<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7RGF0YSBzZXQmcXVvdDsiIG9yaWdpbmFsX3ZhbHVlPSImcXVvdDtEYXRhIHNldCZxdW90OyIgLz48L2ZpbHRlcnM+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Analyzing NPMRDS Using Advanced Machine Learning to Enhance Road Safety, Public Health and System Performance</title>
      <link>https://trid.trb.org/View/2660166</link>
      <description><![CDATA[This project aims to leverage National Performance Management Research Data Set (NPMRDS) data and machine learning techniques to analyze the relationships between speed variations, crash occurrences, weather conditions, and road geometry. The goal is to identify patterns that can measure the performance of road segments and inform targeted safety interventions and infrastructure improvements. The project progressed in an organized sequence We began with systematic data collection and the selection of study segments, followed by rigorous data processing and feature engineering to support both speed prediction and roadway reliability assessment. Multiple modeling approaches are developed, validated, and refined to ensure robustness, while exploratory analyses are conducted to assess crash-related and health-related impacts, extending the scope of insights beyond prediction alone. The findings are carefully examined, leading to actionable recommendations for practice and policy. Implementation activities further demonstrate the practical applicability of the developed methods.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:39:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2660166</guid>
    </item>
    <item>
      <title>A Statewide Highway System Network Analysis: Impact of Probe Vehicle Composition</title>
      <link>https://trid.trb.org/View/2631662</link>
      <description><![CDATA[The U.S. Congress has mandated that the National Performance Management Research Data Set (NPMRDS) be used by federal and state transport agencies and metropolitan planning organizations for performance analyses. The NPMRDS contains archived space mean speed (SMS) from probe vehicles representing Trucks, Passenger Vehicles, and All-Traffic for every 5-min time period of each day across predefined roadway links in the United States. This paper analyzes approximately 41?million SMS estimates from 4,385 spatial segments disaggregated at 5-min intervals on Alabama highways for all days in October 2023. There was strong evidence found that the NPMRDS SMS estimates for All-Traffic were biased toward Truck SMS estimates. The authors subsequently developed a statistically valid approach for removing the oversampling bias. It was found that the maximum mean average percentage difference between the proposed approach and the uncorrected All-Traffic SMS was approximately 25%. To improve the accuracy of traffic metrics that depend on the NPMRDS, it is recommended that users should consider the proposed correction method if their NPMRDS data show similar oversampling bias. It is also recommended that the NPMRDS should explicitly define how the All-Traffic SMS data were estimated and provide the actual number of probes or sample size instead of a quasi-representation of actual probes.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:56:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2631662</guid>
    </item>
    <item>
      <title>Modeling Travel Time Reliability for Non-Interstate National Highway System Routes</title>
      <link>https://trid.trb.org/View/2625404</link>
      <description><![CDATA[Under the Moving Ahead for Progress in the 21st Century Act, state departments of transportation are required to report travel time reliability and set performance targets for interstate and non-interstate National Highway System facilities. Although several Virginia Transportation Research Council studies have analyzed and predicted travel time reliability on interstates, a gap still exists in studies modeling travel time reliability on arterial roads. Using 4 years of INRIX and National Performance Management Research Data Set (NPMRDS) probe data, this study conducted a segment-level comparative analysis of travel time distributions and the Level of Travel Time Reliability (LOTTR). Results show that NPMRDS systematically reports higher travel times than INRIX, inflating reliability metrics. Consequently, NPMRDS classifies significantly more segments as unreliable, especially on short (less than 0.25-mile) segments. Applying sample size filters to exclude segments with low sample sizes does not substantially reduce the discrepancies between NPMRDS and INRIX travel times. Analysis reveals that a major contributing factor is the data density of NPMRDS. More than 80% of observations fall into the lowest category (density A), leading to greater volatility in the higher percentiles of travel times and inflated Level of Travel Time Reliability values. In addition, this study developed planning-level models to predict the 50th, 80th, and 95th percentile travel times on arterial segments statewide. Linear mixed models, random forests, and Light Gradient Boosting Machine models were built using data from multiple sources, and model performances were compared. The results showed that linear mixed models perform reasonably well for segments with LOTTR below 1.5, but machine learning approaches offer superior accuracy across the full reliability spectrum. The study also found that the machine learning models trained with INRIX data underestimate the travel time percentiles calculated using NPMRDS data, with a mean absolute percentage error of more than 10 for the 50th percentile travel time. The study recommends that the Virginia Department of Transportation further develop models trained with NPMRDS data to meet federal reporting requirements and improve accuracy. In addition, the Virginia Department of Transportation should consider improving consistency in travel time reliability analyses across divisions and programs where applicable. Researchers developed an implementation plan that includes stakeholder engagement, technical guidance development, and support for the next target-setting cycle of the system reliability performance measures set forth in 23 CFR Part 490 (May 30, 2025).]]></description>
      <pubDate>Sat, 15 Nov 2025 18:14:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625404</guid>
    </item>
    <item>
      <title>Highway Performance Monitoring System Field Manual Supplemental Guidance: Travel Time Metric Data Reporting Requirements &amp; Specifications</title>
      <link>https://trid.trb.org/View/2444666</link>
      <description><![CDATA[This document serves as supplemental guidance to the 2016 Highway Performance Monitoring System (HPMS) Field Manual and advises State Departments of Transportation (DOTs) as to how they must process and report travel time-related performance metric data as required by the third regulation on the National Performance Management Measures. More specifically, this document provides information on how to submit annual performance metric and related data via the HPMS that comports with the reporting requirements and specifications of 23 CFR 490.511(e), 490.611(b), and 490.711(f).]]></description>
      <pubDate>Sat, 26 Oct 2024 16:09:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2444666</guid>
    </item>
    <item>
      <title>Methods To Improve Freight Highway Performance, Reliability, and Bottlenecks</title>
      <link>https://trid.trb.org/View/2417296</link>
      <description><![CDATA[Helping freight move efficiently through reduced congestion and bottlenecks along with safety is a key factor in supporting supply chains. This report focuses on freight mobility by looking at the State Department of Transportation (DOT) and metropolitan planning organization (MPO) freight performance measurement, bottleneck reporting, and freight improvement strategies to improve freight mobility and reliability. State DOTs and MPOs have been implementing transportation performance management, including measuring truck travel time reliability (TTTR) and freight truck bottlenecks. Most State DOTs and MPOs use other freight mobility performance measures in addition to the TTTR measure to assess freight mobility and reliability. The most common measures are analyses of delay, including hours of delay and delay per mile, the planning time index (PTI) and travel time index (TTI), a level of service (LOS), or volume-to-capacity ratio, and average truck speed. Also, the National Performance Management Research Data Set (NPMRDS) is the primary data source used by the State DOTs and MPOs for mobility analysis. A Toolbox of Solutions is provided in this document to help States DOTs and MPOs improve freight mobility, reliability, and bottlenecks.]]></description>
      <pubDate>Fri, 23 Aug 2024 16:54:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2417296</guid>
    </item>
    <item>
      <title>Freight Value Estimation Methodology for Different Roadway Functional Classes</title>
      <link>https://trid.trb.org/View/2117810</link>
      <description><![CDATA[“Freight movement and economic vitality” was a key focus area under the Moving Ahead for Progress in the 21st Century (MAP‐21) Federal Transportation Act. Since MAP-21, transportation agencies are increasingly incorporating performance-based approaches into their transportation and freight planning processes. This paper focuses on another vital aspect of improving system efficiency of freight movement – estimating the magnitude (quantity as well as value) of freight movement on different roadway functional classes in the highway network of the jurisdictional area of interest. The research uses a combination of data sources, such as Freight Analysis Framework (FAF), Highway Performance Monitoring System (HPMS)/ National Performance Management Research Data Set (NPMRDS), available literature, inventory surveys, and empirical data to estimate desired metrics, including the value of freight movement on a given section of road based on its roadway functional classification, truck volume, and the aggregate value per ton of different commodity movement types. Analysis of data for several states and freight zones reveals interesting findings and trends for different movement categories. A few seaboard states exhibit slightly higher commodity values impacted by the container movement around ports in those regions. FAF zones which are either major freight generators or attractors, or lie along major freight corridors typically exhibit higher values of total commodity movement value per day because of a combination of moderately high value per ton and higher truck volumes. The proposed methodology can help identify high value corridors and segments susceptible to system disruption to monitor potential bottlenecks and improve system resiliency.]]></description>
      <pubDate>Wed, 22 Feb 2023 09:43:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2117810</guid>
    </item>
    <item>
      <title>Prediction of Interstate Travel Time Reliability: Phase II</title>
      <link>https://trid.trb.org/View/2117716</link>
      <description><![CDATA[Accurate prediction of travel time reliability measures would help state departments of transportation set performance targets and communicate the progress toward meeting those targets as required by the Moving Ahead for Progress in the 21st Century Act (MAP-21). In a recent Virginia Transportation Research Council study, Methods to Analyze and Predict Interstate Travel Time Reliability, researchers developed and tested statistical and machine learning models to analyze and predict travel time reliability on interstate highways. The generalized random forest (GRF) model showed promise in terms of data processing (no need for pre-clustering of travel times) and the relative accuracy of the results and was recommended for further evaluation by the study’s technical review panel. The current study directly adapted the previously developed GRF models to meet the requirements of MAP-21 federal target setting. In particular, the GRF approach developed using the INRIX Traffic Message Channel network for weekday peak period traffic by the prior study was successfully (1) adapted to the federally required National Performance Management Research Dataset (NPMRDS) network, and (2) expanded to cover the weekday, midday and weekend daytime periods. The technical review panel was also interested in practical steps to implement the predictive models. To that end, suggested procedures for applying the new GRF models—including relevant model inputs and data preparation steps—are documented in this report. Direct application of the GRF models trained with INRIX data (2017-2018) to predict travel time reliability measures in 2009 on the NPMRDS network highlighted the need for developing new GRF models targeted to the NPMRDS network, especially when the 90th percentile travel time was predicted. Whereas the INRIX models showed mean absolute percentage errors of 37% and 51% for freeway and interchange segments, respectively, for the PM peak hours, the new GRF models (trained with 2017-2018 NPMRDS data) had relatively smaller mean absolute percentage errors of 34% for freeway segments and 38% for interchange segments depending on how work zones were characterized and how data were aggregated. Because operational improvements are often evaluated on the basis of how they improve reliability, especially on how the 90th percentile travel time is affected, the new GRF models are relevant for planning operational investments. In addition, because many of these improvements affect interchanges, the remedy of the new GRF models is essential for evaluating weaving strategies or traveler information systems that could be implemented at these locations.]]></description>
      <pubDate>Sun, 19 Feb 2023 17:53:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2117716</guid>
    </item>
    <item>
      <title>Performance Using the National Performance Management Research Data Set (NPMRDS) Case Study: Application to the Texas Department of Transportation GO I-10 Project</title>
      <link>https://trid.trb.org/View/2071703</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) is engaging with State Departments of Transportation (DOTs) and conducting research to understand work zone mobility performance measurement best practices, challenges, and opportunities. The information will be used to increase awareness on data, tools, and methods for systematic work zone performance measurement across all stages of project development including planning, design, construction, and post-construction. Topics of interest include use of probe data and mainstreaming performance measures into agency policies and processes, including work zone process reviews and incorporating work zones into transportation system management & operations (TSMO) initiatives. This case study is one of a series of resources on work zone mobility performance measurement. It demonstrates how the National Performance Management Research Data Set (NPMRDS) can be used to measure work zone mobility performance. The NPMRDS contains probe-vehicle-based speed and travel time data for major highways and roadways across the United States. A real-world road construction project (Texas DOT’s GO I-10 Project in El Paso, TX) is used to illustrate a simple, repeatable process that State DOTs can use to measure work zone mobility performance.]]></description>
      <pubDate>Tue, 06 Dec 2022 09:26:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2071703</guid>
    </item>
    <item>
      <title>How the speed limit policy affects travel speed?: Quasi-experimental approach</title>
      <link>https://trid.trb.org/View/1764637</link>
      <description><![CDATA[The objective of this study is to examine the effect of posted speed limit change on travel speeds. To do this, the authors use the National Performance Management Research Data Set (NPMRDS), which is a unique and rich dataset provided via the Federal Highway Administration (FHWA). Particularly, the authors focus on the impact of increased posted speed limit, implemented during June 2015, on interstate highways in Wisconsin including urban and rural areas between 2014 and 2016 by employing a quasi-experimental model with a panel dataset. The authors' findings clearly demonstrate that the posted speed limit change from 65 to 70 increases travel speed by 1.7 mph after controlling for location and time specific fixed-effects. Specifically, the posted speed limit change on Wisconsin interstate highways increases average travel speed of passenger and freight vehicles by 1.87 and 1.16 mph, respectively. Findings also show that speed variations have increased after the speed limit change, and higher variations do not occur in freight travel speeds, but in passenger vehicle travel speeds. These variations differently exist between urban and rural areas. The authors suggest that transportation agencies should consider these variations by vehicle types and places when they increase posted speed limit.]]></description>
      <pubDate>Fri, 30 Apr 2021 17:11:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1764637</guid>
    </item>
    <item>
      <title>Transportation Performance Management for System Operations: Development of processes, Tools, Measures and Targets</title>
      <link>https://trid.trb.org/View/1753443</link>
      <description><![CDATA[This study identifies effective practices for transportation systems management and operations (TSMO) at the strategic, programmatic and tactical level, assesses Georgia Department of Transportation’s status using the TSMO Capability Maturity Model (CMM), and offers recommendations to move the agency to the next level. The study develops a tool for calculating transportation system performance metrics using the National Performance Management Research Data Set (NMPRDS) and other databases. The authors conduct a literature review to characterize effective strategic, programmatic and tactical TSMO practices; administer a survey to characterize the status of Georgia Department of Transportation (GDOT) using the TSMO CMM, and offer recommendations to move GDOT to the next level. Subsequently, the authors develop a tool to analyze and report on transportation system performance using the Moving Ahead for Progress in the 21st Century (MAP-21) system performance measures (PM3). The PM3 Tool calculates metrics for travel time reliability on interstate and non-interstate routes on the National Highway System (NHS), truck travel time reliability, and annual hours of peak-hour excessive delay per capita, and reports on percent of non-single occupancy vehicle (SOV) travel, and total emission reductions. It uses data from the NPMRDS, GDOT, the U.S. National Census and the Congestion Mitigation and Air Quality (CMAQ) Public Access Database. The study highlights the importance of developing TSMO at the tactical, programmatic and strategic levels simultaneously, integrating TSMO activities with strategic and long range planning and asset management functions within state departments of transportation (DOTs). Using the PM3 tool, state DOTs can calculate PM3 measures and use them in setting future performance targets, while working at the strategic, programmatic and tactical levels to improve transportation system performance.]]></description>
      <pubDate>Tue, 15 Dec 2020 10:10:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1753443</guid>
    </item>
    <item>
      <title>2019 Urban Congestion Trends: Leveraging Data to Improve Operations Performance</title>
      <link>https://trid.trb.org/View/1740683</link>
      <description><![CDATA[The Federal Highway Administration 2019 Urban Congestion Report (UCR) details trends and the current state of congestion and reliability on the National Highway System (NHS) in 52 of the largest metropolitan areas in the United States. This report also includes examples of agencies using the National Performance Management Research Data Set (NPMRDS) for performance reporting, as well as a few operational strategies and performance management approaches implemented by State and local transportation agencies in recent years. The report concludes with a brief discussion to familiarize readers with a tool developed through an American Association of State Highway and Transportation Officials (AASHTO)– led pooled-fund study to assist agencies with submitting and utilizing performance measures.  Overall, congestion metrics have improved from 2018 to 2019. The national congestion measures across all 52 metropolitan statistical areas (MSAs) included in the UCR are generally down from 2018 to 2019.]]></description>
      <pubDate>Mon, 05 Oct 2020 17:37:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1740683</guid>
    </item>
    <item>
      <title>Inclusion of speed and weather measures in safety performance functions for rural roadways</title>
      <link>https://trid.trb.org/View/1719156</link>
      <description><![CDATA[The research on relationships among vehicle operating speed, roadway design elements, weather, and traffic volume on crash outcomes will greatly benefit the road safety profession in general. If these relationships are well understood and characterized, existing techniques and countermeasures for reducing crash frequencies and crash severities could potentially improve, and the opportunity for new methodologies addressing and anticipating crash occurrence would naturally ensue. This study examines the prevailing operating speeds on a large scale and determines how traffic speeds and different speed measures interact with roadway characteristics and weather condition to influence the likelihood of crashes. This study used three datasets from Washington and Ohio: 1) Highway Safety Information System (HSIS), 2) the National Performance Management Research Dataset (NPMRDS), and 3) National Oceanic and Atmospheric Administration (NOAA) weather data. State-based conflated databases were developed using the linear conflation of HSIS and NPMRDS. The results show that certain speed measures were found to be beneficial in quantifying safety risk. Annual-level crash prediction models show that increased variability in hourly operating speed within a day and an increase in monthly operating speeds within a year are both associated with a higher number of crashes. Safety practitioners can benefit from the current study in addressing the issue of speed and weather in crash outcomes.]]></description>
      <pubDate>Mon, 27 Jul 2020 09:40:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1719156</guid>
    </item>
    <item>
      <title>Rural Speed Safety Project for USDOT Safety Data Initiative</title>
      <link>https://trid.trb.org/View/1702706</link>
      <description><![CDATA[The objective of this project was to examine prevailing operating speeds on a large scale to determine how speed and speed differentials interact with roadway characteristics to influence the likelihood of crashes. The project team conducted three major tasks: (a) developed conflated databases for Ohio and Washington by incorporating the Highway Safety Information System (HSIS) and the National Performance Management Research Data Set; (b) developed static and interactive data visualization tools to show the association between operating speed measures and safety outcomes; and (c) developed best-fit models at annual and daily levels to address the impact of operating speed on safety. The overall finding was that speed-related operational information is an area of opportunity to better understand safety outcomes. This pilot project established the framework of data conflation and an analytical pipeline that will help to address the effect of operation speed measures on safety. The replicability procedure developed in this study can be applied to other HSIS States. The project team developed a weblink that includes descriptive statistics and data visualization tools (both static and interactive). The links provide a more detailed view of the speed measures and descriptive statistics, as well as visualization of the association between speed measures and crashes at a granular level. The team members also developed an interactive decision support tool (https://ruralspeedsafety.shinyapps.io/rss_sdi/) to show annual risk scoring using Washington and Ohio data that contain expected total crashes from the developed models.]]></description>
      <pubDate>Fri, 08 May 2020 15:57:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/1702706</guid>
    </item>
    <item>
      <title>National Performance Management Research Dataset (NPMRDS) - Speed Validation for Traffic Performance Measures</title>
      <link>https://trid.trb.org/View/1648429</link>
      <description><![CDATA[This report presents research detailing the use of the first version of the National Performance Management Research Data Set (NPMRDS v.1) comprised of highway vehicle travel times used for computing performance measurements in the state of Oklahoma. Data extraction, preprocessing, and statistical analysis were performed on the dataset and a comprehensive study of dataset characteristics, influencing variables, outliers and anomalies was carried out. In addition, a study on filtering and removing speed data outliers across multiple road segments is developed, and a comparative analysis of raw baseline speed data and cleansed data is performed. A method for improved congestion detection is investigated and developed. Identification and a computational comparison analysis of travel time reliability performance metrics for both raw and cleansed datasets is shown. An outlier removal framework is formulated, and a cleansed and complete version of NPMRDS v.1 is generated. Finally, a validation analysis on the cleansed dataset is presented. In the end, research affirms that understanding domain specific characteristics is vital for filtering data outliers and anomalies of this dataset, which in turn is key for calculating accurate performance measurements. Thus, careful consideration for outlier removal must be taken into account when computing travel time reliability metrics using the NPMRDS.]]></description>
      <pubDate>Tue, 24 Sep 2019 14:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1648429</guid>
    </item>
    <item>
      <title>Developing a Guideline for Setting Targets for National Performance Management Measures to Assess Performance of the National Highway System in South Carolina</title>
      <link>https://trid.trb.org/View/1595602</link>
      <description><![CDATA[This paper presents a framework for establishing targets for national system performance measures for reliability. The paper outlines step-by-step procedures followed using the National Performance Management Research Data Set and provides a possible range of estimates for future years’ targets for South Carolina highways. This paper focuses only on the percentage of person-miles traveled on both Interstate and non-Interstate national highway system. The framework presented in this paper is reproducible for other state Departments of Transportation, and accounts for construction projects that might influence the future predicted target number(s).]]></description>
      <pubDate>Tue, 14 May 2019 08:57:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/1595602</guid>
    </item>
  </channel>
</rss>