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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <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>
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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>Blueprint: Coordinated Vulnerability Disclosure (CVD) Adaption and Adoption Guide for Industry To Create Their Own CVD Program</title>
      <link>https://trid.trb.org/View/2694448</link>
      <description><![CDATA[The coordinated vulnerability disclosure (CVD) is a structured process that facilitates the responsible reporting and remediation of security vulnerabilities in software, hardware, or services. It involves collaboration between the person or entity who discovers a vulnerability (often a security researcher); the vendor or maintainer who is responsible for the affected product; and potential third parties, such as government agencies or coordination centers (e.g., Computer Emergency Response Teams (CERTs)). This report provides stakeholders in the electric vehicle supply equipment (EVSE) industry with information on how to adopt and adapt the general CERT CVD guide and the tools to create their own CVD program. This report also illustrates how the roles and processes defined in the CERT CVD guide can be mapped into EVSE industry-specific roles and processes, and it provides step-by-step examples. Finally, it presents how to manage, validate, and prioritize vulnerability reports.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694448</guid>
    </item>
    <item>
      <title>Resilience-based post-earthquake restoration scheduling for urban interdependent transportation-electric power network</title>
      <link>https://trid.trb.org/View/2691674</link>
      <description><![CDATA[As critical lifeline systems, transportation network (TN) and electric power network (EPN) are highly susceptible to natural hazards, such as earthquakes during their service life. At the same time, restoration of damaged TN and EPN is essential to support the post-earthquake reconstruction and emergency rescue in affected areas. Restoration strategies were traditionally developed for TN or EPN separately. However, neglecting the potential interconnection between these two networks in the recovery phase may lead to detrimental consequences, as in real-world scenarios, the obtained strategy may be less efficient or even unfeasible given that recovery of one system is usually dependent on the others for service provision. Accordingly, this paper presents a resilience-based framework for post-earthquake restoration of interdependent transportation-electric power networks. In this framework, restoration independencies and functionality dependencies are introduced to represent the interaction between TN and EPN. Then, a bi-level optimization model with the objective of maximizing seismic resilience is established to characterize the network recovery problem. Furthermore, a solution algorithm that incorporates a genetic algorithm and a chromosome validity test operator is designed to obtain the near-optimal solution. Finally, the proposed framework is illustrated through two numerical examples.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691674</guid>
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    <item>
      <title>Evaluation of Impacts Due to a Bridge Closure: A Case Study of the Mississippi River Bridges in Arkansas</title>
      <link>https://trid.trb.org/View/2696163</link>
      <description><![CDATA[The TRC 2303 report presents a comprehensive analysis of the impacts of bridge closures across the Mississippi River in Arkansas, focusing on four key crossings: I-40, I-55, HWY 49, and HWY 82. Prompted by the emergency closure of the I-40 Hernando de Soto Bridge in 2021, the study evaluates both full and partial closures to quantify economic, safety, and mobility consequences. Using the Arkansas Department of Transportation (ARDOT) Road User Cost (RUC) framework and the Arkansas Statewide Travel Demand Model (ARSTDM), the report estimates daily costs and system-level changes in Vehicle Hours Traveled (VHT) for autos, single-unit trucks (SUT), and tractor-trailer trucks (TTT). Partial closures of high-volume bridges like I-40 and I-55 result in substantial daily costs, e.g., up to $2.4 million for I-40 along with widespread VHT increases. This is especially so for TTT, for which 6,902 miles of roadway exhibited more than a 4% increase in VHT under the I-40 closure scenario. In contrast, HWY 49 and HWY 82 closures had more localized effects, with HWY 82 showing the highest auto and SUT impacts but minimal freight disruption. Waterway disruption costs, derived from Automatic Identification System (AIS) data, also rose sharply between 2021 and 2024, with HWY 49 carrying the highest vessel volumes and HWY 82 showing the fastest growth. To support planning, the study delivered a web-based Bridge Closure Impact Analysis Tool that automates cost calculations and visualizes impacts. Mitigation strategies include operational measures, maintenance practices, and planning-based approaches, with partial closures recommended where feasible. Overall, the report equips ARDOT with data-driven tools to prioritize investments, manage emergencies, and maintain transportation resilience across river crossings.]]></description>
      <pubDate>Tue, 05 May 2026 10:18:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696163</guid>
    </item>
    <item>
      <title>Determination of Recovery Bridge Corridors by Comparing Post EQ Network</title>
      <link>https://trid.trb.org/View/2696131</link>
      <description><![CDATA[This study focuses on Caltrans District 4 in the San Francisco Bay Area (Alameda, Contra Costa, Marin, Napa, San Francisco, Santa Clara, San Mateo, Solano, and Sonoma). It proposes a framework to identify and prioritize critical bridge corridors that enable access to emergency facilities, including hospitals, fire stations, police stations, Caltrans maintenance facilities, airports, seaports, and ferry terminals. Bridges are first grouped into corridors using an interchange-based approach. Next, a shortest-path algorithm is applied to find routes from each zip-based zone to its nearest facility of each type. Corridor “usage” is computed from how frequently corridors appear on these access routes, and total usage is used to rank corridor criticality. Bridges within top corridors are then evaluated and ranked using damage probabilities. The proposed method is validated against Google Maps, showing 5.6% route dissimilarity, indicating that access to critical facilities strongly depends on Caltrans routes. Corridor importance varies by facility type because facility distributions differ. For example, District 4 contains 563 fire stations across 298 zones, so most zones access a fire station locally and only 31 zones require Caltrans bridges, whereas 159 zones require Caltrans bridges to reach hospitals. The study also compares corridor rankings with and without population weighting. Without population, top corridors often occur in rural areas that serve as sole connectors for multiple zones; adding population shifts priorities toward densely populated areas, highlighting the need to define planning objectives. An updated methodology is proposed to remove selected corridors and recomputes rankings to test impacts, showing rural corridors are often irreplaceable while urban networks are highly redundant. Finally, another optimization method is introduced to minimize the number of Caltrans bridges used, trading off travel time to reduce recovery designations and costs. A web-based platform implements and visualizes these methods.]]></description>
      <pubDate>Mon, 04 May 2026 11:19:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696131</guid>
    </item>
    <item>
      <title>Federal-Industry Waterway Governance Mapping</title>
      <link>https://trid.trb.org/View/2698370</link>
      <description><![CDATA[This project will produce the first comprehensive governance map of the U.S. inland waterway system, documenting how federal agencies, waterway commissions, port authorities, operators, industry associations, and advisory bodies exercise authority, coordinate responsibilities, and influence decisions across planning, operations, maintenance, and emergency response. While the inland waterway system depends on a complex interplay of federal ownership, federally authorized navigation channels, industry-operated vessels, federally maintained locks and dams, state commissions, port authorities, cooperative working groups, and advisory committees, there is currently no resource that synthesizes this institutional architecture into a clear, accessible structure. The research will analyze agency documentation, statutory authorities, standing committee structures, and operational guidance, complemented by targeted interviews with practitioners, to clarify how decisions flow through the system and how organizations interact across routine and non-routine conditions. The final product will provide a governance map and a narrative analysis that identifies gaps, redundancies, and friction points in the institutional landscape. This work will support planners, policymakers, and operators and offer a foundational understanding of how governance arrangements shape reliability, resilience, and system performance.]]></description>
      <pubDate>Fri, 01 May 2026 19:58:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698370</guid>
    </item>
    <item>
      <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>Resilience assessment and enhancement of urban transportation interdependent network under cascading failure</title>
      <link>https://trid.trb.org/View/2664296</link>
      <description><![CDATA[Urban transportation systems are essential for sustaining urban growth and ensuring efficient resource allocation. Existing studies primarily focus on evaluating network resilience after system disturbances, with insufficient attention paid to the response mechanisms during disturbances and the enhancement of resilience afterward. Therefore, we propose a cascading failure model that considers passenger transfer impedance, and design a recovery priority strategy for failed nodes to maximize the resilience of the urban transportation interdependent network (UTIN). Specifically, based on traffic sensing data, we construct a station-centric UTIN to assess structural resilience under various disruption scenarios and different transfer distances. By combining impedance function and flow redistribution, passenger behavior and node load update are considered. Additionally, the recovery priority strategy for failed nodes is discussed. The results indicate: 1) UTINs with longer transfer distances exhibit stronger resistance to risks. When considering impedance costs, the optimal transfer distance is 800 m. 2) During cascading failure propagation, optimizing flow distribution effectively lowers the critical capacity threshold required for system stability, thereby enhancing network resilience. 3) During the recovery phase, different recovery strategies exhibit significant differences in their effectiveness in restoring system resilience. The research findings provide valuable references for disaster prevention, emergency response, and post-disaster recovery in urban transportation systems.]]></description>
      <pubDate>Thu, 30 Apr 2026 11:28:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664296</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>Anticipating the consequences of zero-occupant vehicles (ZOV): a qualitative study of urban mobility impacts in Toronto</title>
      <link>https://trid.trb.org/View/2664079</link>
      <description><![CDATA[The impact of Autonomous Vehicles (AVs) on our everyday lives depends on their usage and operation, as they can yield either favorable or unfavorable outcomes. AVs with self-driving capabilities can generate trips by zero-occupancy vehicles (ZOVs), i.e., AVs that travel without passengers. Unlike occupied AVs that replace human-driven trips, ZOVs generate additional empty vehicle circulation as they reposition between trips or seek parking, raising distinct concerns for traffic management, environmental sustainability, and infrastructure planning. While prior research has focused on quantitative modeling of ZOV impacts, policymakers and stakeholders’ perspectives on regulating this emerging technology remain underexplored. This study addresses this gap through qualitative analysis of focus groups and interviews with 45 stakeholders in Toronto, Canada, including planners, engineers, and policymakers. Toronto provides a relevant case study as a major North American city actively planning for AV deployment, with municipal authority over transportation policy that enables regulatory innovation. Through thematic analysis, we identify key policy concerns such as congestion management, environmental sustainability, infrastructure adaptation, and the economic implications of AV adoption. Stakeholders emphasized the need for regulatory interventions to limit unnecessary ZOV trips. Specific recommendations include establishing dedicated lanes and designated testing areas for safe ZOV deployment, creating comprehensive mobility pricing frameworks, and leveraging data from connected vehicles and infrastructure for real-time traffic management and emergency response prioritization. Standardization and cross-sector collaboration between government, industry, and academia were seen as critical to ensuring an effective transition. Our findings provide evidence-based guidance for policymakers navigating the regulatory challenges of ZOV deployment and highlight the need for adaptive, collaborative governance approaches in the context of rapidly evolving autonomous vehicle technology.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664079</guid>
    </item>
    <item>
      <title>Population Evacuation in Motion: Harnessing Disaster Evolution for Effective Dynamic Emergency Response</title>
      <link>https://trid.trb.org/View/2659121</link>
      <description><![CDATA[This study introduces a robust Dynamic Population Evacuation (DPE) framework in response to the escalating challenges in wildfire-urban interface regions. The DPE model elevates emergency response strategies by seamlessly integrating traffic simulation, real-time mobility tracking, optimization systems leveraging advanced algorithms, and cloud-based communication. The offline component of the framework focuses on pre-disaster preparation by optimizing the allocation of evacuation shelters and generating initial route plans for impacted populations, incorporating changing on-the-ground hazard conditions and geography. The online phase dynamically reallocates shelters and provides real-time guidance for vehicle navigation. This approach significantly improves the efficiency and safety of evacuation processes by utilizing advanced algorithms and cloud infrastructure. Key innovations include an offline planning phase, optimizing shelter allocation and route plans with a keen eye on evolving hazards and geography. Extensive testing and simulations of the DPE framework, including models of real-world evacuation scenarios such as the Tubbs fire in California, validate the proposed approach and demonstrate significant improvement in efficiency, responsiveness, and safety for populations relative to traditional evacuation planning methods and frameworks. The study further underscores the broader applicability of the DPE model to enhance resilience and outcomes in urban evacuations not only for wildfires but also for other hazards such as floods and earthquakes.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659121</guid>
    </item>
    <item>
      <title>Volume Estimation of Landslide Debris on Roadways Using UAVs and Structure from Motion</title>
      <link>https://trid.trb.org/View/2640277</link>
      <description><![CDATA[This paper introduces a novel framework that combines computer vision and Structure from Motion to detect and calculate the volume of landslide debris on roadways to enable planning for efficient roadway clearance and recovery operations. The framework first employs the You Only Look Once (YOLO) algorithm for efficient road segmentation and identification of obstructions from aerial images. This step is crucial for the initial detection and analysis of obstacles. Following this, the Structure from Motion (SfM) technique is integrated to accurately estimate the volume of obstructions on the roadway. This measurement is vital for planning practical and timely removal strategies, such as determining the order of removal operations and allocation of equipment resources for their removal. This methodology provides a comprehensive solution for quick and reliable road clearance planning after landslide events and other hazards that cause debris blockage of roadway networks. Such automated detection and estimation systems can improve effective disaster response to ensure access routes for emergency operations.]]></description>
      <pubDate>Tue, 28 Apr 2026 12:18:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640277</guid>
    </item>
    <item>
      <title>Urban Priority Pass: Fair signalised intersection management accounting for passenger needs through prioritisation</title>
      <link>https://trid.trb.org/View/2692489</link>
      <description><![CDATA[Over the past few decades, efforts of road traffic management and practice have predominantly focused on maximizing system efficiency and mitigating congestion from a system perspective. This efficiency-driven approach implies the equal treatment of all vehicles, which often overlooks individual user experiences, broader social impacts, the fact that users are heterogeneous in their urgency and that they experience different costs when being delayed. Even though they are the major bottleneck for traffic in cities, no dedicated instrument enables prioritization of individual drivers at intersections. The Priority Pass is a reservation-based, economic controller that expedites entitled vehicles at signalized intersections, without causing arbitrary delays for non-entitled vehicles and without affecting transportation efficiency de trop. Particularly applicable to large, congested cities with rich sensor infrastructure, the prioritization of vulnerable road users, emergency vehicles, commercial taxi and delivery drivers, or urgent individuals, this approach can enhance road safety, and achieve social, environmental, and economic goals. A case study of Manhattan demonstrates the feasibility of individual prioritization (up to 40% delay reduction), and quantifies the potential of the Priority Pass to gain social welfare benefits for the people. A market for prioritization could generate up to $ 1 million in daily revenue for Manhattan, and equitably allocate delay reductions to those in need. The findings provide a foundation for integrating user-centric prioritization mechanisms into emerging smart city traffic management systems, supporting data-driven policymaking and equitable mobility planning. Source code and material available on GitHub https://github.com/DerKevinRiehl/urban_priority_pass.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:18:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692489</guid>
    </item>
    <item>
      <title>Accessibility Assessment of Coastal Transportation Networks Under Storm Surge Scenarios Influenced by Sea Level Rise</title>
      <link>https://trid.trb.org/View/2581461</link>
      <description><![CDATA[Coastal cities are particularly vulnerable to the impacts of sea level rise, which is one of the main consequences of human-generated climate change. As global temperatures increase, polar ice caps and glaciers melt, leading to the expansion of seawater and the rising of sea levels, which poses numerous risks and challenges for transportation networks near the shore in coastal cities, impacting their accessibility. Several studies have analyzed the impact of inundation due to storm surge on the transportation networks in low-lying regions, but none of them consider the transient case of overtopping and overflow before the inundation itself. By implementing a coupled hydrodynamic and wave model in combination with the coastal geometry to calculate the overtopping, overflowing, and later inundation, an accessibility-based methodology is used to evaluate the level of disruption in the transportation network affected by the natural event. To weigh the area in accordance with the populations, we use Mobile Spatial Statistics as an input of our accessibility evaluation model. The study area is the Suruga Bay, on Shizuoka prefecture, Japan. The coast of Shizuoka was hit by Typhoon Hagibis in October of 2019, causing the highest-level storm surges recorded in the area and widespread destruction. When contrasting travel durations between standard and the most degraded scenarios, there is a 1.6% cumulative rise in travel time throughout the entire surveyed network.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581461</guid>
    </item>
    <item>
      <title>Optimizing pre-occurrence maritime search and rescue system: A dynamic location-allocation model with considering spatiotemporal accessibility</title>
      <link>https://trid.trb.org/View/2693385</link>
      <description><![CDATA[This study presents a novel approach to optimizing a pre-occurrence search and rescue (SAR) system to enhance both efficiency and cost-effectiveness in maritime emergency. First, we formulate a dynamic location-allocation problem (LAP) for rescue resources (including rescue stations at sea, rescue vessels, and emergency supplies) under multi-period scenarios, simultaneously accounting for seasonal variations in incident demand, rescue capacity, and oceanic conditions. To quantify the impact of these variations on LAP decision-making, we integrate a spatiotemporal SAR accessibility measure, enabling seasonal reallocation of SAR resources across different scenarios. The model aims to achieve two key objectives: maximizing expected accessibility to incidents and minimizing total system costs. To solve this complex problem, we employ a hybrid algorithm that combines k-means clustering with the multi-objective plant growth simulation algorithm (MO-PGSA). A real-life case study in the Bohai Sea, China, validates the effectiveness of the proposed approach, with numerical results demonstrating significant improvements in SAR system performance. The findings offer valuable decision support for strategic SAR planning, enhance resource utilization, and contribute to improved maritime safety management.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693385</guid>
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