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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>Strengthening Cross-Disciplinary Collaboration Throughout Project Development and Delivery</title>
      <link>https://trid.trb.org/View/2768407</link>
      <description><![CDATA[Project development/design and construction activities at the Kentucky Transportation Cabinet (KYTC) tend to be siloed. During project design and development, and construction personnel have few if any opportunities to provide input. And once construction begins, project managers and designers have little involvement. The lack of sustained engagement has multiple consequences. Contract plans prepared without construction input can be difficult to build in the field. Similarly, if designers and project managers proceed without early feedback from construction subject matter experts and do not participate during the construction phase, they will not gain knowledge of how to minimize or mitigate constructibility issues that contractors sometimes confront on project sites. They will also have little role in finalizing as-built plans, which are integral to guiding future maintenance and construction efforts. The absence of cross-disciplinary collaboration throughout project development and delivery also undercuts quality management and can leave key risks undetected (e.g., complex traffic control scenarios, construction spanning multiple seasons), and thus unaddressed and un-mitigated. Resolving these challenges requires putting into place integrated workflows that foster cross-disciplinary dialogue and reciprocity throughout the project life cycle.]]></description>
      <pubDate>Wed, 26 Aug 2026 17:04:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2768407</guid>
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    <item>
      <title>Development and Logistics Technical Program Plan: Engines and Fuel Systems</title>
      <link>https://trid.trb.org/View/2736763</link>
      <description><![CDATA[During the past 20 years, much progress has been made to advance commercial and general aviation in concert with advancements in engine technology. Recent demanding requirements for engines consuming less fuel, producing significantly less noise and pollution, and insuring maximum dependability in airline service have brought about rapid development of advanced turbo-machinery designs to achieve higher thrust and higher efficiency. The introduction of these newer technology propulsion systems into commercial service must be achieved with an equal or better propulsion safety and reliability than existing capability. The definition of these advanced air-breathing engine system configurations and the desired state-of-the-art advancements, requires research and development efforts of components, materials, and structures techniques applicable to these engines, to be modified as needed to adapt to lessons learned and changing needs. The National Transportation Safety Board (NTSB) continues to report that a significant number of general aviation accident types are directly or indirectly related to the propulsion system. However, Federal Aviation Administration (FAA) review of the details of these reported incidents indicates that only five percent of the accidents are propulsion oriented, per se. The FAA Technical Center will establish programs designed to understand and reduce the propulsion system related percentage. The FAA Headquarters and Regions will be continually updated on such progress. This Propulsion Safety Engineering and Development Program Plan has been formulated to accomplish these goals.]]></description>
      <pubDate>Wed, 26 Aug 2026 12:15:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2736763</guid>
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    <item>
      <title>Roadway Friction Screening and Measurement with Automated Vehicle Telematics and Control: Data Management Plan</title>
      <link>https://trid.trb.org/View/2735674</link>
      <description><![CDATA[This Data Management Plan (DMP) provides a framework for managing the data that are expected to be generated from the research project titled “Roadway Friction Screening and Measurement with Automated Vehicle Telematics and Control ”. This DMP is a living document that will be continuously reviewed and updated throughout the research project’s data lifecycle. In this research project, the data collected will include regular vehicles (RVs) and automated vehicles (AVs) trajectory data, RVs use onboard recorders connected to the CAN BUS for tracking wheel slip and axle metrics. AVs capture friction data from IMUs, LiDAR, global positioning system (GPS), and etc. Data includes the real-time vehicle positions, velocities, vehicle types, and the dynamic information of traffic scenarios; and simulation experiments outcomes from the case studies. The data collected will be objective, from vehicle data bus, like Controller Area Network (CAN), radar measurements, camera, high-precision GPS and GNSS (Global Navigation Satellite System) systems and etc. The project aims to collect data to enhance pavement friction measurement accuracy through sensor fusion and cooperative perception (e.g., pooling data from multiple sources), including connected RVs and AVs, as well as roadside data. In this project, we will use vehicle data bus recording, radar detection and GPS real-time data for objective analysis and video recordings for supplementary information about surrounding environment.]]></description>
      <pubDate>Thu, 13 Aug 2026 15:07:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735674</guid>
    </item>
    <item>
      <title>Precise Connected Automated Vehicle Motion Planning with V2I Passive System: Data Management Plan</title>
      <link>https://trid.trb.org/View/2727309</link>
      <description><![CDATA[The project merged a recently developed vehicle-to-infrastructure (V2I) passive pavement sensing system for secondary lane departure warning with an existing connected automated vehicle (CAV) platform to maintain vehicle position in the lane in all road and weather conditions and provide localized motion planning to maximize passenger comfort. This data management plan provides (1) details of the dataset that will be collected as part of the CAV deployment and testing efforts in the project, (2) data format and metadata standards, (3) access policies, (4) policies for re-use, redistribution and derivatives, and (5) plans for archiving and preservation.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727309</guid>
    </item>
    <item>
      <title>Developing Tools to Mitigate the Impact of Design Errors and Omissions</title>
      <link>https://trid.trb.org/View/2717375</link>
      <description><![CDATA[The main objectives of this research are to quantify the impact of design errors and omissions (E&Os) in Wyoming Department of Transportation (WYDOT) highway construction projects, identify their causes, propose mitigation strategies, develop tools to minimize the adverse effects of E&Os, and create plans and procedures for effective management. This will be achieved through analyzing change order data and insights from DOT professionals by utilizing questionnaire surveys and semi-structured interviews. This report recommends an updated design review checklist for WYDOT to assist new engineers. It identifies communication issues as a major cause of design E&Os and highlights that plan and estimate errors increase project costs and change orders. To improve coordination, the report advocates for introducing real-time collaboration platforms. It also stresses the differing perspectives of design and construction staff on causes, impacts, and the use and effectiveness of design quality control tools. Finally, this research provides an updated policy and procedure document to handle design E&O.]]></description>
      <pubDate>Thu, 02 Jul 2026 11:04:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717375</guid>
    </item>
    <item>
      <title>Large-Scale Airline Crew Recovery Using Mixed-Integer Optimization and Supervised Machine Learning</title>
      <link>https://trid.trb.org/View/2686231</link>
      <description><![CDATA[Airlines take a variety of actions to recover schedules of their aircraft, crew, and passengers from operational disruptions. Aircraft are typically recovered first, followed by crew recovery, and then passenger recovery. This paper aims to repair disrupted crew schedules while ensuring the feasibility of previously decided aircraft recovery plans and indirectly accounting for passenger disruption costs. We develop a fast solution approach that effectively combines mixed-integer optimization and supervised machine learning (ML) methods to find high-quality solutions to large-scale recovery problems. Our approach reduces the solution space by adding constraints based on the patterns discovered in the solutions to offline instances. The model with the added constraints is solved using a mixed-integer optimization solver. To account for the fact that the available time for airlines to handle disruptions may vary during the day of operations, our solution approach allows parameter tuning to flexibly match the extent of solution space reduction to the available runtime. This helps the proposed method to effectively navigate the trade-off between solution quality and runtime. Extensive computational experiments with actual flight and crew schedules of a major U.S. airline with more than 2,800 daily flights show that our approach consistently generates solutions of significantly higher quality than benchmarks and is estimated to provide tens of millions of dollars of reduction in annual operating costs. Moreover, our ML models have interpretable structures that are critical to enhance end-user trust in the ML recommendations. Finally, our approach yields solutions that are more robust to uncertainty in delay prediction than those found by direct optimization..]]></description>
      <pubDate>Tue, 30 Jun 2026 17:03:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686231</guid>
    </item>
    <item>
      <title>SMART METRO: Data Management Plan</title>
      <link>https://trid.trb.org/View/2717067</link>
      <description><![CDATA[With support from the U.S. Department of Transportation (USDOT) SMART Grant Stage 1 Prototyping Grant, the Broward Metropolitan Planning Organization (BMPO) is developing SMART METRO, an innovative regional application of systems integration to create and implement an efficient foundational digital twin platform. Accelerating threats to infrastructure, housing costs, congestion impacts, and population growth pose significant risks to Broward County, Florida. SMART METRO will integrate data science, geospatial analytics, and simulation tools and models for efficient transportation, infrastructure hardening, economic development, and land-use analysis. This data management plan describes: the data collected or used during the project; data format and metadata standards employed; data access policies; re-use, redistribution, and derivatives products policies; and the archiving and preservation plan.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:11:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717067</guid>
    </item>
    <item>
      <title>Lessons from High-Speed Level-5 AV Racing: Data Management Plan</title>
      <link>https://trid.trb.org/View/2716614</link>
      <description><![CDATA[This Center for Connected and Automated Transportation (CCAT) Data Management Plan (DMP) provides a framework for managing the data that are expected to be generated from the research project titled “Lessons from High-speed Level-5 AV Racing” that was awarded through the center. For this project, data will be collected on high-speed racing vehicle performance in a field environment and human subject attributes in a driving simulation environment. This document discusses the key elements of Purdue CCAT’s Data Management Plan for this project, namely, data description, data format and metadata standards, access policies, policies for re-use, redistribution, derivatives, and plans for archiving and preservation.]]></description>
      <pubDate>Wed, 24 Jun 2026 17:03:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2716614</guid>
    </item>
    <item>
      <title>Effectiveness of Inductive Vehicle Charging to Alleviate EV Range Anxiety: Data Management Plan</title>
      <link>https://trid.trb.org/View/2709411</link>
      <description><![CDATA[This project, titled "Effectiveness of Inductive Vehicle Charging to Alleviate EV Range Anxiety", will evaluate the efficacy of inductive vehicle charging (IVC) in overcoming range anxiety for different electric vehicle (EV) users. It systematically categorizes different passenger and freight transportation user groups and investigates their use cases where these various users can reap benefits from IVC implementation. Considering different EV user groups, this proposal provides proof of concept for locations or scenarios in which IVC technology effectively removes range anxiety for light to heavy-duty vehicles. In addition, the proposal investigates the current IVC technology characteristics to assess the cost of IVC implementation and identify installation and maintenance requirements. The data collected during this project is from the literature, meeting with IVC technology manufacturer(s), and gathering information from the IVC pilots. This data management plan describes the data that will be collected and how it will be stored, accessed, and archived. All input data for the models will be stored in ASCII text files.]]></description>
      <pubDate>Thu, 11 Jun 2026 13:20:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709411</guid>
    </item>
    <item>
      <title>Philadelphia Digital ROW and Mobility Improvement SMART Grant Project: Data Management Plan</title>
      <link>https://trid.trb.org/View/2705983</link>
      <description><![CDATA[Through the Digital Right-of-Way (ROW) and Mobility Improvement Project, the City of Philadelphia developed and implemented a set of new technologies to test digital management of the right-of-way (ROW). Traditionally, as in other cities, Philadelphia issues regulations on how roadways and sidewalks may be used or blocked, and posts physical signs that users must read, interpret, and follow to use the street safely and legally. The Smart Cities team, in partnership with the Streets Department, Open Mobility Foundation, and several vendors, tested communicating these right-of-way regulations and closures digitally, such that a user’s phone or vehicle could receive them, parse out relevant information for specific location/day/time and turn it into guidance for the user - for instance, should the user park or not, and if a user is blocking a bike lane or not. This is the data management plan for the Digital Right-of-Way (ROW) and Mobility Improvement Project. Data will include: information about the extent, granularity, and recency of the City's existing (at project start) RoW or RoW-related datasets; information about physical and policy elements of the RoW that are or were missing from the City's existing RoW or RoW-related datasets; maps (Shapefile, geoJSON) of the RoW; a data topology for all potential RoW elements, including physical features and policies of or relating to the sidewalk, curb, and street; a report on City processes that interact with, impact, or depend upon the actual status of, or information about, the RoW, the automated or digitized (or manual or analog) nature of each City process of or relating to the RoW along with recommendations for automation and integration of the latter; all RoW related datasets, in Shapefile, geoJSON, and JSON formats, necessary for the building of applications for: ingesting geospatial data about the RoW; RoW management; RoW information dissemination to smart devices through existing apps or apps to-be-developed; RoW information dissemination to digital signages such as E Ink-based platforms; data about the efficacy of Transit Signal Priority Enhancement technologies; data about uptime/downtime of all deployed technologies.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:02:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705983</guid>
    </item>
    <item>
      <title>Probabilistic Mission Design for Neuro-Symbolic Unmanned Aircraft Systems</title>
      <link>https://trid.trb.org/View/2659041</link>
      <description><![CDATA[Advanced Air Mobility (AAM) is a growing field that demands accurate and trustworthy models of legal concepts and restrictions for navigating Unmanned Aircraft Systems (UAS). In addition, any implementation of AAM needs to face the challenges posed by inherently dynamic and uncertain human-inhabited spaces robustly. Nevertheless, the employment of UAS beyond visual line of sight (BVLOS) is an endearing task that promises to significantly enhance today’s logistics and emergency response capabilities. Hence, we propose Probabilistic Mission Design (ProMis), a novel neuro-symbolic approach to navigating UAS within legal frameworks. ProMis is an interpretable and adaptable system architecture that links uncertain geospatial data and noisy perception with declarative, Hybrid Probabilistic Logic Programs (HPLP) to reason over the agent’s state space and its legality. To inform planning with legal restrictions and uncertainty in mind, ProMis yields Probabilistic Mission Landscapes (PML). These scalar fields quantify the belief that the HPLP is satisfied across the agent’s state space. Extending prior work on ProMis’ reasoning capabilities and computational characteristics, we show its integration with potent machine learning models such as Large Language Models (LLM) and Transformer-based vision models. Hence, our experiments underpin the application of ProMis with multi-modal input data and how our method applies to many AAM scenarios.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659041</guid>
    </item>
    <item>
      <title>Phase 3 Data Management Plan (DMP) Heart of Iowa Regional Transit Agency ITS4US Deployment Project</title>
      <link>https://trid.trb.org/View/2701100</link>
      <description><![CDATA[Heart of Iowa Regional Transit Agency (HIRTA) is one of four awardees of the ITS4US Phase 2/3. The deployment site for this project is Dallas County, located in central Iowa, west of Des Moines. This deployment will provide enhanced transportation access to healthcare options for all travelers in Dallas County. The project will deliver a solution to provide transportation access to healthcare facilities located in Dallas County using HIRTA and its contractor vehicles. The solution will enable coordination among HIRTA and its partners (e.g., Dallas County Health Department, healthcare providers, State of Iowa Medicaid transportation broker, funding entities). In Phase 1, the deployment concept for the Health Connector application was developed. In Phase 2, the project shifted from establishing a conceptual framework to design and testing of the system. Phase 2 consisted of system architecture and design, data management planning, procurement of a Mobility-On-Demand (MOD) vendor, middleware development, and systems testing. In Phase 3, Health Connector is being deployed and pilot operations are being evaluated. The Data Management Plan (DMP) documents the data that are needed to deliver and evaluate Health Connector services, how those data will be collected, the roles of different stakeholders in creating, storing, managing, exchanging, and using those data, the data standards that the project team will use throughout, and how the data under discussion relate to the goals of the ITS4US program.]]></description>
      <pubDate>Thu, 28 May 2026 16:15:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701100</guid>
    </item>
    <item>
      <title>Hardening the Economical Acquisition of Intersection Data to Improve System Integrity Data Management Plan</title>
      <link>https://trid.trb.org/View/2694452</link>
      <description><![CDATA[This Center for Connected and Automated Transportation (CCAT) Data Management Plan (DMP) provides a framework for managing the data that are expected to be generated from the research project titled “Hardening the economical acquisition of intersection data to improve system integrity” that was awarded through the center. This document discusses the key elements of Purdue CCAT’s Data Management Plan for this project, namely, data description, data format and metadata standards, access policies, policies for re-use, redistribution, derivatives, and plans for archiving and preservation. The project will generate large-scale datasets that include vehicle telemetry data and intersection signal data. Data will be created through both simulated and real-world driving tests at specified intersections in the City of Owosso. Data collection is planned to occur in Summer 2024.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694452</guid>
    </item>
    <item>
      <title>Use of Trajectory Option Sets to Support Collaborative Constraint Propagation</title>
      <link>https://trid.trb.org/View/2680964</link>
      <description><![CDATA[Air traffic flow management is supported by a highly distributed work system in which airline dispatchers and Federal Aviation Administration (FAA) traffic managers must coordinate. To support asynchronous coordination between a dispatcher and a traffic manager, the FAA has developed software that allows the flight operators to submit multiple, prioritized alternative flight plans. This set of alternative flight plans, submitted along with a filed route, is referred to as a Trajectory Option Set (TOS). And some airlines have now developed initial versions of software capable of generating and submitting such TOSs. This paper reports on cognitive walkthroughs with 5 dispatchers and 3 traffic managers on 5 scenarios designed to evaluate the operational concept, procedures and supporting FAA and airline software. The findings provide guidance for application of the concept of collaborative constraint propagation to support distributed work, as well as 42 recommendations for enhancing associated procedures and supporting software designs.]]></description>
      <pubDate>Sat, 02 May 2026 15:47:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680964</guid>
    </item>
    <item>
      <title>Data Management Plan for Tier 1 UTC Center for Healthy and Durable Transportation Data Management Plan</title>
      <link>https://trid.trb.org/View/2694291</link>
      <description><![CDATA[The Center for Healthy and Durable Transportation (CHDT) is a tier-one University Transportation Center (UTC) led by the University of Missouri-Kansas City. The primary research focus area of CHDT is enhancing the durability and service life of transportation infrastructure using innovative construction materials and techniques. Central to this effort is the integration of recycled and repurposed waste materials, which not only diverts significant waste from landfills but also contributes to more resilient infrastructure components. This Data Management Plan (DMP) aims to facilitate the best practices of data documentation and promote the sharing of research results and experimental data across the broad spectrum of stakeholders of the center. This DMP additionally serves as the basis for specific Data Management Plans of the projects that CHDT is sponsoring.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:55:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694291</guid>
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