<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/Record/RSS?s=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" 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>Uncovering spatiotemporal coupling of electricity outages and food access disruptions during disasters</title>
      <link>https://trid.trb.org/View/2689450</link>
      <description><![CDATA[Disaster-induced power outages create cascading disruptions across urban lifelines, yet the timed coupling between grid failures and essential service access remains poorly understood. Focusing on Hurricane Beryl in Houston (2024), this study integrates 173,000 15-minute outage records with 1.25 million revealed visits to 3,187 food facilities across 140 ZIP Code Tabulation Areas to quantify how infrastructure performance and human access co-evolve. We construct daily outage and food-access indices, estimate cross-system lags using lagged correlations, and identify recovery patterns with Dynamic Time Warping k-means clustering. Results reveal a consistent two-day lag: food access reaches its nadir on July 8, while outage severity peaks on July 10, with correlations strongest at a two-day delay. Overlaying outage and access clusters produces four compound typologies, showing road network sparsity drives persistent access loss. We identify 294 critical food facilities requiring targeted continuity measures. The framework offers a generalizable template for diagnosing cascading disruptions and informing recovery prioritization.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:37:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689450</guid>
    </item>
    <item>
      <title>Restoring the Princess: A Case Study in Disaster Response, Recovery, and Resilience from the Sint Maarten Airport Terminal Reconstruction</title>
      <link>https://trid.trb.org/View/2676900</link>
      <description><![CDATA[The reconstruction of Princess Juliana International Airport (PJIA) in Sint Maarten represents one of the most comprehensive airport disaster recovery efforts in recent history. When Category 5 Hurricanes Irma and Maria devastated the terminal in September 2017, they threatened the economic lifeline of the entire island. Seven years later, in November 2024, the terminal reopened as a stronger, more efficient, and more customer-friendly version of itself. The story of PJIA’s reconstruction exemplifies how institutional leadership, effective stakeholder engagement, adherence to international standards and cooperation, and unwavering commitment can transform catastrophic setback into opportunity. This case study examines the Princess’s complex journey from destruction to renewal, offering critical insights for airport owners, operators, and regulators in disaster-prone regions and small island states facing similar vulnerabilities in an era of intensifying hurricane risks.]]></description>
      <pubDate>Mon, 16 Mar 2026 08:41:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676900</guid>
    </item>
    <item>
      <title>Transferring Household Evacuation Choice Behavioral Models to Create a Digital Twin for Future Storm Responses: Opportunities and Challenges</title>
      <link>https://trid.trb.org/View/2672024</link>
      <description><![CDATA[This research studied whether and how hurricane evacuation behavioral models, estimated with survey data collected from previous storms, could be used in predicting evacuation patterns in a new storm setting (with an anticipation of being able to conduct real-time simulation in the future). With publicly available data, this study first created synthetic populations for the study region (i.e., New Orleans, Louisiana) by year. Findings from this process show that: 1) simulating evacuation behavior can only be done for storms that have occurred between 2013 and two years back from the current year; and 2) it might not be appropriate to use population data of a different year to simulate evacuation behavior in a current storm year owing to population migration. With synthetic populations created for 2021, this study simulated household hurricane evacuation-related choices in Hurricane Ida with behavioral models estimated before, which facilitates discussions about model transferability. It was found that lognormal distance function parameters in the evacuate/stay and departure timing joint choice model, and destination risk perception values in the destination choice model are the two most critical factors that need to be updated. Both factor updates are related to storm characteristics and can be completed with live storm feeds, which indicates real-time data input is indispensable in improving prediction accuracy. This study highlights data challenges for real-time simulation, helps improve the usefulness of estimated statistical models in practical applications, and emphasizes the importance of considering human components (including demographic profiles and choice behavior) in creating digital twins.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672024</guid>
    </item>
    <item>
      <title>Investigating Traffic Resilience and Clearance Time in Hurricane Evacuations: The Role of Population, Geographic Factors, and Evacuation Corridors</title>
      <link>https://trid.trb.org/View/2669559</link>
      <description><![CDATA[This study investigates the determinants of traffic resilience and clearance time (CT) during regional hurricane evacuations, addressing the limited empirical understanding of how evacuation efficiency evolves across multiple disasters. Despite extensive simulation-based studies, few have validated evacuation dynamics using large-scale observed traffic data. To bridge this gap, we analyze real evacuation traffic records from Hurricanes Irma (2017) and Ian (2022) in Florida, U.S., and develop a negative exponential decay model to quantify the relationship between CT and lane capacity. Results show that CT decreases at an accelerating rate as lane capacity increases, with higher decay rates indicating more efficient evacuations. Notably, the increase in decay rates over this period suggests an improvement in traffic resilience. Geographic factors play a crucial role in shaping both CT and resilience—regions with higher populations, low road network density, and limited evacuation corridors experience prolonged evacuation times. Expanding corridor capacity proves to be a key strategy in accelerating evacuations, underscoring its significance in strengthening traffic resilience. These findings provide critical insights for policymakers and emergency planners, offering data-driven strategies to optimize evacuation planning and enhance the resilience of transportation systems against hurricanes and other natural disasters.]]></description>
      <pubDate>Tue, 17 Feb 2026 09:19:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669559</guid>
    </item>
    <item>
      <title>South Louisiana River and Coastal Ports: Lessons Learned from Hurricane Katrina</title>
      <link>https://trid.trb.org/View/2263846</link>
      <description><![CDATA[The authors conducted a weeklong field assessment of damage to selected South Louisiana river and coastal port facilities resulting from the Hurricane Katrina event of August 29, 2005. Based on review of available data and internal team discussions, the assessment focused on four river ports and three coastal ports believed to have received damage from the event. The field assessment, consisting of site observations and discussions with port or terminal facility personnel, was conducted from October 17–21, 2005 — seven weeks after the event. The assessment focused on preparation, response, and recovery activities of the affected ports, with an eye toward identifying "lessons learned" that could be beneficially applied or implemented to mitigate impacts from future such events. The key lessons learned include: (1) communications were inhibited by lack of reliable back-up systems to maintain reliable, open channels; (2) stockpiling of emergency materials, such as fuel for standby power generation, is a necessary measure; (3) locating critical equipment, such as communications, controls, and power supply, above flooding levels to maximize survival; and (4) providing organized evacuation policies and procedures for port staff, including post-event communications and logistics to enable quicker recovery of operations.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:39:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2263846</guid>
    </item>
    <item>
      <title>Lessons in Bridge Vulnerability from Hurricane Katrina: Reconnaissance Findings and Analysis of Empirical Data</title>
      <link>https://trid.trb.org/View/2263824</link>
      <description><![CDATA[Hurricane Katrina caused significant damage to the bridges in the Gulf Coast region of the United States, including Louisiana, Mississippi, and Alabama. Damage to these key components of the transportation system can cause significant economic losses and impede post-event recovery and restoration. The overall cost to repair or replace the 44 bridges damaged during Katrina, including emergency repairs, was estimated at well over $1 billion. The paper summarized the findings of reconnaissance along the Gulf Coast by analyzing the observed damage patterns to bridges, including storm surge, wind, and impact damage. These bridges suffered span shifting and unseating, yielded piers, spalling of bent beam, shearing of bearings, and damage to electrical and mechanical equipment, among others. Common features of the severely damaged bridges include low elevation, limited connectivity between superstructure and substructure, and simply supported spans. Potential improved design details and retrofit measures will be presented, drawing upon lessons from Katrina. Nationwide risk assessment packages for lifeline systems lack any reliable input models of bridge fragility to assess the risk to the transportation infrastructure posed by hurricane induced storm surge. As a first step toward the development and validation of quantitative models of bridge reliability under hurricane induced storm surge and wave loading, evidence from the 2005 Hurricane Katrina is used to evaluate hazard intensity measures and bridge characteristics indicative of increasing level of bridge damage. This information can be used for binning of bridges and identification of viable intensity measures for fragility analysis.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:39:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2263824</guid>
    </item>
    <item>
      <title>Flood Risk in Georgia: A GIS Analysis on the Impact on Transportation Infrastructure</title>
      <link>https://trid.trb.org/View/2562095</link>
      <description><![CDATA[Global climate change and rising sea levels intensify the number and strength of extreme weather events, which put coastal regions at high risk for infrastructure loss. Devastating meteorological events, like hurricanes, hitting the east coast of the United States increases the risk for inland pluvial and fluvial flooding, which impacts emergency transportation routes and recovery efforts. This study uses Geographic Information Systems (GIS) to assess Georgia’s current and future flood vulnerability, focusing on critical transportation networks during extreme weather events. Three coastal populations were selected, Savannah, Brunswick, and Kingsland, and current floodplains were mapped, and future changes projected. Additionally, it analyzes how local topography, land use, and storm surge patterns contribute to flood susceptibility and the impact on key transportation routes at two inland hotspots, Dublin and Lumber City. During the analysis, US Highway 80 and Highway 341 infrastructure was identified, as locations that could benefit from adaptation measures, including rerouting evacuation paths, flood barriers, and infrastructure improvements, ensuring public safety and transportation resilience during emergencies. These findings can also be used to plan recovery efforts.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562095</guid>
    </item>
    <item>
      <title>Vulnerability assessment of electric vehicles and their charging station network during evacuations</title>
      <link>https://trid.trb.org/View/2652310</link>
      <description><![CDATA[Electric vehicle (EV) drivers face range anxiety and long recharging times and navigate sparse public charging networks, which challenge both preemptive and short-notice evacuations. We propose a multi-criteria vulnerability assessment of the coupled EV driver and charging station network during evacuations. We study flooding evacuations in Chicago, IL and hurricane evacuations in Southeast Florida, FL. The sensitivity analysis is conducted to examine the effects of initial battery state of charge (SOC), reduced battery efficiency under adverse weather, EV penetration rates, and home charging accessibility. Our findings show the impact of vehicle and infrastructure-related (charging network, driving range and vehicle heterogeneity) and evacuation-related (network properties, hazard intensity, and warning system type) characteristics to evacuation feasibility and performance. Most EV drivers can evacuate with or without charging during mild and moderate hazards, even with the expected decrease in charging station accessibility and network disruptions. During rare and severe hazards, those with short-range EVs face a higher risk of getting stranded without enough power and reduced charging infrastructure access. The initial SOC of the EV battery determines drivers’ capability to initiate an evacuation.]]></description>
      <pubDate>Tue, 27 Jan 2026 09:19:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652310</guid>
    </item>
    <item>
      <title>Characterizing performance resilience of transportation networks against hurricane events</title>
      <link>https://trid.trb.org/View/2613747</link>
      <description><![CDATA[Extreme weather events are posing significant challenges to transportation infrastructure networks, both physically and functionally. While previous studies have examined the performance of infrastructure networks against disruptions, rare research integrates segment-level performance metrics, such as traffic volume and speed, to evaluate spatiotemporal operational responses to climate-disruptive events, like hurricanes. This study highlighted multiple traffic segments in transportation networks and investigated their geospatial changes in average traffic volume and median traffic speed before, during, and after hurricanes to quantify segment-level volume and speed resilience. Analyzing highway networks’ traffic and hurricane data from Miami-Dade County, Florida, we revealed four-quadrant performance resilience patterns, including (1) negative volume, positive speed (80 % of the highway networks); (2) both negative (17 %); (3) both positive (0.6 %); and (4) positive volume, negative speed (2.4 %). Volume resilience ranged within −0.04∼0.001 and speed resilience within −0.3∼0.3, indicating volume changes of <4 % of highway capacity and speed changes of <30 % of speed limits during hurricanes. A Bayesian Additive Regression Trees (BART) model identified highway type, lane direction, demographics, and land use as crucial factors influencing resilience. Highways near densely populated neighborhoods with fewer White residents and more diverse land uses exhibited lower volume but higher speed resilience, suggesting racial disparity. These findings offer valuable insights into network design and adaptation planning strategies to enhance transportation resilience and mitigate the impacts of climatic disruptions on network performance.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613747</guid>
    </item>
    <item>
      <title>Integrating interdependency effects into coastal bridge resilience against hurricane-induced waves within a life-cycle context</title>
      <link>https://trid.trb.org/View/2640585</link>
      <description><![CDATA[The life-cycle hurricane resilience assessment of coastal bridges plays an important role in guiding decisions for their long-term operation and maintenance. Although some studies focused on bridge resilience under other disasters, there remains a paucity of resilience assessment methods associated with hurricanes and extreme waves. To address this issue, this study proposes a three-step resilience assessment framework to evaluate structural performance under hurricane hazards. Such a framework improves the existing method from the following aspects: (a) characterization of structural responses under extreme waves using a numerical-based Pseudo-Fluid-Structure-Interaction (PFSI) scheme, (b) consideration of the restoration of interdependent infrastructure systems at the pre-recovery phase, and (c) multiple resilience indices that integrate the effects of resources and recovery time. The developed framework is illustrated using a high-risk coastal bridge. A multi-criteria optimization problem is formulated to examine the practical value of the proposed resilience indices. The results of the developed framework are compared with the method without considering the effects of time delay, which shows that the bias could reach 15% under extreme cases. The proposed approach could facilitate decision-making by integrating multiple performance indices of coastal bridges into the decision-making process, thereby aiding decision-makers in optimizing their objectives.]]></description>
      <pubDate>Thu, 22 Jan 2026 09:10:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640585</guid>
    </item>
    <item>
      <title>Assessment of Wave Impacts on Highway Embankments Due to Hurricanes/Trop​ical Storms in Coastal Louisiana [supporting dataset]</title>
      <link>https://trid.trb.org/View/2646972</link>
      <description><![CDATA[The submitted dataset, “Louisiana Coastal Wave & Hurricane Data—SPTC Cycle 1,” supports research on wave impacts and embankment design/vulnerab​ility assessment in coastal Louisiana. Its purpose is to provide historical and interpolated coastal wave conditions and hurricane activity for statistical wave modeling, spatial interpolation, and wave-pressure calculations.Th​e package includes four files: (1)  A .xlsx workbook of historical wave data from 13 coastal Louisiana stations, one worksheet per station (213 KB, Historical_wave​_data_13_statio​ns.xlsx). (2) A .xlsx file of historical hurricane high-water-mark records for coastal Louisiana (12 KB, Hurrican_Waterm​arks_Louisiana.​xlsx). (3) A geospatial dataset containing the interpolated 20-year return-period wave heights across coastal Louisiana (1.4 MB, Geospatial_Data​_Interpolated_2​0yr_wave_height​.xlsx). (4) A .xlsx file of budgetary data. The top three files are researcher-​generated tables and layers compiled from observed National Oceanic and Atmospheric Administration (NOAA) coastal station data and National Hurricane Center records, with processing performed through statistical interpolation (GEV analysis and ordinary kriging in Python) and geospatial mapping in QGIS to ensure spatial consistency for analysis.]]></description>
      <pubDate>Wed, 21 Jan 2026 10:46:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646972</guid>
    </item>
    <item>
      <title>Assessment of Wave Impacts on Highway Embankments due to Hurricanes/Tropical Storms in Coastal Louisiana</title>
      <link>https://trid.trb.org/View/2646973</link>
      <description><![CDATA[This study focuses on the analysis of wave pressure analyses and development of wave pressure envelopes that can be used for the design of coastal embankment as well as for assessing vulnerability of existing embankment to hurricanes along Louisiana’s coastline due to adverse weather events. The research integrates the Generalized Extreme Value (GEV) analysis with Ordinary Kriging Interpolation, producing a comprehensive spatial representation of wave height for return periods of 2, 5, 10, and 20 years. This innovative approach enables more accurate predictions of wave height variability across the Louisiana coastline, identifying regions that are most vulnerable to extreme weather impacts. Wave height data were collected from an expanded network of monitoring stations, incorporating historical hurricane data to simulate extreme conditions. The study employs empirical formulas, including the Minikin and Blackmore-Hewson methods, to calculate hydrodynamic pressures and forces on embankments. Detailed pressure distribution diagrams were developed to illustrate the combined effects of hydrodynamic and hydrostatic forces, aiding in the optimization of embankment designs. The outcome of this study underscores the critical importance of designing region-specific coastal embankments that account for spatial variability in wave impacts. The methodology outlined in this report offers a practical solution for predicting wave heights and calculating wave-induced forces on embankments, supporting disaster risk management and coastal engineering applications. The research findings will be implemented by the Coastal Protection and Restoration Authority (CPRA) in dike design practices for coastal marsh creation projects, contributing to the prevention of wetland loss in coastal Louisiana.]]></description>
      <pubDate>Wed, 21 Jan 2026 10:46:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646973</guid>
    </item>
    <item>
      <title>Economic Impact Analysis of Artificial Draft Restriction on the Lower Mississippi River Port Complex</title>
      <link>https://trid.trb.org/View/2643076</link>
      <description><![CDATA[The Lower Mississippi River Port Complex is a critical hub for U.S. and global trade, handling over 500 million tons of cargo annually, including grain, petroleum, and chemicals. In August 2021, Hurricane Ida severely disrupted maritime operations, knocking out electronic aids to navigation (eATON) at the Southwest Pass, a vital shipping corridor. This study examines the potential economic impact of eATON failure, focusing on vessel delays, draft restrictions, and supply chain disruptions. A model-based economic impact analysis was conducted, utilizing industry estimates, historical disruptions, and expert assumptions to quantify financial losses. The findings reveal that the loss of eATON resulted in a conservative estimate of $46.4 million in economic losses for a specific number of vessels over a given period, with draft restrictions accounting for 94% of the total impact. Vessel delays led to port congestion and increased operational costs, while reduced draft capacity forced vessels to carry an estimated 72,800 tons less cargo, significantly impacting U.S. grain, oil, and chemical exports. Additionally, supply chain disruptions required cargo to be rerouted via rail and trucking, further increasing transportation time and costs and reducing U.S. trade competitiveness. The study highlights the vulnerability of inland waterway navigation to weather events, emphasizes the urgent need for resilient aid to navigation systems infrastructure, and offers some next steps for securing and maintaining up-to-date and resilient systems.]]></description>
      <pubDate>Mon, 22 Dec 2025 09:52:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643076</guid>
    </item>
    <item>
      <title>Landslides and Bridge Damages in Western North Carolina after Hurricane Helene</title>
      <link>https://trid.trb.org/View/2601501</link>
      <description><![CDATA[Hurricane Helene, a Category 4 storm at landfall in 2024, caused extensive damage across the Southeastern United States, with sustained winds reaching 220 km/h. Helene resulted in over $78 billion in economic losses and claimed more than 200 lives, making it one of the most destructive hurricanes in recent history. Hurricane Helene was unique in its rapid intensification and its sustained strength as it reached the western Carolina mountains, bringing prolonged heavy rainfall that triggered multiple hazards, including widespread bridge failures. This paper reports ground observations and lessons learned from the structural damage associated with the hurricane event, highlighting the need to evaluate bridge approach designs for overtopped bridges and debris flows during extreme flooding.]]></description>
      <pubDate>Thu, 18 Dec 2025 15:37:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601501</guid>
    </item>
    <item>
      <title>Dynamic truck-drone collaborative transportation during hurricanes</title>
      <link>https://trid.trb.org/View/2594357</link>
      <description><![CDATA[The increasing frequency and severity of extreme weather events highlight the urgent need for resilient and adaptive logistics solutions. Existing models often overlook the complexities introduced by extreme weather conditions, which can significantly impair the operational efficiency of transportation means including trucks and drones. To address this gap, we propose a dynamic collaborative truck-drone vehicle routing problem (VRP) with mixed line hauls and back hauls in wind-varying conditions (CVRPLB-WV) under hurricanes lasting three to five days, aiming to maintain transportation resilience by reducing delivery delays and maximizing profit. The proposed CVRPLB-WV leverages a flexible truck-drone collaboration strategy to adapt to varying wind speeds by restricting available transportation resources and speed limits. We formulate the CVRPLB-WV problem as a mixed integer programming (MIP) model and solve the problem by proposing a resilient adaptive large neighborhood search (R-ALNS) algorithm under the rolling horizon (RH) framework to handle the inherent complexities. Extensive numerical experiments utilizing data from three real Hurricane events, i.e., Nalgae, Koinu, and Sura, demonstrate that our approach consistently outperforms the other benchmark algorithms (ALNS, LNS, Greedy, and reinforcement learning algorithms) by achieving up to 79.08 % delay reduction and higher profits. Additionally, a sensitivity analysis explores the impact of varying truck-to-drone ratios, drone endurance, capacities, hurricane durations, and collaboration strategies, providing valuable managerial insights that underscore the practical applicability and robustness of our approach.]]></description>
      <pubDate>Thu, 20 Nov 2025 17:06:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594357</guid>
    </item>
  </channel>
</rss>