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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>Use of Design-Build on Mass Transit Rail Projects</title>
      <link>https://trid.trb.org/View/2164477</link>
      <description><![CDATA[While the design-build delivery method has been used successfully on many types of projects, its application to mass transit rail projects has produced mixed results. In an effort to determine the reasons for this, a study was undertaken to evaluate the use of the design-build delivery method on large mass transit rail projects in the United States. The objective of the study was to analyze the New Starts process used by the Federal Transit Administration. Eight design-build mass transit rail projects were analyzed to determine if revisions could be identified that would facilitate the use of a design-build delivery process. Questionnaires were sent to the three primary stakeholders for each project to collect data regarding how the project was administered and the outcome achieved. Analysis of the data indicate that two useful changes to the New Starts process would be: (1) the timing of the procurement of the design-build contractor and (2) timely assurance of the availability of federal funds.]]></description>
      <pubDate>Mon, 05 Oct 2026 21:48:15 GMT</pubDate>
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    <item>
      <title>“It’s like pinball”: train driver preferences and experiences with in-cab technology</title>
      <link>https://trid.trb.org/View/2736341</link>
      <description><![CDATA[Modern train cabs have multiple technological systems aiming to minimise the risk of an incident. This qualitative study of train driver instructors investigated: what in-cab systems and warnings are perceived to be the most helpful for train drivers? And, what themes best describe the relationship between train driving and in-cab technology?. Five online focus groups with train driver instructors (n = 19) involved participants undertaking a two-part prioritisation exercise considering the current technologies and safety features in the train cab and ranking the three they considered to be most helpful and most unhelpful. Open discussion about the importance of the technologies in supporting safety, the driver instructors’ opinions about using the technologies, and any safety gaps followed. The safety systems considered most helpful were the AWS (Automatic Warning System), Vigilance, DRA (Driver Reminder Appliance), and TPWS (Train Protection Warning System). Three key themes were identified. “The patchwork cab”, highlights the participants’ experience of using the numerous and sometimes overlapping in function in-cab technologies; “eyes, ears, and staying aware in the cab”, highlights challenges around distraction; and “balancing alerts and silence” highlights workload dynamics and task engagement. Additions of new safety features while retaining legacy systems has resulted in a patchwork like cab design that may not effectively support the driver in their work and decision making. The management of acknowledged distractions has become normalised and is accepted rather than designed out of the cab. In-cab technology has an important role to play in safe vehicle operations as humans and machines become essential team-mates. However, saturation point has been reached for this patchwork approach to in-cab technology, and it will be a challenge to the industry to change this method and redesign systems rather than adding to them.]]></description>
      <pubDate>Mon, 05 Oct 2026 11:01:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736341</guid>
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      <title>Effects of station-area built environment on urban rail transit passenger flow</title>
      <link>https://trid.trb.org/View/2785347</link>
      <description><![CDATA[The spatiotemporal mismatch between urban rail transit (URT) stations and surrounding urban functions challenges operational efficiency worldwide. Existing research predominantly focuses on average daily ridership, offering limited insights into how built environment influences vary across station types, time periods, and spatial scales. This study develops an integrated framework combining station classification with multiscale geographically weighted regression (MGWR) to investigate the mechanisms linking built environment to passenger flows. Using Changsha, China as a case study, stations are classified into residential, employment, and mixed-use types via K-Means clustering. The MGWR model is compared with OLS model and GWR model. Results reveal three key findings: built environment effects exhibit spatiotemporal heterogeneity, functional-type dependence, and spatial scale variability; factors such as office density have divergent effects across station types and between morning and evening rush hours. The framework effectively identifies local and global operational scales, supporting a paradigm shift towards station-specific, refined governance strategies.]]></description>
      <pubDate>Mon, 05 Oct 2026 09:07:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2785347</guid>
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    <item>
      <title>High-Speed rail expansion and avian Biodiversity Decline: Evidence from a Hub-Link Perspective</title>
      <link>https://trid.trb.org/View/2785056</link>
      <description><![CDATA[Although the economic effects of high-speed rail (HSR) are well documented, its implications for avian biodiversity remain unclear. Using a county-level panel for China from 2002 to 2020, we combine effort-adjusted indices from citizen science bird records with geospatial data on HSR lines and stations and estimate treatment effects using a Double Machine Learning (DML) framework. A hub-link decomposition indicates that the negative estimate for avian biodiversity is concentrated in counties with HSR stations, whereas pass-through corridors have no detectable average effect at the county scale. The mechanism analysis is consistent with increased human activity and land conversion around stations. A micro-spatial buffer analysis further shows that the avian estimates are most negative within 1 km of stations and diminish with distance. These findings suggest that ecological assessments of HSR projects should consider not only rail alignments but also station-area land development.]]></description>
      <pubDate>Mon, 05 Oct 2026 09:07:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2785056</guid>
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    <item>
      <title>Feasibility of Vibration Test Unit Modifications for TGMS Validations</title>
      <link>https://trid.trb.org/View/2784738</link>
      <description><![CDATA[The Federal Railroad Administration (FRA) sponsored a research team from ENSCO to determine the feasibility of using the Vibration Test Unit (VTU) located at the Transportation Technology Center (TTC) to validate the accuracy of track geometry measurement systems (TGMS). The VTU can dynamically input deviations occurring at each wheel to create a simulation of a full-scale rail car moving along a track. TGMS systems use optical sensors consisting of lasers and cameras to measure the position and shape of the rail relative to the rail car. Inertial sensors are used to determine the position of the rail car in space as a reference plane and thereby compensate for vehicle movements. The team evaluated adding a complementary fixture to the VTU to allow assessment of TGMS accuracy under dynamic scenarios in a controlled laboratory environment. The fixture would dynamically move short railhead samples, providing the same rail deviation input to the optical sensors of the tested TGMS that the VTU is inputting to the rail vehicle. Researchers explored two potential concepts for the fixture, one based on a Stewart platform and the other using a set of linear motor actuators. The team found that the VTU fitted with the additional fixture can be used to validate TGMS systems in tangents with reasonable fidelity, despite the inability to input gage deviations. This concept also would not be able to evaluate the performance of TGMS systems in curves due to VTU’s inability to replicate centripetal accelerations and forces.]]></description>
      <pubDate>Sat, 03 Oct 2026 18:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784738</guid>
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    <item>
      <title>A Risk-Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm
Hazards</title>
      <link>https://trid.trb.org/View/2784736</link>
      <description><![CDATA[Researchers from Michigan Technological University, in collaboration with Argonne National Laboratory, conducted this study from May 2022 to July 2025. They aimed to identify and mitigate risks of current and future tropical storms impacting the railroad infrastructure along the Gulf of America. The study focused on two critical track segments owned and operated by CSX Transportation and CN railway in the Northern Gulf of America (NGOA). The research team developed a comprehensive, risk-based framework to estimate the impact of storm-induced surge and wave hazards on coastal railroads along the NGOA coastline. The research team integrated a physics-based high-fidelity hydrodynamic model to simulate the storm surge and wave generated by tropical storms. The simulations projected future storms and data adjustments for the year 2050. The research team integrated the probabilistic hazard model with a statistical model that assessed the failure probability of railroad components to determine the probability of failure of tracks and bridges due to ballast and embankment scour and the uplift of ballasted bridge decks. The failure probabilities were used within a statistical analysis to determine expected life of tracks and bridges and to devise mitigation strategies that can increase the expected life of coastal tracks and bridges.]]></description>
      <pubDate>Sat, 03 Oct 2026 18:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784736</guid>
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    <item>
      <title>Railroad Information Sharing Environment Data Steward: Phase II</title>
      <link>https://trid.trb.org/View/2784734</link>
      <description><![CDATA[Building on the success of Phase I, Phase II of the Federal Railroad Administration (FRA) sponsored Railroad Information Sharing Environment (RISE) established collaborative relationships among rail stakeholders in class I freight, passenger and short line railroads, in addition to labor groups, state departments of transportation (DOTs) and railroad safety special interests groups with support from the Volpe Transportation Systems Center and RISE third-party data steward, the University of Maryland – Center for Advanced Transportation Technology Laboratory (CATT Lab). This Phase, conducted between September 2021 and February 2025, developed Issue Analysis Teams (IATs) to pursue railroad safety topics identified by FRA, Volpe, and railroad stakeholders, with the goal of improving railroad safety. Two IATs were established around the topics of rail property trespassing and vertical profiles of highway-rail grade crossings. This report will discuss the development of these IATs and three RISE products, a Grade Crossing Planar Deviation Dashboard, Grade Crossing Vehicle Simulation Model, and a Non-Injury Railroad Property Trespassing Collection Form that were developed with the direction of RISE stakeholders.]]></description>
      <pubDate>Sat, 03 Oct 2026 18:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784734</guid>
    </item>
    <item>
      <title>Railroad Information Sharing Environment (RISE): Program Attributes, Lessons Learned, and
Opportunities for Expanded Future Impact</title>
      <link>https://trid.trb.org/View/2784733</link>
      <description><![CDATA[The Railroad Information Sharing Environment (RISE) is a voluntary, non-regulatory, non-punitive, data-driven safety partnership consisting of railroad stakeholders, including the Federal Railroad Administration (FRA), to advance railroad safety. Through a data trust, railroad stakeholders voluntarily share data and work collaboratively to address complex railroad safety challenges. Between October 2024 and March 2025, FRA contracted MITRE Corporation (MITRE) to review the RISE program to assess and document lessons learned and identify opportunities to expand the data trust and the impact of RISE. The research team reviewed RISE program documentation, interviewed RISE stakeholders, and assessed the functionality of RISE in relation to similar programs operating a data trust.]]></description>
      <pubDate>Sat, 03 Oct 2026 18:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784733</guid>
    </item>
    <item>
      <title>Machine Learning-based Analysis for Railway Track Maintenance</title>
      <link>https://trid.trb.org/View/2784735</link>
      <description><![CDATA[Understanding and mitigating track degradation rates (TDRs) is crucial for ensuring the safety and efficiency of railway systems, as well as for controlling maintenance costs and preserving ride quality. The Federal Railroad Administration (FRA) sponsored the University of Massachusetts Amherst and Loram Technologies, Inc. to extensively analyze TDR over 280 miles of passenger revenue track using data from 2011 to 2021. Using a large dataset was pivotal, as it enhanced the robustness of the analysis, allowing for more accurate and reliable conclusions. The researchers used ground penetrating radar (GPR) and light detection and ranging (LiDAR) along with geometry data to examine subsurface and drainage conditions of the tracks. The project employed two distinct methods to estimate TDR, each offering unique insights and applicability based on available historical data and desired precision. These complementary approaches provide the industry with versatile tools for estimating TDR and improving maintenance scheduling, emphasizing their reliability and adaptability.]]></description>
      <pubDate>Sat, 03 Oct 2026 18:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784735</guid>
    </item>
    <item>
      <title>FRA DOTX-216 Passenger Vehicle Characterization and Modeling</title>
      <link>https://trid.trb.org/View/2784737</link>
      <description><![CDATA[Transportation Technology Center, Inc. (TTCI), now MxV Rail, performed work funded by the Federal Railroad Administration to characterize and model the high-speed passenger vehicle DOTX-216 car. The objectives of this project were to develop a list of parameters necessary to complete a dynamic model of the passenger vehicle, perform tests to measure the parameters, develop a mathematical model using the measured parameters, and perform on-track tests to validate the model. TTCI performed these tasks and validated the model of the DOTX-216 car using a combination of validation methods. This report describes the procedures, results, and model validation analysis for the DOTX-216 car.]]></description>
      <pubDate>Sat, 03 Oct 2026 18:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2784737</guid>
    </item>
    <item>
      <title>Characterizing Adaptive Capacity for Rail Transportation Resilience [dataset]</title>
      <link>https://trid.trb.org/View/2780453</link>
      <description><![CDATA[This study involved conducting over 40 interviews to explore impediments and enablers associated with rail transportation adaptation and resilience. In accordance with the study’s IRB protocol, raw audio recordings and transcripts will not be made available. The primary products of this research include a finalized thematic codebook, a series of spreadsheets (crosstabulations), and general metadata for interviewees. Excel files include aggregated thematic summaries from the coding process, such as impediments and strategies categorized by agency type and region. Additionally, a thematic hierarchy chart is provided in PDF format to illustrate the relationship between research concepts. The semi-structured interview guide used in this research is also available in PDF format.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780453</guid>
    </item>
    <item>
      <title>Progress Toward Safer Rail Tank Cars Transporting Flammable Liquids: 2026 Report</title>
      <link>https://trid.trb.org/View/2780508</link>
      <description><![CDATA[This annual report addresses Section 7308(b), (c), and (f) of the Fixing America’s Surface Transportation (FAST) Act [Public Law 114–94] by summarizing industry-wide progress toward implementing mandated rail tank car–safety upgrades between 2013 and 2025 by type of rail tank car and flammable liquid—as defined by the Association of American Railroads.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780508</guid>
    </item>
    <item>
      <title>Harnessing Machine Learning for Railway Damage Assessment and Prediction</title>
      <link>https://trid.trb.org/View/2678439</link>
      <description><![CDATA[Railway systems in coastal regions face distinct challenges due to environmental factors such as coastal erosion, hurricanes, and rising sea levels. Coastal erosion undermines railway embankments, resulting in structural instability and heightened maintenance costs. Hurricanes and storm surge present further risks, with high winds and flooding threatening to damage rail infrastructure, disrupt services, and compromise public safety. To address these vulnerabilities, railway systems in these areas require advanced engineering solutions capable of mitigating the effects of extreme weather events while ensuring operational continuity. This thesis seeks to leverage machine learning techniques to assess and quantify damage to railway infrastructure following major storm events, providing valuable insights to inform the design and maintenance of coastal railway systems. By collecting and processing data on railway characteristics, storm events, and resulting damage, a predictive machine learning model was developed to estimate future storm-related damage. The model achieved an accuracy of approximately 84%, demonstrating its potential for practical application. Additionally, three hypothetical scenarios were developed to evaluate the model’s performance by varying key input parameters. The findings of this study offer critical guidance for railway stakeholders in making informed decisions regarding the reinforcement of coastal railways and pave the way for further research and model refinement.]]></description>
      <pubDate>Fri, 02 Oct 2026 16:48:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2678439</guid>
    </item>
    <item>
      <title>Assessment of Expanded Clay Aggregate for Use in Railroad Embankments</title>
      <link>https://trid.trb.org/View/2678490</link>
      <description><![CDATA[Ground settlement is a major challenge in railroad embankment construction and widening, threatening both track stability and nearby infrastructure. Settlement arises from two mechanisms: immediate elastic compression of soils and longer-term consolidation. To mitigate these risks, lightweight aggregates (LWAs), such as expanded clay, offer a promising solution by reducing embankment load and associated settlement. Expanded clay is nearly half the weight of conventional aggregates, with high internal friction angle and excellent drainage, enhancing stability and reducing hydrostatic pressure. However, its long-term performance under cyclic train loading remains uncertain. To address this, a laboratory testing program, including large-scale Cyclic Direct Simple Shear (CDSS) tests, was developed to simulate railroad loading conditions and evaluate the durability and deformation behavior of expanded clay. This study aims to validate LWAs as a viable alternative for minimizing settlement without compromising long-term track performance.]]></description>
      <pubDate>Fri, 02 Oct 2026 15:56:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2678490</guid>
    </item>
    <item>
      <title>The Utah Thistle Landslide of 1983: A Historical Perspective</title>
      <link>https://trid.trb.org/View/2678378</link>
      <description><![CDATA[In April 1983, an ancient landslide mass in Spanish Fork Canyon, Utah, re-mobilized, creating a landslide dam in the canyon over 200 ft (61 m) high in less than 30 days. The landslide buried a significant highway corridor, a major railroad line, and dammed the Spanish Fork River, inundating the town of Thistle, permanently displacing all of its residents, and destroying all structures in the town. Losses caused by the landslide resulted in Utah’s first Presidential Disaster Declaration and the designation of the Thistle Landslide as the most economically damaging landslide in US history up to that time. Numerous studies characterized the geological, geotechnical, and hydrological conditions associated with the slide and discussed factors that may have initiated it, without broad agreement on the cause. At least two lawsuits ensued, one of which concluded that a century of excavations at the toe of the ancient slide mass by the railroad aggravated stability and allowed a period of extreme precipitation to trigger the movement. The court awarded damages to the displaced residents. This paper discusses the background, circumstances, and impacts of the Thistle Landslide and summarizes the disagreements over the causes of this damaging event.]]></description>
      <pubDate>Fri, 02 Oct 2026 15:56:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2678378</guid>
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