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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>Contextualized transport reallocation for crisis response in smart cities</title>
      <link>https://trid.trb.org/View/2697233</link>
      <description><![CDATA[Extreme events, such as floods, have become increasingly frequent and severe, with disproportionate impacts on vulnerable populations. Addressing these challenges requires integrated solutions that combine event detection, vulnerability assessment, resource reallocation, and communication with affected communities. This paper explores different components of such a response. The contribution focuses on bus rerouting during floods, applying graph-based algorithms on top of a vulnerability assessment procedure to reorganize urban transport and prioritize the mobility of those most in need. Results indicate that the approach can identify efficient detours with low computational cost, making it feasible for real-time applications. The solution is part of the Integrated Crisis Awareness and Resource Utilization for Smart Cities (ICARUS) system, a smart city hypervisor aimed at supporting rapid and multifaceted response to extreme events. This paper describes the overall system, with a focus on the use case of bus rerouting during floods.]]></description>
      <pubDate>Tue, 05 May 2026 09:26:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697233</guid>
    </item>
    <item>
      <title>Flood Inundation-Induced Bridge Pressure Scour: Developing New Prediction Equations and Exploring Machine-Learning Methods</title>
      <link>https://trid.trb.org/View/2663201</link>
      <description><![CDATA[Bridge pressure scour occurs when the bridge deck is inundated during a flood event, causing significant threats to the stability of local streambeds and bridge foundations. In the present study, new flume experiments were conducted to investigate the influence of the flow intensity, bridge deck clearance height, bridge deck thickness, and bridge deck longitudinal length on pressure scour depth. A comprehensive pressure scour dataset was assembled, using the new experimental data and existing data from the literature, to examine detailed parameter dependencies on scour depth and derive new empirical predictive equations. During this process, XGBoost, a machine learning (ML) model, was also developed using the dataset to assist parameter analysis, primarily as a parameter analyzer to corroborate variable selection and functional dependencies, and secondarily to provide an alternative, data-driven approach to scour prediction. The results show that, by using the newly proposed equations, 47% data are within the ±20% accuracy range, while the rate for over 20% underestimation was below 12%. This performance is significantly better than the existing methods and is beneficial in its theoretical framework. While the ML model attains strong predictive accuracy, we recommend treating it as a diagnostic parameter-analysis tool at the current stage rather than a reliable design method; the lack of a physical basis limits its generalizability and makes it important to exercise extra caution in practice. Finally, future research directions are discussed in this paper, particularly the future fusion of conventional empirical methods and ML models.]]></description>
      <pubDate>Fri, 01 May 2026 14:33:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663201</guid>
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      <title>Dynamic evolutionary pathway analysis of urban rail transit flood risks and intelligent decision support based on knowledge graphs</title>
      <link>https://trid.trb.org/View/2664366</link>
      <description><![CDATA[With the intensification of global climate change, rainstorm disasters have become increasingly frequent and catastrophic. Urban rail transit (URT) systems, which are primarily constructed underground, possess structural features that make them particularly vulnerable to severe impacts during heavy rainfall events. Such disasters can result in significant casualties and substantial losses. Meanwhile, extensive domain-specific knowledge has been accumulated from historical disaster events. Effectively extracting and utilizing such knowledge is essential for improving disaster risk identification and enhancing emergency management practice. To address these challenges, this study proposes a method for analyzing risk evolution mechanisms by integrating Knowledge Graph and Natural Language Processing (NLP) technologies. The knowledge graph enables structured knowledge representation and facilitates effective knowledge reuse. Building on this, a knowledge-driven decision support model is established by combining the language understanding capability of NLP with the inferential capacity of knowledge graphs. Case studies of representative examples are conducted to validate the effectiveness of the proposed method in this study. The findings show that structuring knowledge in the form of a graph network offers significant advantages for the intelligent analysis of disaster risk evolution. On one hand, a large amount of multi-source, heterogeneous knowledge related to URT flood risks is systematically structured and represented, thereby enhancing the efficiency of knowledge utilization by decision-makers. On the other hand, integrating NLP with knowledge graph–based risk network analysis enables the accurate identification of potential risk paths, providing valuable insights and a foundation for disaster prevention and mitigation decision-making.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664366</guid>
    </item>
    <item>
      <title>Beyond waterlogging: Evaluating the impact of extreme rainfall on the road network</title>
      <link>https://trid.trb.org/View/2664291</link>
      <description><![CDATA[Existing research of extreme rainfall impact on transport networks primarily examines the effect of waterlogging. Although the other two main factors—reduced visibility and traffic-signal power outages—have been shown to significantly affect road operation, their contributions at the network scale remain underexplored. Taking a macroscopic approach, this study gauges the impacts of these three factors on the road network connectivity and efficiency during extreme rainfall through a case study of 26 Local Government Areas in and around Greater London. The result shows that focusing solely on waterlogging while disregarding reduced visibility and traffic signal power failures overestimates road capacities by 15–30% and underestimates network efficiency impacts by 1–23% under different rainfall scenarios. Particularly, the largest impact underestimation is observed for 1-in-30-year rainfall risk, where waterlogging is less dominant, while poor visibility considerably contributes to the impacts. The analysis also suggests that signal power failures during rainfall have limited, localised effects at the network level.]]></description>
      <pubDate>Thu, 30 Apr 2026 11:28:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664291</guid>
    </item>
    <item>
      <title>A GNN-Based Framework for Assessing Flood Impacts on Highway Networks: Integrating Network Structural, Functional, and Social Features</title>
      <link>https://trid.trb.org/View/2685634</link>
      <description><![CDATA[Due to global change, natural disasters such as floods have become more frequent in recent years. An effective impact assessment of highway networks before and during floods can help transportation departments prioritize resources and take necessary emergency measures. Although current works have assessed the flood impacts from different perspectives, none have comprehensively evaluated the integrated impacts that capture network structural, functional, and social features, limiting their reliability for decision-making and resilience planning in engineering management. To address this gap, we proposed a graph neural network (GNN)-based framework that incorporates two synthesized indicators—the disaster impact index and criticality score—to integrate structural, functional, and social features. These multidimensional features were inputs to the GNN model, enabling it to capture complex interdependencies and more accurately predict traffic flow and speed under disasters. The practicality of this framework was demonstrated in the case study of Harris County affected by floods caused by Hurricane Harvey. The results showed that Beltway 8, IH-10, and IH-45 were most vulnerable to potential impacts before the flood, while Beltway 8, US-59, and IH-10 were most impacted during the flood, highlighting the need for proactive preflood preparedness and prioritized postflood recovery for these critical roadways. The proposed framework captures complex interdependencies among multidimensional features and more accurately predicts traffic flow and speed. Consequently, it provides a more realistic prediction of the uncertainties in transportation network performance under disasters, offering a robust and practical tool for resilience planning and resource prioritization of other critical infrastructure systems in engineering management.]]></description>
      <pubDate>Thu, 30 Apr 2026 09:11:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685634</guid>
    </item>
    <item>
      <title>From the Titanic era to the AI era: a rational framework for life-cycle damage stability and flooding risk management of passenger ships</title>
      <link>https://trid.trb.org/View/2666614</link>
      <description><![CDATA[Maritime accidents involving passenger ships have long influenced industry approaches to ship design, emphasizing resilience and fail-safe performance following flooding events. Consequently, regulatory frameworks have focused predominantly on damage containment and emergency response rather than on accident prevention. This emphasis is reinforced by largely rules-based regulations that apply mainly to newbuildings and reflect legacy assumptions that have not kept pace with modern technological advances. As a result, many existing ships operate under comparatively lower safety standards, with limited means to sustain or enhance safety during operation. Meanwhile, progress in accident prevention has been modest, failing to capitalize on contemporary developments that could offer cost-effective and transformative safety improvements. There is a clear need for a paradigm shift from post-accident protection toward proactive accident prevention, with the ultimate objective of eliminating loss of life at sea. This paper proposes such a shift and outlines the essential elements required to design and operate fundamentally safer ships.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666614</guid>
    </item>
    <item>
      <title>The digital reconstruction and resurrection of the RMS Titanic’s sinking</title>
      <link>https://trid.trb.org/View/2666608</link>
      <description><![CDATA[This work presents a supercomputer-based digital reconstruction of the RMS Titanic’s sinking using advanced nonlinear finite element analysis (NLFEA) and computational fluid dynamics (CFD) simulations. The modeling reproduces the sequence of events from iceberg collision and progressive flooding to structural failure and ultimate hull-girder breakup. A counterfactual head-on collision scenario is also investigated to assess whether the vessel might have remained afloat under such conditions. To enable these large-scale simulations, continuous welded structural models were developed to represent the ship’s original riveted hull, informed by archival blueprints, wreck-site evidence, and historical testimonies for validation. All computations were carried out on the high-performance computing facilities at University College London. The findings, featured in Titanic: The Digital Resurrection (BBC and National Geographic, April 2025), commemorate the 113th anniversary of the disaster and provide the most comprehensive and technically accurate visualization of the sinking to date. Selected simulation footage is available at: https://www.youtube.com/watch?v=wPRuoC_091g.]]></description>
      <pubDate>Wed, 29 Apr 2026 17:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666608</guid>
    </item>
    <item>
      <title>Symbolic Dynamics Coarse-Graining Approach for Analysing Traffic Flow Dynamics in Complex Road Networks under Flooding</title>
      <link>https://trid.trb.org/View/2640286</link>
      <description><![CDATA[In the context of climate change, accurately understanding and predicting traffic flow patterns in transportation systems is increasingly vital. Traditional models, which often rely on flow-speed-density relationships, statistical methods, and time series algorithms, assume determinism or higher order linearity but struggle to capture the inherent nonlinearity of transportation systems, particularly under flood conditions. This study introduces a novel framework using a symbolic dynamics coarse-graining algorithm to analyse traffic systems’ nonlinearity. The approach refines phase space, transforms time series into symbolic vectors, and constructs a weighted directed network to interpret traffic flow dynamics. The study assesses the precision of this method using London’s road transportation network data under flooding conditions. Results indicate that the proposed network features effectively reveal periodic patterns and nonlinear dynamics of traffic flow, especially during floods. This enhanced understanding of traffic flow dynamics contributes to developing more robust traffic management strategies and improving the resilience of transportation networks in flood conditions. The findings are significant for researchers, authorities, and policymakers, as they offer insights for real-time traffic control and policy decisions aimed at bolstering transportation resilience.]]></description>
      <pubDate>Tue, 28 Apr 2026 12:18:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640286</guid>
    </item>
    <item>
      <title>The impact of risk control options in reducing/preventing risk in case of a flooding event</title>
      <link>https://trid.trb.org/View/2634078</link>
      <description><![CDATA[Safety in case of a flooding event is a primary concern in the design process of passenger ships and should be thoroughly assessed from the initial design phases. To evaluate the risk of flooding events, an effective metric is needed to compare various design solutions. The Potential Loss of Lives (PLL) is a valuable tool for quantifying this risk from the early stages of design, enabled by a multi-level framework developed during the FLARE project, which enhances the reliability of predictions as the design progresses. This approach facilitates the examination and assessment of countermeasures, known as Risk Control Options, aimed at reducing or preventing risk in the event of flooding. This study analyses the implementation of different Risk Control Options across a sample of nine passenger ships, including cruise and Ro-Pax vessels. The analysis is conducted at various levels of fidelity in accordance with the established framework, highlighting the effectiveness of mitigation and prevention measures in reducing PLL.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2634078</guid>
    </item>
    <item>
      <title>Study on Hydroaccumulation Patterns in V-Shaped Slope Tunnels of Subways During Floods</title>
      <link>https://trid.trb.org/View/2632983</link>
      <description><![CDATA[Under the broad consensus on reinforcing flood resilience in underground spaces, the hydraulic properties of metro tunnels have not been thoroughly examined. V-slope configurations are widely adopted as a standard feature in metro tunnel systems. This study aims to enhance the understanding of water propagation mechanisms in such tunnels to optimize the response of metro systems to upcoming floods. Through a combination of scaled physical model experiments and VOF numerical simulations, the research reveals key stages and patterns of water accumulation in V-shaped slope tunnels. The flood propagation process is divided into four stages: downhill flow on a single slope, uphill flow undergoing deceleration and accumulation, emergence of hydraulic jump, and wave reflection and oscillation. By investigating hydraulic jump characteristics and the evolution of submersion under varying conditions, the research highlights the local flow field discontinuity and identifies the incompatibility of existing hydraulic models with metro tunnel flooding prediction. It emphasizes the importance of considering detailed flood front movements and the surge of water depth for early flood warning in metro tunnels. The findings enhance predictive accuracy for inundation timing and dynamic flood progression.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632983</guid>
    </item>
    <item>
      <title>Laboratory Study of IoT-Based Real-Time Culvert Blockage Detection</title>
      <link>https://trid.trb.org/View/2645426</link>
      <description><![CDATA[Extreme weather events are increasing the risk of flood-related infrastructure failures in areas where culverts play a critical role in water management. Blockages caused by sediment and debris can significantly reduce hydraulic capacity, leading to flooding and structural damage. Existing monitoring solutions such as visual inspection, conventional sensors, and closed-circuit television (CCTV)-based systems are often labor-intensive, costly, energy-demanding, resource-intensive, or insufficiently scalable for widespread application, especially in rural or resource-limited areas. This study presents a novel, low-cost Internet of Things (IoT)-enabled system for real-time culvert blockage detection that overcomes these limitations by eliminating reliance on visual data and complex computing infrastructure. By leveraging low-cost ultrasonic sensors and a geometry-independent decision-rule-based classification algorithm embedded in the microcontroller, the system demonstrated average detection accuracy of 87.0% (steady state) and 84.0% (surge) across 100 steady-state and 75 surge condition experiments in a controlled laboratory environment. The system successfully categorized blockage levels (0%, 25%, 50%, 75%, and 100%) and showed consistent performance under varying flow conditions. With a setup cost of approximately USD 35 per unit, the system offers high scalability, low power requirements, and practical deployable across large culvert networks, positioning it as a transformative tool for proactive culvert management and flood mitigation. Future work will focus on field trials under varied conditions for enhanced reliability, with potential applications in standardizing culvert monitoring practices and supporting proactive maintenance strategies.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645426</guid>
    </item>
    <item>
      <title>Assessing the vulnerability of U.S. energy infrastructure to dual source flood hazards: A spatial and population exposure analysis</title>
      <link>https://trid.trb.org/View/2657282</link>
      <description><![CDATA[Flood risk to U.S. energy infrastructure (EI) is shaped by both inland and coastal processes, yet most national assessments rely on a single hazard dataset. To address this limitation, we combine FEMA Special Flood Hazard Areas (SFHA; 1% annual chance) with NOAA's coastal composite (storm surge, high-tide flooding, sea-level rise, and related layers) to develop a unified view of exposure. Using a national EI inventory (n = 21,988), we conduct spatial overlays, county-level Getis-Ord Gi* clustering, subtype analyses, and a 5-mile population proximity assessment. Nationwide, 3174 facilities (14.4%) are classified as flood-exposed in the combined dataset, compared with 9.2% when using FEMA alone and 9.5% when using NOAA alone; 925 facilities (4.2%) are exposed in both, while FEMA-only (1096) and NOAA-only (1153) contribute roughly equally. Exposure varies by sector, with petrochemical and petroleum facilities exhibiting the highest rates. Subtypes such as petroleum ports, LNG terminals, and hydropower plants stand out as particularly exposed. Within NOAA's footprint, agreement between sources is high (87.8%), but NOAA-only facilities far exceed FEMA-only facilities, highlighting additional coastal exposure beyond regulatory SFHAs. Hotspot analysis reveals complementary geographies: NOAA emphasizes a continuous coastal belt (TX–LA Gulf, Florida, Mid-Atlantic, NY–NJ harbor, Puget Sound), while FEMA emphasizes inland regions along the Mississippi and Ohio–Tennessee corridors. Population exposure is substantial, with over 52 million people living within 5 miles of FEMA SFHA electric power facilities and 37 million near petroleum assets, underscoring the societal stakes of infrastructure disruption. To our knowledge, this is the first national-scale study to integrate FEMA and NOAA hazard datasets for EI exposure, providing a more comprehensive basis for resilience planning and focusing attention on the sectors and communities at most significant risk.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:40:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657282</guid>
    </item>
    <item>
      <title>Integrating human capabilities into flood vulnerability assessments for transport systems</title>
      <link>https://trid.trb.org/View/2686520</link>
      <description><![CDATA[Traditional approaches to evaluating transport network vulnerability primarily focus on technical performance metrics or economic costs, often overlooking the societal and human impacts of disruptions. This paper addresses this gap by proposing a novel framework that integrates the Capability Approach (CA) into flood-vulnerability assessment of transport networks, thus linking individuals’ real freedoms to pursue valued life outcomes. A metric, Capability-Link-Dependency is used to quantify how transport disruptions can potentially constrain individual capabilities to access essential services such as healthcare, food, education and emergency services. A case study in Fingal County, Ireland, demonstrates how communities may experience severe accessibility losses even when located outside flood zones. The findings highlight that vulnerability assessments should incorporate wellbeing to ensure equitable resilience planning. This approach identifies both critical links and socially vulnerable populations and provides a transferable framework that can be expanded to other hazards and geographic contexts.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:37:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686520</guid>
    </item>
    <item>
      <title>Scour Around Bridge Piers of Overflow Structures at I-35 Bridge on the Cimarron River</title>
      <link>https://trid.trb.org/View/2680662</link>
      <description><![CDATA[The October 1986 flood of the Cimarron River resulted in severe scour around piers of eight overflow structures of the I-35 bridge. A major portion of the flood passed through these structures resulting in a high velocity of flow. Furthermore, the bridge is located on a meander, and that leads to a skewed flow in the flood plain. This fact caused additional scour around the piers. The scour was measured using the Electronic Distance Meter. After the flood receded, the maximum depth of scour ranged from 10 feet to 30 feet.]]></description>
      <pubDate>Sat, 11 Apr 2026 11:02:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680662</guid>
    </item>
    <item>
      <title>Flood-induced traffic congestion and accessibility loss for urban road networks using agent-based simulation: The case study of Bristol, UK</title>
      <link>https://trid.trb.org/View/2673517</link>
      <description><![CDATA[This study proposes a methodology for assessing the impact of flood-induced functional disruptions on urban road networks through an agent-based traffic simulation. Network functionality is altered by reducing roads’ free-flow speeds using a risk-based approach, and traffic is appraised considering agent-based traffic dynamics. MATSim, an open-source transport simulator, is employed to model dynamic traffic redistribution and congestion under both baseline (non-flood) and flood conditions of the urban road transportation network. The methodology is applied to the city of Bristol, UK, which is chosen for its complex road layout and flood susceptibility. Key indicators, including travel speed ratios, redistribution ratios, changes in agent count, and time-based isochrones, are used to assess variations in congestion and accessibility under both baseline and flood conditions. This study further advances existing approaches by comparing the spatial shifts of congestion hotspots before and after flooding, and by integrating hazard scenarios to predict potential future congestion patterns and their subsequent impacts on the accessibility of critical facilities, such as the Bristol Royal Infirmary. Results indicate a substantial redistribution of traffic from flood-affected minor roads to central arterial routes, leading to increased congestion and reduced accessibility, which can be particularly detrimental to emergency services that require rapid access to affected areas. The findings highlight the importance of simulating agent-level behavioral responses to network disruption caused by flooding and provide a transferable framework for assessing urban transport resilience during flood events.]]></description>
      <pubDate>Wed, 08 Apr 2026 15:32:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673517</guid>
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