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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>Future freight and logistics survey: An integrated vehicle-and-shipment-tracking data collection method and a case study in the United States</title>
      <link>https://trid.trb.org/View/2618285</link>
      <description><![CDATA[High-quality freight data are essential for transport planning and decision-making, particularly when conducting freight analysis and modeling. Conventional freight surveys have limitations, such as low data collection efficiency and a lack of detailed shipment and vehicle activities. Moreover, few previous studies have developed an integrated survey methodology to collect high-quality freight data and conducted a practical pilot study to verify its feasibility. To address this research gap, this study provides a systematic approach to collect high-quality data and reduce the burden on both the surveyors and participants. Based on the Future Mobility Sensing (FMS) platform, the survey is implemented. It utilizes mobile sensing devices and wireless communication technologies to capture data on the movements and activities of vehicles and shipments. They are then interpreted into freight diaries using machine learning algorithms. A pilot study was conducted in the United States, where the vehicles and shipments of each participating establishment were continuously tracked for 1–3 weeks. This effort collected high-resolution GPS trajectories and identified 806 vehicle stops with detailed activity data captured at each stop. The 97.27 % (784 out of 806) detection success rate for vehicle stops and the 81.81 % (72 out of 88) success rate for generating shipment timelines have demonstrated the feasibility of this integrated approach. The novelty and key contributions of this study are threefold: (1) It proposes a fully integrated and feasible freight survey methodology that involves all key decision-makers (i.e., shipper, carrier, driver, and receiver) to collect comprehensive freight activity data that reveal actual behaviors. (2) The proposed methodology includes simultaneous tracking of shipments and vehicles, which supports the matching of these flows to reconstruct the complete transport process. The dual-tracking also enables cross-checking when shipment or vehicle data are missing. (3) It introduces a platform-based multi-party collaborative verification mechanism to support all decision-makers in verifying the collected data directly. This enhances both data accuracy and reliability. Overall, the framework involves multiple decision-makers and provides a holistic view of the entire freight process, offering a significant advancement over traditional freight surveys. Moreover, the comprehensive dataset collected through this integrated approach supports the model development, especially for activity-based models and agent-based models, which are essential for evaluating logistics performance and informing freight policy-making.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618285</guid>
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    <item>
      <title>Flight Test of Very High Frequency (VHF) Communication Frequency Antenna Radiation Signal Levels From the Washington National Airport</title>
      <link>https://trid.trb.org/View/2705401</link>
      <description><![CDATA[The purpose of the test flight was to collect signal level data radiated from Very High Frequency communications antennas located at the Washington National Airport (DCA). The data were to provide information on the coverage at a range of 40 nautical miles (nmi) and at 8,000 feet mean sea level (m.s.l.) and all approaches and departures of the airport.]]></description>
      <pubDate>Tue, 16 Jun 2026 09:35:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705401</guid>
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    <item>
      <title>Investigating the Influence of Alternative Survey Participant Recruitment Strategies on Measurement and Inference of Mobility Patterns</title>
      <link>https://trid.trb.org/View/2712615</link>
      <description><![CDATA[There are growing concerns about the representativeness of survey data in an era of rapidly emerging and evolving technology, low response rates, and increasingly diverse and heterogeneous populations. Because of the complexities and costs associated with conducting surveys using traditional mail and phone methods, researchers and practitioners are adopting new methods to sample respondents. This project aims to provide a comprehensive assessment of the representativeness of the samples obtained from three survey sampling strategies utilized in the nationwide COVID Future Panel Survey: convenience sampling, email sampling, and online panel sampling. The three subsamples were statistically different from each other for all socio-economic and demographic variables except race, ethnicity, household size, and gender. However, these differences were ameliorated with the application of weights and the three subsamples converged to census distributions on many variables except educational attainment. Weighting was also able to reduce the differences between the subsamples for a variety of mobility variables except transit use frequency. Modeling the influence of survey sample recruitment strategy on measures of mobility shows that it is significant even after controlling for socio-economic and demographic variables in the model specification. It is likely that the survey sample recruitment strategy variable is accounting for unobserved traits such as attitudes and lifestyle preferences. It is therefore recommended to include attitudinal and lifestyle preference questions in transportation surveys so that these traits can be explicitly included in travel model specifications to enhance explanatory power and reduce bias.]]></description>
      <pubDate>Tue, 16 Jun 2026 07:28:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712615</guid>
    </item>
    <item>
      <title>Research Support to INDOT on Systemwide Asset Condition Assessment Using Connected Vehicle Data</title>
      <link>https://trid.trb.org/View/2712611</link>
      <description><![CDATA[Crowdsourced pavement roughness data using connected vehicles (CVs) now provides cost-effective, high-resolution condition monitoring in near real-time. This study evaluates how connected vehicle data can support effective investment prioritization across Indiana’s road network and improve upon traditional data sources using surveys that are often done annually or biennially. A key challenge is handling terabyte-scale geospatial roughness datasets and integrating them meaningfully into asset management workflows. A scalable data processing methodology is outlined to address this issue and is used for the subsequent analysis. This report’s analysis uses more than 3 billion daily CV-estimated International Roughness Index (IRICVe) measurements from 2022–2025, in addition to multiple visual condition sources: 2024 Pavement Condition Index (PCI) data from Indianapolis and Noblesville and 2025 computer-vision PASER (Pavement Surface Evaluation and Rating) data from Noblesville. Statewide local paved roads show a spatial data coverage increase from 46.5% to 53.2% between 2023 and 2024. Case studies from I-65, Noblesville, and Indianapolis demonstrate the potential of these developing data sources even at the segment and route levels. Additionally, multiple metric comparisons confirm a weak roughness-surface condition correlation (with R² of 0.15 to 0.34) and were used for network-wide screening for outlier detection and quality control. These results support implementation recommendations for using IRI and condition data together for a more comprehensive estimation of pavement quality. Although the evaluated data sources show substantial potential, they are not yet ready to replace traditional data for all use cases.]]></description>
      <pubDate>Tue, 16 Jun 2026 07:28:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712611</guid>
    </item>
    <item>
      <title>Phase 2 Revised Performance Measurement and Evaluation Support Plan (PMESP) – Buffalo NY ITS4US Deployment Project</title>
      <link>https://trid.trb.org/View/2711615</link>
      <description><![CDATA[The Buffalo NY ITS4US Deployment Project seeks to improve mobility to, from and within the Buffalo Niagara Medical Campus by deploying new and advanced technologies with a focus on addressing existing mobility and accessibility challenges. Examples of the technologies to be deployed are electric and self-driving shuttles, a trip planning app that is customized for accessible travel, intersections that use tactile and mobile technologies to enable travelers with disabilities navigate intersections, and Smart Infrastructure to support outdoor and indoor wayfinding. The deployment geography includes the 120-acre Medical Campus and surrounding neighborhoods with a focus on three nearby neighborhoods (Fruit Belt, Masten Park, and Allentown) with underserved populations (low income, vision impaired, deaf, or hard of hearing, wheeled mobility device users and older adults). This document describes the Performance Measurement and Evaluation Support (PMESP) Plan, originally drafted during Phase 1 and now updated during Phase 2 of the Complete Trip Deployment in Buffalo, New York. This PMESP lists the performance measures and targets based on deployment goals and use case scenarios, describes the confounding factors in measurement and proposed mitigation approaches, details the experimental design for each use case, and defines the proposed data collection plan for deployment to ensure the required data is collected for system measurement.]]></description>
      <pubDate>Fri, 12 Jun 2026 16:00:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711615</guid>
    </item>
    <item>
      <title>Flight Loads and Airframe Usage Analysis of Next-Generation Airtankers – CL-415</title>
      <link>https://trid.trb.org/View/2709282</link>
      <description><![CDATA[This report presents the results of an analysis of operational data from a fleet of four CL-415 Super Scooper aircraft flown in support of the United States Forest Service aerial firefighting operations. The aircraft were equipped with IONode100 digital flight data recorders supported by Latitude Technologies Corporation. Data used for this report was collected over the calendar years 2015-2019 and consisted of approximately 4,700 hours of flight time, almost equally divided among the four airframes. The analysis has been limited to ground-air-ground segments of the missions, excluding ground operations. Missions have been divided into three groups: firefighting, ferry, and maintenance/training. Firefighting missions have been further divided into ten flight phases. Airframe usage has been examined for each flight and each phase of the flight. The results have been compared with aircraft limitations on airspeeds, altitudes, and load factors pertaining to individual flap deflections. Unreliable pitch and roll angles have prevented examination of flights in unusual attitudes. All aircraft are shown to have been flown well within the operation altitude limits. Incidents of excessive vertical acceleration and indicated airspeeds, for the corresponding flap deflection, are shown to have been common. Lack of clear indicators, such as weight on wheels, for water landings have prevented clear identification of points of contact with, and departure from, water landings. For airborne phases, vertical load factors due to gust and maneuver have been separated using the two-second rule. Frequency of occurrence of each type has been determined using the method of peaks-between-means. The results have been presented in the form of exceedance spectra per 1000 hours and per nautical mile for various altitude bands. The report is concluded with some recommendations for improved data acquisition for further efforts.]]></description>
      <pubDate>Tue, 09 Jun 2026 10:56:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709282</guid>
    </item>
    <item>
      <title>Test and Evaluation of the Discrete Address Beacon System (DABS) / Moving Target Detector (MTD) / Radar Data Acquisition Subsystem (RDAS)</title>
      <link>https://trid.trb.org/View/2703681</link>
      <description><![CDATA[The primary objectives of testing the Moving Target Detector (MTD) and the Radar Data Acquisition Subsystem (RDAS) as an integral part of the Discrete Address Beacon System (DABS) are to characterize their combined performance in: a. Providing radar/beacon correlation of DABS and Air Traffic Control Radar Beacon System (ATCRBS) targets with radar targets provided as an input to DABS from either the RDAS or MTD; b. Providing improved radar surveillance on aircraft not equipped with a beacon transponder for display and tracking purposes; and c. Providing weather information to an air traffic control (ATC) facility.]]></description>
      <pubDate>Mon, 08 Jun 2026 15:26:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703681</guid>
    </item>
    <item>
      <title>Risk-Based and Cost-Effective Agency Verification of Contractor-Collected Pavement and Bridge Profiles</title>
      <link>https://trid.trb.org/View/2712193</link>
      <description><![CDATA[State departments of transportation (DOTs) recognize that pavement and bridge smoothness is a key indicator of performance and public satisfaction. As state DOT staffing levels have declined, contractors have become increasingly responsible for collecting profile data, calculating smoothness indices, and sometimes determining pay factors. While federal regulations require independent verification of contractor data used for acceptance decisions, agencies remain uncertain about the level of verification needed to ensure accuracy and judicious allocation of public funds.

Current practices for validation and verification vary widely across state DOTs. Some agencies collect independent profiles on a subset of projects, while others rely on partial sampling, comparisons with contractor data, or limited review processes. The statistical reliability and risk implications of these approaches are not well understood. Additionally, advances in data collection technologies, such as high-speed profilers, have increased the volume of data, challenging traditional verification approaches. There is a need for research that helps state DOTs accurately determine pavement life through the potential use of emerging technologies and improved verification of contractor-collected pavement and bridge profile data.

The objective of this research is to develop a guide and supporting tool to assist state DOTs in conducting cost-effective, risk-based verification of contractor-collected pavement and bridge profiles.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712193</guid>
    </item>
    <item>
      <title>Developing Data Literacy Competencies and Practices for State Transportation Workforce</title>
      <link>https://trid.trb.org/View/2712190</link>
      <description><![CDATA[State departments of transportation (DOTs) are undergoing a major transformation in how they collect, manage, and use data. Historically reliant on manual observations and field reports, DOTs now collect large and diverse datasets from traffic monitoring, asset condition assessments, maintenance records, freight compliance, Global Positioning System (GPS) probe data, light detection and ranging (LiDAR), drones, and video analytics. These technologies support more data-driven decisions related to infrastructure management, operations, and planning.

The growing volume and diversity of transportation data have created significant challenges for integration, governance, and analysis. To address these issues, many DOTs are adopting standardized data formats and centralized governance structures that improve interoperability, reduce duplication, and support collaboration with external stakeholders. At the same time, data access has expanded across agencies, allowing planners, engineers, managers, and policy staff to work directly with increasingly complex datasets.

Artificial intelligence (AI) and machine learning applications are accelerating this shift, particularly in areas such as traffic incident detection, pavement performance prediction, asset management, and safety analysis. However, many transportation professionals lack foundational competencies in data governance, statistical reasoning, ethical data use, visualization, and interpretation of analytical outputs. The shortage of qualified data-science personnel within public agencies further increases reliance on undertrained staff and external consultants. Communication gaps between technical teams and transportation practitioners also hinder effective implementation of data-driven tools and practices.

The objective of this research is to improve data literacy within transportation agencies by identifying current skill gaps and workforce needs, evaluating data usage practices, and developing strategies to improve the ability of staff to collect, interpret, manage, and apply data effectively.

This research will identify baseline competencies required for transportation data literacy; examine barriers related to training, governance, and organizational silos; evaluate the impacts of limited data-science staffing; and explore best practices for training, communication, and knowledge management. The study will develop actionable recommendations for tailored training, improved data governance, reduced reliance on external consultants, and stronger data-driven decision-making across transportation agencies.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:01:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712190</guid>
    </item>
    <item>
      <title>Active transportation surveillance in children and adolescents: A global review using Active Healthy Kids Global Alliance Global Matrix 4.0 data</title>
      <link>https://trid.trb.org/View/2704115</link>
      <description><![CDATA[The current review aims to analyze and synthesize the current state of active transport (AT) surveillance and monitoring among children and adolescents globally, leveraging data from the Global Matrix 4.0 to identify gaps and opportunities for improving AT measurement. Two independent researchers systematically reviewed all evidence sources used by 57 countries/jurisdictions to assign AT grades.AT grades were predominantly in the mid-range, with most countries scoring between C- and B+. Over half (54%) of the countries used nationally representative data, although a substantial proportion relied on subnational or mixed sources. AT indicators were based on self-report questionnaires assessing the usual mode of transport (44%) or frequency of travel (34%), most often completed by children and adolescents. A few data sources included items on duration, trip diaries, and previous-day recall. AT assessment was largely restricted to school travel (66%), with minimal inclusion of other destinations. Less than 10% of sources reported any evidence of validity or reliability. The most frequently reported challenges in grade assignment were related to data limitations, methodological inconsistencies, and limited destination focus. Priorities for improvement of AT included infrastructure development, education and awareness initiatives, and urban and transport planning strategies, with consistent patterns observed across country income groups. This review highlights substantial heterogeneity in how countries monitor AT in children and adolescents. The inconsistent constructs, school-centric focus, and lack of reported reliability and validity of the items/tools hinder meaningful comparisons, long-term surveillance, and accurate assessment of children's and adolescents' actual AT behaviors.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704115</guid>
    </item>
    <item>
      <title>SPR-5041: SPR-4517 Implementation: Wireless Data Collection and Model Development</title>
      <link>https://trid.trb.org/View/2709427</link>
      <description><![CDATA[SPR-4517 deployed an edge-enabled, solar-powered wireless monitoring system at the I-69 instrumentation section to evaluate pavement drainage and related performance indicators. This implementation study will sustain field hardware, formalize an automated data pipeline with quality assurance/quality control (QA/QC) protocols, develop and validate performance indicators and predictive models, and package the complete workflow for the Indiana Department of Transportation (INDOT). Outcomes include continued wireless monitoring, versioned data products with monthly health reports, validated drainage performance models, and a transferable implementation package enabling INDOT to maintain long-term field data collection and adopt similar capabilities at additional sites.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:23:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709427</guid>
    </item>
    <item>
      <title>Sensorless Estimation of In-Cabin CO₂ Concentration for Model-Based Air Recirculation Control</title>
      <link>https://trid.trb.org/View/2691886</link>
      <description><![CDATA[This paper introduces a sensorless approach for data-driven modeling of in-cabin CO₂ concentration to optimize air recirculation flap control without the need for a dedicated CO₂ sensor. Elevated CO₂ concentrations, resulting from passenger exhalation, can impair occupants’ cognitive function and comfort. Current state-of-the-art solutions rely either on time-based control strategies, which lack responsiveness to actual cabin conditions, or on direct CO₂ measurements via sensors, which increase system complexity and costs. In contrast, the proposed approach aims to replicate the benefits of sensor-based control without requiring physical sensors. In this study, a model-based methodology is presented, utilizing empirical CO₂ measurement data collected from real-world test drives at varying occupancies, fan stages, vehicle speeds, and flap positions. Data acquisition involves a multi-gas analyzer positioned within the passengers’ breathing zone under controlled operation of the vehicle’s climate control unit. Based on these measurements, time-dependent CO₂ concentration profiles are represented using exponential functions. These regression curves capture CO₂ accumulation, depletion, and balancing behaviors, considering factors such as cabin leakage, pressure differentials at varying speeds, and ventilation conditions. These influences are inherently included in the calibration curves due to their empirical basis. The derived regression curves are implemented into a control model to simulate CO₂ concentration throughout the drive, including situations where outside pollution is high and prolonged air recirculation is necessary – such as when driving through tunnels or behind trucks. On the baseline of this simulation, the sensorless control strategy adjusts flap positions accordingly, thereby minimizing both excessive CO₂ buildup and unnecessary energy losses due to overventilation. By omitting CO₂ sensors and relying solely on existing in-vehicle databus signals, this approach offers a cost-effective solution for cabin air quality management. Future work will focus on real-world validation of the control model and integration of exterior air quality monitoring as a complementary input.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691886</guid>
    </item>
    <item>
      <title>Adaptive Speed-Regulated Impedance Control for Robotic Ultrasound Scanning: Reducing Constant-Force Tracking Errors</title>
      <link>https://trid.trb.org/View/2706206</link>
      <description><![CDATA[Robotic ultrasound scanning technology is a research hotspot in the field of medical imaging, and can achieve standardized and high-precision data acquisition. However, large force tracking errors occur during scanning, especially in complex human tissues, which can severely degrade image quality and diagnostic accuracy. Therefore, we propose an adaptive speed-regulated impedance control strategy to address this challenge, which innovatively combines the spline real-time interpolation and impedance control for constant force tracking. Firstly, the discrete ultrasound scanning paths are fitted to generate a smooth and synchronized interpolation trajectory. Then, the speed of the reference trajectory is adjusted in real time based on the Taylor formula to reduce the force tracking error. Experimental verification was conducted, and the results showed that the force tracking error increases with the increase of trajectory speed. In addition, at high speeds (e.g., 10 mm/s), the mean/variance of the force tracking error of the proposed method (0.3067N/0.2784) is reduced by 31.1%/37.4% respectively compared with the mean/variance of the traditional impedance control (0.4452N/0.4448), fully demonstrating the effectiveness of the proposed control strategy.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:12:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706206</guid>
    </item>
    <item>
      <title>City of Philadelphia Right-of-Way Data Typology and Process Mapping</title>
      <link>https://trid.trb.org/View/2705981</link>
      <description><![CDATA[The City of Philadelphia has embarked on the Philadelphia Digital Right-of-Way and Mobility Improvement Project as part of the U.S. Department of Transportation's Strengthening Mobility and Revolutionizing Transportation (SMART) Grant initiative. A key component of this project is the development of a comprehensive Right-of-Way Data Specification (ROWDS). This report provides an in-depth review of the Right-of-Way Data Specification (ROWDS) that was created as part of this project, how data can be generated in this format, and how it can be applied to City processes. The report is structured as follows: (1) Proposed Right-of-Way Data Specification: provides and overview of the endpoints included in the ROWDS, including policies, events, and physical objects; describes how ROWDS endpoints correspond with each other; (2) Data Proof of Concept: provides an in-depth description of the data collection and conversion process for the pilot area; (3) Current Right-of-Way Permitting process: provides an overview of the City's current Right-of-Way permitting process; discusses the challenges and potential opportunities for improving the Right-of-Way permitting process; (4) Impacts and Improvements to the Permitting Process: provides examples of how the ROWDS can be applied to a variety of previous permit requests received by the City; (5) Next Steps: discusses how data collection can be expanded, how new data can be integrated and maintained, and how ROWDS can be refined in the future.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:02:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705981</guid>
    </item>
    <item>
      <title>Microwave Landing System (MLS) Clearance Format Assessment Tests</title>
      <link>https://trid.trb.org/View/2701096</link>
      <description><![CDATA[This data report documents the Microwave Landing System (MLS) clearance format assessment tests performed at the Federal Aviation Administration Technical Center from January through February 1980. The test data were provided for inclusion in the United States presentation for the International Civil Aviation Organization (ICAO) All-Weather Operation Panel-8 meeting in Montreal in March 1980.]]></description>
      <pubDate>Sun, 31 May 2026 16:45:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701096</guid>
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