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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>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>Research on the Approach of Dynamic Collection and Feature-Based Ship-to-Shore Transmission of Marine Equipment Operation and Maintenance Data Based on Deep Learning</title>
      <link>https://trid.trb.org/View/2610621</link>
      <description><![CDATA[The traditional operational maintenance (O&M) tasks for marine equipment involve the continuous collection of operational status data and full data transmission between the ship and shore. While this method provides comprehensive monitoring information, it often incurs significant data monitoring, storage, and transmission costs. These issues are particularly pronounced in marine environments where bandwidth is limited or the power supply for monitoring devices is constrained. This study proposes a Deep Learning-based method for Dynamic Collection of operational maintenance data and Transmission of data features between the Ship and Shore, termed the DLDCTSS. By employing a deep learning anomaly detection model, DLDCTSS establishes a closed-loop feedback mechanism for dynamic sampling. Specifically, vibration sensors are activated for data collection only when abnormalities are detected, and critical state feature data are selectively transmitted based on O&M requirements. A theoretical analysis demonstrates that the DLDCTSS approach effectively reduces onboard storage overhead, lowers communication energy consumption between ship and shore, and improves data transmission efficiency, cutting overall system expenses. This study first assesses various anomaly detection models on open-source datasets, evaluating their suitability in maritime contexts. Subsequently, tests on a custom water-lubricated stern bearing platform validate both the anomaly detection model and the DLDCTSS approach. This dynamic sampling strategy not only diminishes redundant data collection but also ensures vital information is captured at critical moments, maximizing monitoring quality while enhancing operational efficiency. Meanwhile, transmitting only essential feature data markedly lowers bandwidth usage, and onboard feature extraction safeguards data privacy and security, meeting shipowners’ requirements.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610621</guid>
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    <item>
      <title>Which approach better samples extreme traffic conflicts? Conventional- vs. machine learning-based sampling methods</title>
      <link>https://trid.trb.org/View/2663894</link>
      <description><![CDATA[Extreme value theory has been receiving much attention of late for proactively estimating crash risk through a two-step procedure that first samples extreme traffic conflicts and then estimates crash risk based on those sampled extremes. Although the existing body of research has encapsulated sampling methods within a predominant conventional technique, there is no universally accepted practice on how to efficiently select threshold values, nor on how to evaluate the sampled extreme conflicts alignment with the conceptual crash severity level framework. This research aims to address these issues by employing machine learning-based sampling methods, which do not require predefined thresholds, and by comparing the sampled extremes with the conceptual severity levels, to assess their alignment. After a review of recent developments in machine learning techniques in transportation and other engineering fields, two promising machine learning sampling models, autoencoder neural network and isolation forest, were investigated using a database of vehicle-to-pedestrian conflicts at urban signalized intersections. Sampled extreme conflicts using the machine learning and conventional sampling techniques—as a baseline —were assessed and compared using two criteria: their visual alignment with the conceptual severity level framework, and their compatibility with the extreme value distribution. The results demonstrate that the extreme conflicts selected based on the machine learning methods better mirror the conceptual severity levels than the conventional sampling technique. Moreover, extremes classified by the isolation forest more closely preserve the characteristics of the empirical tail distributions, demonstrating a better contextual representation for modeling with the extreme value distribution compared to the autoencoder neural network and conventional sampling methods.]]></description>
      <pubDate>Wed, 25 Feb 2026 13:58:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663894</guid>
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    <item>
      <title>Nonmotorist Crashes in the 2024 Crash Investigation Sampling System</title>
      <link>https://trid.trb.org/View/2645458</link>
      <description><![CDATA[In 2024 the National Highway Traffic Safety Administration began implementing provisions in the Infrastructure Investment and Jobs Act (IIJA) “to enhance the collection of crash data by upgrading the Crash Investigation Sampling System (CISS) to include – 1) additional data collection sites; 2) an expanded scope that includes all crash types; and 3) on-scene investigation protocols." NHTSA increased the number of data collection sites from 32 to 40 in the 2024 CISS. NHTSA also expanded the scope to include nonmotorist crashes, defined as police-reported motor vehicle traffic crashes that each involve a nonmotorist who sustained a police-reported injury severity of killed, incapacitating injury, or non-incapacitating injury. The nonmotorist category includes: an occupant of a non-motor vehicle transport device (e.g., person riding in an animal-drawn conveyance); a pedestrian; a bicyclist including operator, passengers, and people being pulled by a bicycle (e.g., in a wagon); “Other Cyclist” (e.g., unicycle or tricycle); and “Person on Personal Conveyance." For the 2024 CISS, the crash scope covers police-reported motor vehicle traffic crashes where at least one passenger vehicle (i.e., passenger car, light truck, or van with a gross vehicle weight rating of less than 10,000 lb) was towed away from the scene of the crash; or police-reported motor vehicle traffic crashes where at least one nonmotorist sustained a police-reported injury severity of killed, incapacitating injury, or non-incapacitating injury in the crash.]]></description>
      <pubDate>Thu, 08 Jan 2026 16:50:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645458</guid>
    </item>
    <item>
      <title>NHTSA Field Crash Investigation 2024 Nonmotorist Coding and Editing Manual</title>
      <link>https://trid.trb.org/View/2645457</link>
      <description><![CDATA[NHTSA’s field crash investigation-based data collection programs are the Crash Investigation Sampling System (CISS), Special Crash Investigations (SCI), and the Crash Injury Research & Engineering Network (CIREN). NHTSA investigation-based programs collect detailed crash data to help scientists and engineers analyze motor vehicle crashes and injuries. CISS collects data on a representative sample of minor, serious, and fatal crashes involving at least one passenger vehicle – cars, light trucks, SUVs, and vans – towed from the scene. This publication is the nonmotorist coding manual for CISS, SCI, and CIREN for 2024.]]></description>
      <pubDate>Thu, 08 Jan 2026 16:50:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645457</guid>
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    <item>
      <title>NHTSA Field Crash Investigation 2024 Coding and Editing Manual</title>
      <link>https://trid.trb.org/View/2645456</link>
      <description><![CDATA[NHTSA’s field crash investigation-based data collection programs consist of data from the Crash Investigation Sampling System (CISS), Special Crash Investigations (SCI), and the Crash Injury Research & Engineering Network (CIREN). The CISS builds on the long running National Automotive Sampling System Crashworthiness Data System (NASS CDS). The NHTSA investigation-based programs collect detailed crash data to help scientists and engineers analyze motor vehicle crashes and injuries. CISS collects data on a representative sample of minor, serious, and fatal crashes involving at least one passenger vehicle – cars, light trucks, SUVs, and vans – towed from the scene. Beginning in 2024 CISS also collects information on non-motorist crashes. This publication is the coding manual for CISS, SCI, and CIREN for 2024.]]></description>
      <pubDate>Thu, 08 Jan 2026 16:50:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645456</guid>
    </item>
    <item>
      <title>Overview of the 2024 Crash Investigation Sampling System</title>
      <link>https://trid.trb.org/View/2645455</link>
      <description><![CDATA[In 2024 the National Highway Traffic Safety Administration increased the number of data collection sites from 32 to 40 for the 2024 Crash Investigation Sampling System (CISS). Concurrently, NHTSA also expanded the scope of data collection and investigated crashes involving nonmotorists. In 2024, CISS selected 6,132 police-reported crashes. Of these, 5,290 were eligible for investigation. The 2024 CISS shows there were an estimated 2,879,549 police-reported motor vehicle traffic crashes nationwide, representing the CISS crash target population where at least one passenger vehicle (i.e., passenger car, light truck, or van with a gross vehicle weight rating of less than or equal to 10,000 lb) was towed from the crash scene or a nonmotorist sustained a police-reported injury severity of killed, incapacitating injury, or non-incapacitating injury. This resulted in an estimated 1,265,043 injured occupants of in-transport towed passenger vehicles and 90,902 injured nonmotorists. Among the 2,796,576 towed passenger vehicle crashes, 2.5 percent (70,411) had serious injury or above, 25.0 percent (699,795) had moderate or minor injury, and 55.4 percent (1,549,511) had no injury.]]></description>
      <pubDate>Thu, 08 Jan 2026 16:50:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645455</guid>
    </item>
    <item>
      <title>Crash Investigation Sampling System 2024 Analytical User’s Manual</title>
      <link>https://trid.trb.org/View/2645454</link>
      <description><![CDATA[The Crash Investigation Sampling System is a national traffic crash database that provides comprehensive traffic crash data on police-reported motor vehicle crashes occurring in the United States during the year involving passenger cars, light trucks, and light vans that were towed. In 2024 nonmotorist crashes were added to the crashes researched by CISS. For a nonmotorist to qualify, a police-reported injury severity of killed, incapacitated, or nonincapacitating injury is required. This manual is an overview of the CISS sampling scheme and file structure, and has detailed information on the data elements including definitions, column names, attribute codes, and attribute labels. This manual and the NHTSA Field Crash Investigation 2024 Coding and Editing Manual and the NHTSA Field Crash Investigation 2024 Nonmotorist Coding and Editing Manual are the primary documentation supporting the 2024 CISS data file.]]></description>
      <pubDate>Thu, 08 Jan 2026 16:50:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2645454</guid>
    </item>
    <item>
      <title>NHTSA Crash Investigation Sampling System Event Data Recorder Data Element Benchmarking Study</title>
      <link>https://trid.trb.org/View/2633328</link>
      <description><![CDATA[An event data recorder (EDR) is defined in 49 CFR Part 563 to mean a device or function in a vehicle that records the vehicle's dynamic time-series data during the time period just prior to a crash event (e.g., vehicle speed vs. time) or during a crash event (e.g., delta-V vs. time), intended for retrieval after the crash event. This data can be downloaded to help crash reconstruction or vehicle safety research. NHTSA established EDR requirements in 49 CFR Part 563 in 2012. Since the promulgation of the requirements, crash avoidance and advanced driver assistance systems (ADAS) technologies have expanded in market penetration. Some vehicle original equipment manufacturers (OEMs) are recording status and activation of these technologies during the time period prior to the crash event in their EDRs. This study observes which data elements are recorded beyond what is required by Part 563 since it went into effect. A query on 2022-2023 Crash Investigation Sampling System (CISS) case year data was conducted, then model year 2022-2024 vehicles were selected that had that information available for analysis. Fifteen vehicle OEMs met these criteria and were eligible for review. EDR reports were assessed to identify data elements OEMs were reporting from their EDRs. These were compared to two existing EDR standards. Data elements were also identified that were not incorporated in existing regulations or best practices and were unique to each OEM. All 15 OEMs were reporting all Part 563 Table I data elements, but reporting Part 563 Table II elements varied across OEMs. Large variation in the number of data elements beyond Part 563 reported across OEMs ranged from 9 to 405 additional elements. Some OEMs were found to be reporting certain data elements from UN R160 and SAE J1698, but there were differences across OEMs for which elements. Eleven similar elements were reported by at least two OEMs, with a maximum of 9 of 15 OEMs reporting a particular similar element.]]></description>
      <pubDate>Thu, 04 Dec 2025 17:13:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633328</guid>
    </item>
    <item>
      <title>Determination of GPS sampling interval for trip energy consumption estimation of electric buses: Analysis of real-world data</title>
      <link>https://trid.trb.org/View/2563924</link>
      <description><![CDATA[This paper analyzes the effect of GPS sampling interval on the performance of the longitudinal dynamic model for estimating electric bus trip energy consumption based on real-world operational data. The performance of the estimation model under different sampling intervals is primarily evaluated by the MAE, RMSE, MAPE, and probability distribution functions. It is observed that the relationships between sampling intervals and the MAE, RMSE, and MAPE are all roughly S-shaped. The estimation accuracy is similar when the sampling interval is less than or equal to 13 s. The probability distribution functions of residuals are no longer stably consistent with that of the observed trip energy consumption when the sampling interval is larger than 16 s. In addition, the adaptability of the estimation model under different sampling intervals is analyzed from the perspective of bus operation management. Results indicate that the threshold value of the sampling interval is 16 s at the current battery rated capacity of 162.3 kWh. The threshold value of the sampling interval will become smaller with the decrease in battery rated capacity and increase in daily operation mileage.]]></description>
      <pubDate>Fri, 21 Nov 2025 08:44:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563924</guid>
    </item>
    <item>
      <title>A Non-Uniform Sampling and Dynamic Constrained Optimization-Based Trajectory Planning Method for Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2619136</link>
      <description><![CDATA[The quintic polynomial algorithm is not suitable for the sudden change of road curvature in the roundabout overtaking scenario, resulting in large curvature fluctuation and velocity oscillation. A trajectory planning method is proposed based on non-uniform sampling (NUS) and dynamic constraint optimization (DCO). Based on the NUS mechanism, the curvature fluctuation can be effectively reduced by using different center angles and road radii for sampling. Additionally, arc-length parameterization and time mapping techniques are adopted to extract velocity data, thereby achieving real-time adjustment of interpolation points. Centripetal acceleration constraints are employed to suppress velocity oscillations under dynamic conditions, significantly improving the smoothness of trajectories and comfort of vehicle driving. The simulation results show that the path length and curvature are optimized, and the centripetal acceleration is reduced, which significantly improves trajectory smoothness and passenger comfort.]]></description>
      <pubDate>Thu, 06 Nov 2025 16:53:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2619136</guid>
    </item>
    <item>
      <title>Adaptive synthetic generation using one-step Gibbs Sampler</title>
      <link>https://trid.trb.org/View/2594506</link>
      <description><![CDATA[Most existing state-of-the-art synthetic generation methods produce static snapshots of data that fail to adapt to demographic changes over time, which makes them quickly outdated. This paper introduces an adaptive approach to synthetic population generation using a one-step Gibbs Sampler that allows for the maintenance of synthetic data by integrating new information adaptively, rather than requiring a complete regeneration of datasets each time an update is necessary. The authors compare existing independent regeneration methods with the proposed adaptive generation and demonstrate that the approach creates a synthetic population of the same level of accuracy, but more efficiently. Also, the authors show that when the initial data is scarce or biased, the adaptive generator is particularly effective in enhancing dataset quality by adaptively enriching the population sample. To account for updates, the authors introduce a new Gibbs-resampling technique as an intermediate step that uses information from the most recent disaggregated real data to correct the errors and improve the representativeness and heterogeneity of the synthetic data. Furthermore, the results indicate that the adaptive approach is robust to unforeseen events, which helps mitigate the lack of representativeness in real data during such occurrences.]]></description>
      <pubDate>Mon, 13 Oct 2025 13:52:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594506</guid>
    </item>
    <item>
      <title>Comparative Analysis of Upsampling Algorithms for Pavement Point Clouds: Toward 3D Pavement Distress Inspection</title>
      <link>https://trid.trb.org/View/2591075</link>
      <description><![CDATA[The accurate representation of pavement condition is crucial for road agencies to make appropriate maintenance decisions. With the great development of three-dimensional (3D) measurement technologies and devices in the past decades, acquiring 3D information of pavement has become much more accessible in the practice of road maintenance. However, the insufficient density of point clouds collected by economical devices still limits their utilization in pavement condition evaluation. Fortunately, many point cloud upsampling methods have been successfully developed to convert sparse and nonuniform points to dense representations, while their performance in the pavement area still needs validation. This study aims to examine existing upsampling algorithms for pavement point clouds based on public datasets and evaluate their effectiveness through visualization of point clouds collected by the authors. Specifically, a dataset comprising 10,026 samples of point clouds selected from public recourse was developed for training and evaluation. Three distinct point cloud upsampling methods, Poisson reconstruction, cubic spline interpolation, and deep learning-based methods, were compared on the dataset. Moreover, the point clouds of pavement collected by the authors were upsampled to evaluate their performance. The results indicated that the deep learning method demonstrated superior performance in the P2F metric, reducing it by 5.9% compared to the traditional cubic spline method. The deep learning methods enhance the information capture capability of low-density point cloud devices, which will facilitate the application of low-cost equipment for road surface detection, thereby contributing to the development of efficient road surface detection and management systems.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591075</guid>
    </item>
    <item>
      <title>Application of a Machine Learning–Based Sampling Method in Extreme Value Theory for Crash Risk Estimation of a Freeway Segment</title>
      <link>https://trid.trb.org/View/2596619</link>
      <description><![CDATA[Surrogate safety measures (SSM) have been used extensively in traffic safety studies for crash risk estimation. Most SSM-based studies employing extreme value theory (EVT) use the peak over threshold (POT) approach to detect anomalies or extreme events during safety-critical situations. This study investigated the efficacy of unsupervised machine learning (ML)-based anomaly detection methods as an extreme event sampling approach compared with the conventional POT sampling approach by developing bivariate EVT models for rear-end crash risk estimation on a freeway segment. Three widely used SSMs, namely time-to-collision (TTC), modified time-to-collision (MTTC), and deceleration rate to avoid crash (DRAC), were considered for the bivariate EVT modeling. Video data were collected from the selected segment of the I-40 expressway in Memphis, Tennessee. Among three SSMs, the combination of MTTC and DRAC bivariate EVT models provided the most accurate crash risk estimation (within the 99% confidence interval of the observed crashes), applying the traditional POT sampling approach, and ML-based isolation forest (iForest) and one-class support vector machine (OCSVM) sampling approaches. ML-based OCSVM sampling method provided a 21% crash estimation accuracy improvement over the POT and iForest sampling methods. Based on these findings, it can be concluded that unsupervised ML anomaly detection can be an effective sampling approach, reducing subjectivity in the threshold selection encountered in the POT sampling method. Safety improvement programs aim to maximize outcomes with limited resources, and an accurate estimation of the expected number of crashes helps engineers prioritize high-impact improvement locations.]]></description>
      <pubDate>Mon, 15 Sep 2025 10:29:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596619</guid>
    </item>
    <item>
      <title>Standard Specifications for Transportation Materials and Methods of Sampling and Testing, and AASHTO Provisional Standards</title>
      <link>https://trid.trb.org/View/2593756</link>
      <description><![CDATA[The Standard Specifications for Transportation Materials and Methods of Sampling and Testing, and AASHTO Provisional Standards (the "Materials Standards") contains specifications, recommended practices, and test methods commonly used in the construction of highway facilities. Provisional standards are also published to allow practitioners to use them early in the research or development phase. In addition to revisions to harmonize industry standards, update technology, and improve the standards, the 45th edition includes several conversions of standards to dual units, as well as several important updates: better defining coal ash used on concrete; more clearly defining blended cements alkali loading; clearer requirements for both making and storing concrete test specimens; improvements to the pipe standards; and clarifications to the binder content of asphalt mixtures by nuclear method. In addition, the standards include: (1) Twelve new provisional practices and tests related to pavement measurement and pavement preservation; new dowel bar material and test standards; and a new ice melting capacity test method. (2) Twelve provisional standards that were adopted as full standards. (3) Three new standards related to quality assurance: one for concrete, one for cold central plant recycling, and one for cold in-place recycling. (4) Two new test methods related to asphalt: a new liquid asphalt test method, called the poker chip test, and a new balanced mix design for high RAP asphalt test method. The 45th edition includes the 20 new and 103 revised standards released in 2025. In total, it contains 583 standards divided into 3 PDF files.]]></description>
      <pubDate>Mon, 15 Sep 2025 10:28:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593756</guid>
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