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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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      <title>Project 060 Analytical Methods for Expanding the AEDT Aircraft FLEET Database</title>
      <link>https://trid.trb.org/View/2691546</link>
      <description><![CDATA[The goal of this research is to increase the accuracy of Aviation Environmental Design Tool (AEDT)  noise and emissions modeling of aircraft not currently in the aircraft noise and performance (ANP) database. Georgia Tech will identify, and review aircraft not currently modeled in the AEDT, and will collect information and necessary data to better understand the characteristics of these aircraft. Various statistical analysis methods will be used to classify the aircraft into different types in terms of size, age, technologies, and other engine/airframe parameters. Quantitative and qualitative analytical methods will be identified and evaluated for each aircraft type, to develop ANP noise data for the aircraft. Validation data from certification data or airport planning documents will be gathered to validate the methods. After validation, the models will be applied to develop ANP and noise data for the aircraft. Finally, recommendations and guidelines will be developed for implementing the developed data in the AEDT, to expand the AEDT Fleet database (Fleet dB) to include noise and performance data for aircraft currently not in the ANP database. The final deliverable to the AEDT development team was a series of recommendations on changes needed for the Fleet dB.]]></description>
      <pubDate>Mon, 20 Apr 2026 09:22:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691546</guid>
    </item>
    <item>
      <title>Project 045 Takeoff/Climb Analysis to Support AEDT Aircraft Performance Model (APM) Development</title>
      <link>https://trid.trb.org/View/2688786</link>
      <description><![CDATA[Accurate modeling of aircraft performance is a key factor in estimating aircraft noise, emissions, and fuel burn. Within the Aviation Environmental Design Tool (AEDT), many assumptions are made for aircraft performance modeling with respect to aircraft weight and departure procedure, coupled with aircraft departure typically being modeled by assuming that full rated takeoff power/thrust is used. As operations around airports continue to evolve, there is a need to examine those assumptions and to improve the modeling accuracy with flight data. In recent years, flight data are increasingly being used to enhance models and bring model estimation even closer to reality. Research is needed to build on prior work with a view to develop a robust set of recommendations for improved estimation processes for takeoff weight, reduced thrust takeoffs, and departure profiles within AEDT. During this project, the research provided the Federal Aviation Administration (FAA) with recommendations and an implementation plan to improve the takeoff performance modeling based on real world observations, which ultimately was included in the public release of AEDT3b.]]></description>
      <pubDate>Mon, 13 Apr 2026 17:08:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688786</guid>
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    <item>
      <title>Project 043 Noise–Power–Distance Reevaluation</title>
      <link>https://trid.trb.org/View/2688784</link>
      <description><![CDATA[The standard technique for evaluating fleet noise is to estimate the flight procedure source noise by using noise–power–distance (NPD) curves. Noise calculations within the Aviation Environmental Design Tool (AEDT) rely on NPD curves provided by aircraft manufacturers. This dataset reflects representative aircraft categories at set power levels and aircraft configurations. Noise levels are obtained as a function of slant distance via spherical spreading through a standard atmosphere, and other correction factors are applied to obtain the desired sound field metrics at the location of the receiver. The current NPD model does not consider the aircraft configuration (e.g., flap settings) or alternative flight procedures being implemented. These factors are important, because the noise characteristics of an aircraft depend on the thrust, aircraft speed, and airframe configuration, among other contributing factors such as ambient conditions. The outcome of this research is an approach based on the suggested noise–power–distance+ configuration ((NPD+C) format, which will enable more accurate noise predictions because of its inclusion of aircraft configuration and speed changes.]]></description>
      <pubDate>Mon, 13 Apr 2026 17:08:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688784</guid>
    </item>
    <item>
      <title>Project 009 Geospatially Driven Noise-Estimation Module</title>
      <link>https://trid.trb.org/View/2688767</link>
      <description><![CDATA[The goal of this project was to research potential open-source alternative geospatial frameworks for upgrading the Aviation Environmental Design Tool (AEDT), and to test and experiment with different novel computational approaches in order to drastically speed up the noise exposure calculations from potential large numbers of unmanned aircraft systems (UAS) being operated for package delivery or other potential UAS applications.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688767</guid>
    </item>
    <item>
      <title>Project 010 Aircraft Technology Modeling and Assessment</title>
      <link>https://trid.trb.org/View/2688768</link>
      <description><![CDATA[For the duration of Project 10’s period of performance, the research activities have remained consistent on technology modeling, system level assessment and fleet level modeling.  These activities were conducted for subsonic fleet from 2014 to 2017 and focused on supersonic fleet from 2017 to 2023.  The final report focuses on the latter in which Georgia Tech and Purdue University teams have partnered to investigate the future demand for supersonic air travel and the environmental impacts of supersonic transports (SSTs). The combined team investigated the environmental impacts including direct carbon dioxide (CO₂) emissions, noise, and fuel consumption. The primary objective of this research project is to support the Federal Aviation Administration (FAA) in modeling and assessing the potential future evolution of the next-generation supersonic aircraft fleet. The research in this project consists of five integrated focus areas: (a) establishing fleet assumptions and performing demand assessment (completed in 2021); (b) performing preliminary SST environmental impact prediction (completed in 2023); (c) developing approaches to model SSTs within the FAA Aviation Environmental Design Tool (completed in 2022); (d) performing vehicle and fleet assessments of potential future supersonic aircraft (completed in this report); and (e) performing physics-based modeling of SSTs and conceptual design by using the Framework for Advanced Supersonic Transport (FASST) (completed in the report).]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688768</guid>
    </item>
    <item>
      <title>Development of a Fast Method to Analyze Patterns in Airport Noise</title>
      <link>https://trid.trb.org/View/2685585</link>
      <description><![CDATA[Traditional airport noise modeling is limited in its ability to analyze large quantities of flight tracks due to high computation time. As a result, yearly noise reports are often limited to modeling flights from a single “representative day,” which lacks detail arising from the natural dispersion of flight tracks and variety in airport operations occurring throughout an entire year of operations. A framework for processing actual flight data and applying an existing, fast noise approximation is presented. Tens of thousands of flights can be analyzed in a matter of hours, allowing for a data-comprehensive approach to calculating noise metrics. Method results are cross-validated against the Aviation Environmental Design Tool (AEDT) on a single-event basis and an existing aggregate result on a multi-event basis. Results for a variety of metrics are presented based on data sourced from Boston Logan International Airport in 2016. Day-Night-Level (DNL) is calculated on a yearly, daily, and hourly basis, highlighting the variability in noise patterns depending on evolving airport runway configuration. N60 is calculated as a supplemental metric on a daily basis.]]></description>
      <pubDate>Thu, 09 Apr 2026 13:41:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685585</guid>
    </item>
    <item>
      <title>Houston-Galveston Navigation Channels, Texas Project 50-Year Plan Development and Design</title>
      <link>https://trid.trb.org/View/2171912</link>
      <description><![CDATA[This paper presents a summary of the history, process of the development, and design of the Houston-Galveston Navigation Channels widening and deepening project. The Houston-Galveston Navigation Channels Project will widen and deepen the Houston Ship Channel from 40 feet x 400 feet to 45 feet x 530 feet over a 53.5 mile distance from the Gulf of Mexico to the confluence of Buffalo and Boggy Bayous. Over the 50-year project life of initial construction and maintenance of the channel, some 3,889 acres of marsh, upland, and colonial water bird habitat will be created under the Beneficial Uses Plan. The Beneficial Uses Plan will have a net positive environmental benefit on the Galveston Bay eco-system and is not a mitigation plan, but recognizes and treats dredged material as a resource. The project demonstrates that improvements to commerce and navigation are compatible with the need for environmental restoration. The process for the development of the Beneficial Uses Plan is unique in that a partnership of resource agencies along with the federal and local sponsors defined the final plan. The process has proved to be very successful and can be adapted to other navigation projects.]]></description>
      <pubDate>Mon, 02 Feb 2026 14:13:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2171912</guid>
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    <item>
      <title>Data-driven assessment of rigid pavement vulnerability in Texas coastal regions</title>
      <link>https://trid.trb.org/View/2663108</link>
      <description><![CDATA[This research aims to evaluate the vulnerability of rigid pavements in two major coastal districts of Texas (i.e., Beaumont and Houston) spanning about 900 miles using data-driven approaches. Particularly, the study will (1) identify the key factors contributing to rigid pavement distress under dynamic coastal weather conditions, and (2) develop data-driven strategies to enhance the durability and performance of these pavement networks. Multi-source datasets, such as weather, geotechnical, traffic, coastal proximity, and pavement conditions, will be collected and integrated to support this analysis. Weather data, including temperature and precipitation, will be obtained from national and global databases such as NOAA’s National Centers for Environmental Information (NCEI) and NASA Earthdata/GES DISC. Soil classification and geotechnical attributes will be sourced from the NRCS SSURGO (Soil Survey Geographic Database), while coastal proximity data will be derived from Google Earth. Traffic volumes and loading data will be gathered from TxDOT’s Statewide Traffic Analysis and Reporting System (STARS II). Pavement condition metrics, including distress quantity, distress score, condition score, and ride quality, will be extracted from the Texas Department of Transportation (TxDOT)’s Pavement Management Information System (PMIS) and supplemented with satellite imagery. By integrating these datasets, the project will perform statistical and spatial analyses to establish correlations between weather variables, geotechnical conditions, traffic patterns, and pavement performance indicators.]]></description>
      <pubDate>Thu, 29 Jan 2026 19:58:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663108</guid>
    </item>
    <item>
      <title>Living Breakwaters: Transforming Coastal Resilience and Ecosystem Health in Southern Staten Island</title>
      <link>https://trid.trb.org/View/2559485</link>
      <description><![CDATA[Living Breakwaters is an innovative nature-based coastal infrastructure project designed to reduce and mitigate erosion and damage from storm waves, improve the ecosystem health of Raritan Bay, and encourage stewardship of the nearshore waters, enhancing people’s experience of the southern shoreline of Staten Island.]]></description>
      <pubDate>Mon, 23 Jun 2025 15:53:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559485</guid>
    </item>
    <item>
      <title>Breakwater Overtopping and Transmission Characteristics Associated with Living Shoreline Functional Attributes</title>
      <link>https://trid.trb.org/View/2559471</link>
      <description><![CDATA[A series of physical model tests were performed to explore what, if any, functional benefits in wave protection and damping are associated with various structural augmentations to a traditional breakwater intended to also serve as habitat creating or enhancing. The augmentations are in structural and geometric form and are intended to make large-scale alterations to wave behavior and transmission, and as such, do not involve secondary small-scale measures, such as fronting seagrass meadows of vegetated edges. Those would be considered “opportunities” to further increase the ecological value but were felt to have a limited functional impact or likely value, given the scale of wave aggression. The model test results revealed significant performance deviations from traditional overtopping transmission formulas and experience. Raising the interior breakwater core allowed for a significant lowering of the required breakwater crest height for the same incident wave conditions. Flattening of the front face slope, combined with a reduction in armor size to better achieve bio compatibility, gave improved results but reached a limiting beneficial level. The biggest impact on the reduction of wave overtopping and transmission was found with the introduction of quasi-two-dimensional protruding fronting reef ridges. Similar beneficial results were noted for lee-side habitat ponds where perched pools of water intended as juvenile fish nurseries co-serve as stilling basins in severe wave events before transmitting onward to shore. Incorporating these new design elements into the design of a breakwater offers opportunities to improve visual aesthetics by lowering breakwater crest heights and creating breakwater geometries, which better emulate natural landforms. Such solutions are more conducive to recreating habitat while still retaining the needed wave-mitigating properties of the breakwater.]]></description>
      <pubDate>Mon, 23 Jun 2025 15:53:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559471</guid>
    </item>
    <item>
      <title>A Diffusion model-based intelligent optimization method of rural road environments</title>
      <link>https://trid.trb.org/View/2510581</link>
      <description><![CDATA[Well-designed rural road environments can guide drivers to adopt reasonable driving behaviors, thereby significantly improving the driving experience and ensuring road safety. Existing methods for optimizing rural road environments mainly rely on expert knowledge, have low automation degrees, and are limited in efficiency and accuracy. Therefore, this study aims to propose an intelligent optimization method for rural road environments by using image generation technology. Using environment images from a naturalistic driving dataset, the area and location information of semantic components (e.g., lane markings, vegetation, guardrails, traffic signs, etc.) in rural road environments are extracted, and their impacts on driving speed is analyzed based on explainable machine learning (XGBoost and SHAP). These impacts are then utilized to determine how to adjust and optimize the road environment components at appropriate locations (i.e., obtain the optimization scheme). Then, a novel image generation technique, Diffusion model, is employed to establish an intelligent optimization method, which can directly generate optimized images of rural road environments. Compared to traditional manual mapping or other popular image generation algorithms such as CycleGAN, the method proposed in this study has the advantages of high efficiency, labor saving, and better image generation quality. This study can facilitate the design and optimization of rural road environments and enhance rural road safety in a more intelligent way.]]></description>
      <pubDate>Thu, 10 Apr 2025 09:21:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2510581</guid>
    </item>
    <item>
      <title>TRC2501: Establishing Vegetation in Poor Growing Conditions</title>
      <link>https://trid.trb.org/View/2491048</link>
      <description><![CDATA[The objective of this study is to create a methodology to be used in the preliminary planning and design phases that will identify factors that could inhibit vegetation growth, analyze and test problem soils prior to design or during construction, provide widely applicable options to offset growth inhibitors, and set parameters that will provide mitigation options based on site-specific factors.]]></description>
      <pubDate>Wed, 15 Jan 2025 12:37:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2491048</guid>
    </item>
    <item>
      <title>Crime Prevention through Environmental Design (CPTED) for Public Transit Stations</title>
      <link>https://trid.trb.org/View/2479873</link>
      <description><![CDATA[Crime Prevention through Environmental Design (CPTED) uses design principles to engineer safer spaces through the management of both built and natural environmental features. CPTED principles aim to reduce the chances and fear of criminal activity by designing spaces that both deter criminal activity and build community. Notably, public transportation can be an attractor of crime, and safety is cited as one barrier to public transportation. The goal of this work is to identify opportunities to integrate CPTED into transit station design to improve perceptions of safety for riders and increase access to transit and the opportunities it provides. To accomplish this goal, the project team catalogued CPTED practices already in use by two transit agencies, even if outside of a comprehensive, explicit CPTED framework. The team also developed a CPTED checklist for rail and bus stations based on existing literature, and analyzed CPTED features in place at twelve transit stops in underserved areas. Based on findings to date, the team presents initial options for applying CPTED in public transit. CPTED principles were also integrated into the semester project of an undergraduate civil engineering course.]]></description>
      <pubDate>Mon, 06 Jan 2025 14:35:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2479873</guid>
    </item>
    <item>
      <title>Crime Prevention through Environmental Design (CPTED) for Public Transit Stations - 2023-24 Study Locations [supporting dataset]</title>
      <link>https://trid.trb.org/View/2479874</link>
      <description><![CDATA[Abstract of the final report is stated below for reference: Crime Prevention through Environmental Design (CPTED) uses design principles to engineer safer spaces through the management of both built and natural environmental features. CPTED principles aim to reduce the chances and fear of criminal activity by designing spaces that both deter criminal activity and build community. Notably, public transportation can be an attractor of crime, and safety is cited as one barrier to public transportation. The goal of this work is to identify opportunities to integrate CPTED into transit station design to improve perceptions of safety for riders and increase access to transit and the opportunities it provides. To accomplish this goal, the project team catalogued CPTED practices already in use by two transit agencies, even if outside of a comprehensive, explicit CPTED framework. The team also developed a CPTED checklist for rail and bus stations based on existing literature, and analyzed CPTED features in place at twelve transit stops in underserved areas. Based on findings to date, the team presents initial options for applying CPTED in public transit. CPTED principles were also integrated into the semester project of an undergraduate civil engineering course.]]></description>
      <pubDate>Mon, 06 Jan 2025 14:35:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2479874</guid>
    </item>
    <item>
      <title>Spatio-Temporal Evolution of Port Green Development Quality in the Yangtze River Economic Belt-Based Set Pair Analysis</title>
      <link>https://trid.trb.org/View/2475433</link>
      <description><![CDATA[Under the background of “double carbon,” building a green, smart, and safe world-class port has gradually become a new direction for China’s port development. This paper takes 18 inland ports in the Yangtze River Economic Belt as the research object, uses the set pair analysis theory model and the statistical Moran index to evaluate the green development quality level and spatial autocorrelation characteristics of each port, and then explores the spatio-temporal evolution trend of the green development quality of China’s Yangtze River Economic Belt ports from 2019 to 2021. The results show that from the perspective of time, due to the policy guidance and technological progress, the green development level of each port has been significantly improved. From the perspective of space, the green development of the Yangtze River Economic Belt presents the spatial distribution characteristics of “high at both ends and low in the middle.”]]></description>
      <pubDate>Wed, 18 Dec 2024 12:01:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475433</guid>
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