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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>Survey of State Funding for Public Transportation—Final Report 2026, Based on FY 2024 Data</title>
      <link>https://trid.trb.org/View/2696145</link>
      <description><![CDATA[This annual report provides a snapshot of state-by-state investment in public transportation from federal, state, and local funding sources. With detailed tables and charts, the report explains how different funding and tax mechanisms are used to support transit operations and capital projects; compares differences across modes and the latest ridership trends; and features a selection of innovative state funding initiatives and case studies that highlight the efforts by states to apply state funding to support transit programs beyond federal funding levels. All funding and ridership data has been updated to reflect FY 2024 survey results. This year’s report includes new information on commuter rail, new assessments of ridership trends by transit modes, and information on the continuing impact of the COVID-19 pandemic on state transit programs.]]></description>
      <pubDate>Tue, 05 May 2026 10:18:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696145</guid>
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    <item>
      <title>Informational Guide on Data Collection and Annual Average Daily Traffic (AADT) Estimation for Non-Federal Aid-System (NFAS) Roads</title>
      <link>https://trid.trb.org/View/2691363</link>
      <description><![CDATA[This is an Informational Guide on traffic data collection and estimation of annual average daily traffic (AADT) for non-Federal aid-system (NFAS) roads. NFAS roads refer to rural minor collectors (6R) and both rural and urban local roads (7R and 7U). This Informational Guide describes four preparation steps for safety data integration and an eight-step process for developing a random stratified sampling scheme and AADT estimates for NFAS roads. The intent of the Informational Guide is to assist both experienced traffic monitoring personnel and those who are new to traffic data collection and AADT estimation. The Informational Guide includes methods suitable for agencies that do not collect data nor estimate AADT for NFAS roads, as well as for those desiring to improve their practices and the accuracy of their AADT estimates. Readers are encouraged to take those portions most relevant to their needs to develop a stratification scheme and AADT estimates. Agencies do not have to adopt all the activities described in the Informational Guide.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:22:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691363</guid>
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    <item>
      <title>Off-System Public Roads Annual Average Daily Traffic (AADT) Estimation and Validation Tools: Literature Review and Research Report</title>
      <link>https://trid.trb.org/View/2693724</link>
      <description><![CDATA[This report describes a process for estimating AADT on non-Federal-Aid public roads (collectively called “off-system” roads) in Idaho. This will provide additional data for ITD and regional and local transportation agencies to conduct analysis, and it will meet new federal requirements for estimating AADT on all public roads. The report includes a review of federal guidance, documentation of ITD’s current AADT estimation process, an overview of spatial interpolation via Kriging, a user guide for the Rural AADT Estimation toolbox in Esri’s ArcGIS Pro software environment, and end materials including works cited, a list of independent variables considered, and the Python code used to run the toolbox.]]></description>
      <pubDate>Fri, 17 Apr 2026 10:41:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693724</guid>
    </item>
    <item>
      <title>Geostatistical Innovation for Transportation Planning: The Role of Gaussian Geostatistical Simulation</title>
      <link>https://trid.trb.org/View/2654540</link>
      <description><![CDATA[This study applied a Gaussian geostatistical simulation (GGS) to enhance the estimation of the annual average daily traffic (AADT) on low-volume roads in Montana. Traditional methods such as short-term traffic counts, regression models, manual counts, engineering judgment, seasonal adjustment factors, ratio estimation, and the growth factor method often oversimplify spatial patterns and fail to capture the variability and uncertainty inherent in sparse datasets. The GGS model provides a robust alternative by incorporating spatial autocorrelation to capture the relationships across locations. Its ability to generate stochastic realizations enables a more comprehensive representation of the complex traffic dynamics. This study used data from 2009 to 2016 for AADT thresholds ranging from 400 to 2,000 vehicles per day by applying advanced geostatistical techniques. The approach uses R Studio for variogram modeling, simulation, and spatial visualization to effectively analyze traffic patterns. The results validated the hypothesis that GGS enhances predictive precision and captures spatial variability, which is closely aligned with the simulated and measured AADT values. The analysis of the dataset from Montana showed increasing variability at higher AADT thresholds, while maintaining consistent alignment across metrics. This confirmed the reliability of the model under stable conditions. This study also identified limitations, including underestimation of variability in extreme traffic conditions. These findings highlight the potential of GGS to enhance transportation and infrastructure planning by effectively addressing spatial uncertainties. In addition, it can improve data collection strategies and support informed infrastructure development decisions. Future refinements, such as adaptive modeling and real-time data integration, may further enhance the utility of GGS for traffic management and decision-making.]]></description>
      <pubDate>Wed, 08 Apr 2026 13:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2654540</guid>
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    <item>
      <title>2026 TRB Annual Meeting Highlights: Spotlight on the Future of Transportation</title>
      <link>https://trid.trb.org/View/2681176</link>
      <description><![CDATA[More than 10,500 transportation professionals from around the world gathered in Washington, DC, for the 105th Transportation Research Board (TRB) Annual Meeting, held January 11-15, 2026. Sessions and workshops addressed topics such as digitization and artificial intelligence-machine learning in the rail environment, preventing vessel allision impacts on bridges, and air traffic management modernization. This article includes photographs and highlights from the meeting including awards presented and an introduction to the 2026 chair and 2026 vice chair of the TRB Executive Committee.]]></description>
      <pubDate>Thu, 02 Apr 2026 15:16:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681176</guid>
    </item>
    <item>
      <title>FTA Annual Research Report for FY 2025</title>
      <link>https://trid.trb.org/View/2680159</link>
      <description><![CDATA[This statutorily required annual report provides information on Federal Transit Administration’s Public Transportation Innovation Program (49 U.S.C. § 5312). It includes information on active programs and projects on Research, Innovative Development, Demonstration and Deployment, and Evaluation and Implementation in Fiscal Year (FY) 2025. The report concludes with information on FTA’s Strategic Research Roadmap for FY 2026. Individual project and program descriptions include title, recipient(s),  results, evaluation when applicable, and FTA funding.]]></description>
      <pubDate>Tue, 24 Mar 2026 09:08:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680159</guid>
    </item>
    <item>
      <title>Federal Transit Administration Annual Report to Congress on Technical Assistance and Workforce Development for Fiscal Year 2025</title>
      <link>https://trid.trb.org/View/2680177</link>
      <description><![CDATA[This annual report to Congress provides information on the Federal Transit Administration (FTA) Technical Assistance and Workforce Development Program (49 U.S.C. § 5314) for Fiscal Year (FY) 2025. The primary goals of this program are to provide technical assistance, standards development, human resources training, and workforce development and training projects to improve public transportation services, effectiveness, and efficiency. The report also includes an annual report on frontline workforce activities and trends. The report concludes with proposed priorities for FY 2026 funding.]]></description>
      <pubDate>Tue, 24 Mar 2026 09:08:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680177</guid>
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    <item>
      <title>Toll-Plaza Design: A Case Study of Bengaluru-Chennai Highway</title>
      <link>https://trid.trb.org/View/2659276</link>
      <description><![CDATA[This paper provides a methodology for the design of a toll plaza as per Indian Roads Congress (IRC) guidelines. For this study Nemili toll plaza located on Bengaluru–Chennai highway (NH-48) was chosen. Relevant secondary data pertains to the year 2022 were collected from the National Highway Authority of India (NHAI), Chennai regional office. For the toll able category of vehicles, Average Daily Traffic (ADT) was estimated for the base year 2022. A suitable Seasonal Correction Factor (SCF) was assumed (usually calculated from petrol/diesel sales data) in order to estimate the Annual Average Daily Traffic (AADT). Population, per-capita income and net state domestic product data for the influencing states were collected in time-series and using the suitable state influencing factor, traffic growth elasticity values were arrived. Using the traffic growth elasticity values, base year traffic (i.e. AADT) was projected to the design year 2037. Since stage construction is practiced in India, number of electronic toll bays required for the design year 2030 was calculated based on the traffic corresponding to that year referring the IRC codal recommendations.]]></description>
      <pubDate>Fri, 20 Mar 2026 08:38:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659276</guid>
    </item>
    <item>
      <title>A Decade of Ballast Water Data Submitted to the U.S. EPA: A Trend toward Improved Compliance</title>
      <link>https://trid.trb.org/View/2672674</link>
      <description><![CDATA[To manage the environmental impacts from ballast water and other discharges from ships sailing in waters of the United States, the U.S. Environmental Protection Agency (EPA) put in place the Vessel General Permit (VGP) in 2008. For ships that will install and use a ballast water treatment system (BWTS) to comply with the VGP's requirements for ballast water management, annual reports must be submitted that include the results of sampling and analysis of two types of parameters: bacteria and biocides. From all BWTS, the concentrations of three bacteria must be reported. If a BWTS uses biocides as part of the treatment process, the concentration of relevant biocides and derivatives must also be measured and reported. To date, the United States is the only country to require routine sampling and analysis from ships discharging ballast water into federal waters. We analyzed data submitted to the EPA for a decade (from 2014 to 2023), representing nearly 18,000 reports. First, data were "cleaned", e.g., to remove duplicate reports. Surprisingly, nearly one quarter (23 %) of the reports did not include the required bacteria data. Evaluating the data showed that non-compliance with the parameters' limits was relatively low, <3 % for either of the bacteria (Escherichia coli and enterococci; total heterotrophic bacteria results are only reported, as no limit is stipulated for them). Non-compliance for biocides was <10 %. Evaluating non-compliance of both bacteria and biocides showed more consistency: <2 % of reports were non-compliant for both parameters. Encouragingly, compliance has improved over time, with the best (lowest) non-compliance rates in recent years (2020-2023).]]></description>
      <pubDate>Mon, 23 Feb 2026 11:23:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672674</guid>
    </item>
    <item>
      <title>Developing a Data Fusion Tool for Improved Traffic Crash Exposure
Analysis and Modeling</title>
      <link>https://trid.trb.org/View/2663603</link>
      <description><![CDATA[Accurate measurement of exposure is critical for understanding and preventing traffic crashes, as crash frequency is directly related to how much road users are exposed to risk. However, current exposure estimates rely on data sources with complementary but individually insufficient characteristics. Traditional traffic counts and Annual Average Daily Traffic (AADT) offer high accuracy but limited spatial and temporal coverage, while emerging Location-Based Services (LBS) data provide high-resolution mobility patterns but are often biased and less reliable. This fundamental mismatch between accuracy and coverage prevents agencies from developing the complete and reliable exposure estimates needed for effective safety analysis and planning.
This project develops a data fusion tool that integrates traffic counts and AADT, LBS data, and socio-demographically representative survey data from the National Household Travel Survey (NHTS) into a unified measure of exposure. Unlike previous efforts that focused on a single travel mode or low temporal resolution, the proposed framework generates exposure estimates for motor vehicles, pedestrians, bicyclists, and scooters at fine spatial scales (intersection and mid-block) and temporal scales (daily and monthly). The tool is evaluated in Washington, D.C., using three alternative fusion paradigms: Bayesian fusion through hierarchical or state-space modeling, Dempster–Shafer theory for explicit uncertainty representation and accommodation of LBS coverage gaps, and model-based fusion employing structured error modeling with NHTS socio-demographics to correct LBS data bias.
The fusion methods are compared through crash prediction models estimated with fused exposure measures against models using individual data sources, evaluated via pseudo-R², AIC, BIC, and out-of-sample prediction error, with a target improvement of at least 10% in predictive performance. Fused exposure patterns are further validated against Washington, D.C.’s High Injury Network and independent ground-truth count data where available. The final tool is delivered as an open-source Python package with documentation and secure coding practices. Agency outreach, including engagement with D.C. stakeholders managing the High Injury Network, informs tool refinement and supports preparation for future pilot deployment. This research supports USDOT’s Safety priority by generating more accurate and complete multimodal exposure measures that enable better identification of high-risk locations, improved crash prediction, and targeted safety interventions
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:31:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663603</guid>
    </item>
    <item>
      <title>Annual Report on FAST Action Section 3006(b) Pilot Program for Innovative Coordinated Access and Mobility Fiscal Year 2025</title>
      <link>https://trid.trb.org/View/2652167</link>
      <description><![CDATA[This report provides an update on projects selected pursuant to five Federal Transit Administration (FTA) Notices of Funding Opportunity (NOFOs) (81 FR 17549, 83 FR 46534, 84 FR 58819, 86 FR 55907, and 88 FR 78457) for Section 3006(b) of the Fixing America’s Surface Transportation Act (FAST), Public Law 114-94, Pilot Program for Innovative Coordinated Access, and Mobility (ICAM Pilot Program). The primary purpose of these projects is to find and test promising, replicable public transportation health care access solutions that support the goals of (1) increasing access to care, (2) improving health outcomes, and (3) reducing health care costs. The ICAM Pilot Program, Mobility for All Pilot Program, Access and Mobility Partnership Grants, and Rides to Wellness Demonstration Program are initiatives that build partnerships, stimulate investment, and drive change across the health and transportation sectors to ensure that transportation disadvantaged populations can access non-emergency medical transportation (NEMT) to the health care services they need.]]></description>
      <pubDate>Wed, 28 Jan 2026 08:51:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652167</guid>
    </item>
    <item>
      <title>2025 Cooperative Research Programs Annual Report</title>
      <link>https://trid.trb.org/View/2656285</link>
      <description><![CDATA[The 2025 Cooperative Research Programs Annual Report highlights progress and provides an overview of the National Cooperative Highway Research Program (NCHRP), Transit Cooperative Research Program (TCRP), Airport Cooperative Research Program (ACRP), and the Behavioral Traffic Safety Cooperative Research Program (BTSCRP). In addition, the report outlines the Cooperative Research Program's history and structure, mission and vision, ongoing process improvement initiatives, and research themes or focus areas. For each research program information includes: oversight committee members; program history and mission; program financial report; role of sponsors/funding agencies; accomplishments and updates; current and pending projects with contract amount, status, start and end dates; and program publications.]]></description>
      <pubDate>Wed, 21 Jan 2026 10:46:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656285</guid>
    </item>
    <item>
      <title>Crash analysis for urban road networks: Semi-parametric spatial negative binomial models with monotonic constraints</title>
      <link>https://trid.trb.org/View/2633488</link>
      <description><![CDATA[Spatial heterogeneity constitutes an essential element in advanced crash frequency modeling frameworks. However, current spatial methodologies exhibit two primary limitations: (1) inflexible parametric formulations of exposure variables, and (2) non-conjugate model structures that compromise Gibbs sampler convergence. To address these issues, the authors develop a semi-parametric spatial count model that introduces flexible exposure specifications, requiring only monotonic functional relationships while explicitly incorporating spatial dependence. This framework employs data-augmented Gibbs sampling to achieve computationally efficient Bayesian estimation through conjugate forms. Simulation results verified that the estimation works as intended. Empirical evaluations across Houston and Dallas urban road networks demonstrated significant performance advantages relative to conventional approaches. Integration of estimates with roadway characteristics and exposure metrics yielded substantive insights into crash patterns within adjacent roadway segments. Road crash analysis in both cities showed three key patterns. First, segment length consistently serves as an offset variable, while annual average daily traffic (AADT) displays complex, segment-varying relationships due to unobserved heterogeneity. In Houston, the effect of AADT on crashes weakens under low traffic volumes but becomes nearly proportional (offset-like) at medium-to-high volumes. Conversely, Dallas exhibits more linear patterns, suggesting city-specific heterogeneity. Second, compared to one-way highway design, other designs generally pose higher risks, with lane number, width, and roadbed width increasing risk to different degrees, while better median designs reduce risk. Finally, both cities exhibit strong spatial heterogeneity in crash count. This framework enables transportation agencies to prioritize road safety interventions through precise quantification of crash risk factors and spatial heterogeneity, while avoiding restrictive parametric assumptions.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633488</guid>
    </item>
    <item>
      <title>Improving the Accuracy of the Spatial Transferability of Direct-demand Models for Bicycle Volume Estimation at Intersections</title>
      <link>https://trid.trb.org/View/2642377</link>
      <description><![CDATA[Direct-demand (DD) models are used to estimate bicycle exposure (typically expressed as annual average daily bicycle volume [AADB]) when observed counts are unavailable at a site. The DD models typically estimate exposure as a function of site characteristics, such as the geometry, surrounding land use, and sociodemographic characteristics. Developing a DD model requires observed volume counts and associated site characteristics data for many sites in the target jurisdiction. In the absence of this data, it is desirable to apply a DD model from another jurisdiction. However, the naïve transferability of DD models results in AADB estimates with large errors. This paper investigates the use of calibration methods to enhance the spatial transferability of DD models to estimate bicycle volumes at intersections. This paper examined five DD models across four jurisdictions: (1) City of Milton (52 sites); (2) City of Toronto (28 sites); (3) Region of Waterloo (158 sites) in Canada; (4) Pima County (70 sites); and (6) Arizona, US, covering a total of 308 sites. Five local calibration techniques were evaluated for their effectiveness in mitigating errors in naïve estimates. The findings indicate that calibration, particularly regression-based methods, significantly improves the accuracy of the AADB predictions, with calibration Model 3 being the most effective for jurisdictions with less than 80 count sites (k<80). For k=5, Model 3 reduced error by 56%, and Models 3 and 5 achieved up to an 80% error reduction at k=30. As more sites became available, Model 5 emerged as the superior calibration method for k≥80.]]></description>
      <pubDate>Thu, 18 Dec 2025 11:53:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642377</guid>
    </item>
    <item>
      <title>Guidelines for Obtaining AADT Estimates from Non-Traditional Sources</title>
      <link>https://trid.trb.org/View/2635460</link>
      <description><![CDATA[This document provides decision making guidance that State DOTs and local transportation agencies can use to plan and execute purchases of traffic volume estimates, such as annual average daily traffic (AADT), from the private sector when those estimates are based on non-traditional data sources, such as vehicle probe or smartphone data. It also provides guidance for validating the quality of such estimates. This report replicates some of the content but adds new guidance and expands on the methods of FHWA Publication FHWA-PL-21-031. As such, this document supersedes FHWA-PL-21-031.]]></description>
      <pubDate>Tue, 16 Dec 2025 17:07:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635460</guid>
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