence children’s interactions with their environment and their mobility patterns. The objectives of this study were to describe: (1) the incidence of transport-related emergency department (ED) visits and hospitalizations in Ontario by type (motor vehicle, pedestrian, cycling) and by age and sex; and (2) the temporal trends in transport-related health service utilization by type and level of marginalization over time, including during the COVID-19 pandemic. Data for all traffic and non-traffic motor vehicle and vulnerable road user injuries (VRU, pedestrians and cyclists) were obtained for ED visits and hospitalizations in Ontario from January 2015 to March 2022. Descriptive analyses were completed by age group, sex, and marginalization across the study period. A simulation approach using Bayesian Poisson regression was employed to examine how the pandemic affected temporal trends. During the study period, the rate per 10 000 children and youth with motor vehicle-related injuries was 328 (95% CI: 325–331), and for VRU-related injuries was 275 (95% CI: 272.4–277). Sixty-one percent of cyclist ED visits and hospitalizations were non-traffic related. Males, children and youth aged 10–19 and more marginalized children generally had higher rates of both ED visits and hospitalizations than females, children aged 0–9 and those less marginalized. At the onset of the pandemic, ED visits for traffic-related motor vehicle and all pedestrians were lower than expected, and non-traffic motor vehicle and all cyclists were higher than expected. The greatest differences from expected in ED visits were in the least marginalized children; for example, there was a 107% increase in cyclist non-traffic in the least marginalized versus 11% increase in the most marginalized quintile. The findings of this study reinforce the ongoing need to focus on cycling safety, particularly non-traffic-related, for children and youth. These findings can also inform future equitable injury preventive efforts in light of significant population-level events, such as pandemics, that might change children’s mobility patterns.]]></description>
      <pubDate>Mon, 06 Jul 2026 08:54:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720255</guid>
    </item>
    <item>
      <title>Pandemic-induced uncertainty and maritime terrorism: A quantile-on-quantile analysis of major maritime economies</title>
      <link>https://trid.trb.org/View/2680188</link>
      <description><![CDATA[Pandemic-induced uncertainty reshaped global dynamics, creating critical vulnerabilities in maritime security and intensifying terrorism threats at sea. Supply chain disruptions, weakened economies, and strained international cooperation further compounded the complexity and reach of maritime terrorism, amplifying its global implications. This study investigates the asymmetric impact of pandemic-induced uncertainty on maritime terrorism in 10 selected maritime nations (the USA, China, Somalia, Singapore, Brazil, India, Greece, Nigeria, Australia, and Russia). Unlike past investigations that focused entirely on COVID-19, the present study applies a comprehensive pandemic-related uncertainty index, integrating datasets from various pandemics, including Avian Flu, Ebola, MERS, COVID-19, SARS, and others. The Quantile-on-Quantile technique is applied instead of traditional panel data methods, which often overlook country-specific contexts. This advanced instrument enables an exhaustive analysis of the asymmetric linkage between variables within individual nations. Results reveal an inverse connection between pandemic uncertainty and maritime terrorism in the USA, China, Singapore, and Australia. Meanwhile, Somalia, Brazil, India, and Nigeria show a positive association. However, mixed findings emerge in Russia and Greece, reflecting complex and varied dynamics. These findings underscore the critical need for policymakers to formulate customized policies to monitor the evolving impact of pandemic uncertainty on maritime terrorism, which varies across different quantiles.]]></description>
      <pubDate>Thu, 25 Jun 2026 09:40:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680188</guid>
    </item>
    <item>
      <title>Integrated Risk Assessment Framework for Non-Compliance with Liner Shipping Schedules</title>
      <link>https://trid.trb.org/View/2694417</link>
      <description><![CDATA[The article discusses the problem of assessing the risks of non-compliance with liner shipping schedules, which is a key factor in the efficiency and reliability of international trade. Given the complexity and diversity of factors affecting schedule disruptions, ranging from port congestion and terminal productivity to the disruptive impact of external force majeure accidents (pandemics, storms, hurricanes, wars, etc.). The study proposes a methodology based on formal conceptual analysis (FCA). This approach allows for a structural risk assessment by grouping factors according to liner service ports of call, assigning weights to them, and calculating their integrated impact on the overall reliability of the schedule. The proposed approach is illustrated with a numerical model that demonstrates how changes in weighting factors can affect the final risk assessment. The results contribute to the development of theoretical approaches to risk management in shipping, while offering practical tools for reducing schedule disruptions in the LS-industry.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694417</guid>
    </item>
    <item>
      <title>Data-driven distributionally robust optimization for resilient healthcare resource planning and crisis mitigation</title>
      <link>https://trid.trb.org/View/2614785</link>
      <description><![CDATA[Healthcare systems face significant challenges during public health emergencies, particularly in efficiently allocating limited resources while maintaining service quality under uncertainty. This paper addresses the critical issue of resource allocation during pandemics, focusing on facility location, capacity planning, and workforce management under demand uncertainty and operational constraints. Two distributionally robust optimization (DRO) models are developed, incorporating the L1-norm and the joint L1- and L∞-norm ambiguity sets to capture uncertainty. To enhance robustness, the Conditional Value-at-Risk (CVaR) criterion is employed to more accurately account for some of the worst realizations of random future demand scenarios. The models incorporate the strategic deployment of alternate care sites, multi-category patient demand with heterogeneous service requirements, and allocation of different resources, including general and intensive care unit (ICU) beds, ventilators, and healthcare personnel. Workforce planning is enhanced by considering cross-training and staff mobility. Furthermore, varying penalty costs for unmet demand across patient categories ensure priority of critical care needs. The models are validated through a case study focused on the COVID-19 pandemic in Southern California, demonstrating superior stability and robustness in out-of-sample testing compared to the traditional stochastic programming approach. The findings provide valuable insights for healthcare administrators in designing resilient and efficient healthcare logistics networks during public health emergencies.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614785</guid>
    </item>
    <item>
      <title>Mobility resilience: A social welfare measure of mobility systems under disruption scenarios</title>
      <link>https://trid.trb.org/View/2687030</link>
      <description><![CDATA[Transportation systems today continue to be designed and planned with an implicit assumption of a stable and predictable future. This conventional “predict-and-provide” approach does not account for the frequent and often unpredictable disruptions like extreme weather, pandemics, cultural events, or broader societal shifts that increasingly affect mobility. In direct alignment with the UN’s eleventh Sustainable Development Goal, transportation systems must integrate resilience as a fundamental component to maintain their functionality and sustain their core societal responsibility – generating social welfare – during disruptions. However, current definitions and operationalizations of resilience remain predominantly rooted either in engineering and technical perspectives or qualitative assessments in socio-ecological dimensions. This research contributes a novel perspective on transportation resilience by developing a methodology to quantify and monetize resilience in terms of social welfare. Mobility resilience captures the transportation system’s capacity to sustain societal welfare and environmental sustainability as a function of the operators’ costs and revenues, users’ utility of activities and travel, and social external costs. This methodology is implemented using agent-based modeling based on empirical data to evaluate the performance of private cars, public transportation, and shared autonomous vehicles during an extreme weather scenario, a large-scale cultural event, a public transportation labor strike, and a pandemic in Hamburg, Germany. These scenarios assess the different transportation systems with their unique characteristics regarding their capacity to improve social welfare, resilience, and recovery. By bridging technical robustness and socio-economic sustainability, this research’s contributions enable stakeholders to create resilient transportation systems that remain socially effective during disruptions.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:18:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687030</guid>
    </item>
    <item>
      <title>The Complexity of Adaptation Strategies for Maritime Transport</title>
      <link>https://trid.trb.org/View/2670800</link>
      <description><![CDATA[This chapter discusses the complexity of adaptation strategies for maritime transport. Maritime transport systems face increasing complexity in adapting to global challenges, including climate change, pandemics, and geopolitical tensions like war. It examines the adaptation strategies of UK ports, focusing on specific climate risks, including rising sea levels, extreme weather, and shifting trade patterns, and corresponding resilience measures, such as coastal defences, green technologies, and flexible port management. It also explores public responses to logistical needs during global disruptions like the COVID-19 pandemic and war outbreaks, highlighting evolving demands for essential supplies and reconstruction logistics. This chapter addresses the complexities of climate and global events and provides actionable insights to sustain maritime operations and supply chains in an increasingly uncertain world.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670800</guid>
    </item>
    <item>
      <title>Communicable Disease Preparedness: Modelling and Simulation Framework for Analyzing Cabin Health Hazards: Data Management Plan</title>
      <link>https://trid.trb.org/View/2683245</link>
      <description><![CDATA[The most recent pandemic exposed critical gaps in the aviation system’s ability to assess and manage communicable disease risks within the Safety Management System (SMS) framework. In response, a multi-year research effort developed the prototype, open-source Travel Risk In Pandemics (TRIP-X) simulation model to extend FAA Order 8040.4 Safety Risk Management principles to respiratory and biological hazards in air travel. TRIP-X integrates ventilation, human behavior, operational, and microbiological factors using airport and aircraft data to quantify transmission risk, evaluate layered controls, and support evidence-based decision-making. Beyond pandemic preparedness, TRIP-X enables tailored protective strategies for repatriation and humanitarian operations—such as safely evacuating personnel during Ebola or other severe outbreaks without imposing unnecessary restrictions—while also optimizing outbreak and epidemic management to balance public-health protection with the continuity of air travel, commerce, and societal mobility. Additionally, it supports homeland security and threat preparedness by providing a framework for structured threat and vulnerability analyses of intentional biological releases, strengthening aviation safety, public-health coordination, and national defense readiness under a unified risk-management approach.]]></description>
      <pubDate>Mon, 30 Mar 2026 08:55:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683245</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>Modelling trip scheduling decisions of bus commuters amid disruptive events using smart card data</title>
      <link>https://trid.trb.org/View/2679123</link>
      <description><![CDATA[Departure time models are key tools for understanding time-varying travel demand. Nonetheless, there is limited research focusing on the analysis of trip scheduling decisions in the context of public transport users. In particular, research on how public transport users adapt departure times when the activity and travel landscape are altered as a consequence of disruptive events (e.g. pandemics, social unrest), is yet to be conducted. Smart card data, which passively records time-stamped departure locations of public transport users, offers the opportunity to investigate such shifts in detail but is yet to be utilised. The paper aims to address these two gaps by using smart card data to investigate the trip scheduling decisions of bus commuters amid disruptive events. This goal is achieved by estimating departure time choice models (DTCMs) for characteristic episodes between 2019 and 2022 for Santiago's bus system, a city affected to different degrees by two types of disruptive events within this timeframe: the COVID-19 pandemic and social unrest. The paper addresses the methodological challenges of calculating schedule delay with smart card data by estimating preferred arrival times as a random variable within a mixed multinomial logit model. The approach is assessed through the valuation of the trade-off between travel time and schedule delay (TVSD), with the results falling within the range of values previously reported in the literature. The model results highlight the existence of multi-temporal differences in the arrival time preferences of bus commuters, as well as in their TVSD amid disruptive events. It was found that bus commuters were less willing to accept an increase in their travel time to reduce their schedule delay during disruptive episodes. The heterogeneity between bus travellers was also explored: recurrent bus commuters exhibited higher TVSDs than occasional commuters. The outcome of this study supports using smart card data as a feasible source to investigate how public transport passengers allocate their trip scheduling both during normal periods and amid external disruptions.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679123</guid>
    </item>
    <item>
      <title>H²DGL: Adaptive Metapath-Based Dynamic Graph Learning for Supply Forecasting in Logistics System</title>
      <link>https://trid.trb.org/View/2617705</link>
      <description><![CDATA[The advanced logistics systems are increasingly transitioning towards integrated warehousing and distribution supply networks (IWDSN), where accurately forecasting supply capacity is essential for maintaining delivery capabilities that meet user demands. However, existing research often overlooks the impact of dynamic changes in network topology, resulting in limitations in capturing dynamic routing and diverse node responses. These limitations become particularly pronounced in the context of external events such as pandemics, heavy rain, and promotions. To address the above limitations, we propose  $\mathtt {H^{2}DGL}$ , a Hierarchical Heterogeneous Dynamic Graph Learning framework based on adaptive metapath aggregation, for forecasting supply capabilities in logistics systems. Specifically,  $\mathtt {H^{2}DGL}$  comprises three main modules: (1) Hierarchical Heterogeneous Node Representation, where the micro graph captures dynamic routing information through adaptive meta-path aggregation from routing and event view graphs, and the macro graph extracts spatial representations using bipartite graph learning. (2) The Dynamic Graph Encoding module integrates macro and micro features from different snapshots to derive unified node representations. (3) The Spatio-temporal Joint Forecasting combines spatial features with temporal features from a time-series encoder to predict future supply capacity. Extensive experiments on two real-world datasets from different cities demonstrate that  $\mathtt {H^{2}DGL}$  achieves state-of-the-art performance compared to advanced baseline models. The code is available at https://github.com/kaiwxai/H2DGL]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617705</guid>
    </item>
    <item>
      <title>Modeling the cancellation of inner-city leisure trips to different destinations in future pandemics: employing psychological factors based on previous personal experiences</title>
      <link>https://trid.trb.org/View/2643198</link>
      <description><![CDATA[As pandemics can be classified as crises, it is essential to develop predictions for future pandemic situations. Previous research focusing on the impact of pandemics on travel behavior was primarily conducted during crises. However, individuals may react differently to similar pandemic situations in the future based on their knowledge, experience and attitudes. Considering a hypothetical future pandemic, this paper investigates the factors that influence whether people would continue their inner-city leisure activities or cancel them. A stated preference survey, incorporating psychological and socioeconomic variables, was designed to examine individuals' travel behavior for various leisure destinations. To identify the impact of specific destinations on decision-making, a general model was first developed, followed by four binary logit models for indoor public, outdoor public and private leisure destinations. We found that psychological factors, especially personal concern about health risks, social responsibility and value-driven beliefs like altruism, play a major role in shaping people's choices. Those who strongly believed in protecting others or saw COVID-19 as a serious threat were much more likely to cancel their leisure trips. These findings may shed light on the existing literature and assist city managers in making better decisions regarding the operation of leisure destinations during future pandemics.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643198</guid>
    </item>
    <item>
      <title>Resilient supply chain network design under super-disruption considering inter-arrival time dependency: a new data-driven stochastic optimization approach</title>
      <link>https://trid.trb.org/View/2649626</link>
      <description><![CDATA[During large-scale disruptions, particularly super-disruptions such as global pandemics or large-scale natural disasters, supply chains are exposed to significant adverse impacts. This paper addresses the resilience in a supply chain network design problem under disruption risk by explicitly modeling the dependency between the inter-arrival times of disruptive events and severity of their consequences. A novel data-driven stochastic optimization framework is proposed to consider the ripple effects that typically propagate across supply chain networks following severe disruptions. Specifically, we have devised a hybrid methodology that integrates a clustering algorithm (unsupervised machine learning technique), a phase-type disruption model, and a two-stage stochastic model. To elaborate, a genetic-based clustering algorithm is used to identify the structure dependencies in the input data. Phase-type distributions and their associated theorems are then used to determine the probability distributions of disruptions. A novel mathematical model is developed to design the supply chain using the scenarios generated based on the obtained distributions, which is then solved using the Lagrangian decomposition combined with a new hyper-matheuristic algorithm. The computational efficiency and practical value of the proposed approach are demonstrated through a real-world case study. The findings highlight the effectiveness of developed methodology in designing a resilient supply chain, the proposed resilience strategies substantially improve the supply chain’s performance compared to a non-resilient approach.]]></description>
      <pubDate>Thu, 19 Feb 2026 10:53:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2649626</guid>
    </item>
    <item>
      <title>An Impact Assessment of Cordon Pricing Relaxation on Modal Shift During the COVID-19 Pandemic</title>
      <link>https://trid.trb.org/View/2627360</link>
      <description><![CDATA[The COVID-19 pandemic brought significant disruptions to transportation patterns worldwide, with a notable increase in Private Vehicle (PV) usage. Many studies have focused on travel restrictions, while neglecting to consider potential changes in transportation-related policies implemented during COVID-19 that may have improved public health. The present study explores the motivations for modal shifts to PVs and tries to analyze which of the risks of exposure and modifications in transportation policies has caused these modifications. Focusing on the case study of Tehran, Iran, where cordon pricing was relaxed during the pandemic, the study collected data through online and paper-based surveys from 1475 respondents. Binary logit models were employed to analyze the data and understand the impact of destination location, residency area, trip purpose, trip frequency, and vehicle characteristics on modal shift behavior. In addition to COVID-19 exposure as a primary reason for the modal shift, respondents also identified the relaxation of cordon pricing restrictions as a factor that increased the utility of PVs. The study's findings contribute to better understanding the dynamics of travel behavior during pandemics, guiding policymakers in devising effective strategies to address both public health concerns and traffic conditions.]]></description>
      <pubDate>Thu, 05 Feb 2026 16:39:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627360</guid>
    </item>
    <item>
      <title>Carryover Effects of Covid-19 on Precautionary Behaviours during Similar Future Pandemics</title>
      <link>https://trid.trb.org/View/2628406</link>
      <description><![CDATA[The behaviour of the public during pandemics may be driven by past pandemic experiences. For instance, individuals’ precautionary behaviour during a future pandemic may be influenced by their experiences during the COVID-19 pandemic. The objective of this study was to investigate the intentions of individuals to adopt precautionary measures during a future pandemic similar to COVID-19. More specifically, it aimed to determine the lingering impacts of the COVID-19 pandemic on the intention to get vaccinated, follow precautionary measures, and use public transport during a future similar pandemic situation. A questionnaire survey was conducted in Lahore, Pakistan, in August 2022 that yielded 904 responses. The intentions to get vaccinated and use public transport during a future similar pandemic were modelled using binary logistic regression, whereas the intentions to follow precautionary measures were modelled using linear regression. The results indicated that individuals experiencing the negative carryover effects of COVID-19 precautionary measures were more likely to be vaccinated. Those believing COVID-19 to be an exaggerated threat were less likely to be vaccinated and follow precautionary measures. Further, males and married people were more likely to be vaccinated and use public transport during the future pandemic. Moreover, individuals who experienced the negative carryover effects of COVID-19 precautionary measures and believed COVID-19 to be an exaggerated threat tended to use public transport more often during the future pandemic. The findings could be useful for planning agencies to understand how the carryover effects of a past pandemic may affect public behaviour during a potential future pandemic.]]></description>
      <pubDate>Thu, 05 Feb 2026 11:52:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628406</guid>
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