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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>The unintended consequences of monitoring technologies: Evidence from the Electronic Logging Device mandate</title>
      <link>https://trid.trb.org/View/2661790</link>
      <description><![CDATA[The electronic logging device (ELD) final rule was passed in 2015 and implemented in phases over the course of four years. The mandate required that most commercial motor vehicles be fitted with an ELD by December 2019. The primary purpose of the transportation safety policy is to better enforce existing hours-of-service (HOS) regulations. HOS rules seek to limit the amount of driving time commercial drivers are on the road each day to avoid fatigued driving that may cause accidents and fatalities. Prior to the ELD mandate, commercial long-haul drivers cited that they regularly violated daily and weekly HOS rules when paper driving logs could be easily manipulated.Using data from the Fatality Analysis Reporting System (FARS) on traffic fatalities, we run two sets of difference-in-differences analyses exploiting the introduction of the ELD mandate: one predicting the change in fatal truck accidents as well as fatalities and injuries in these accidents and one showing the possible aggressive driving mechanisms (e.g., speeding, drunk driving) behind the main effects. Our first model finds that the ELD mandate led to an increase in fatal truck accidents by 8 %, an increase in truck fatalities by 11 %, and an increase in the number of surviving passengers with severe injuries experienced by 17 %. Our second model finds that fatal crashes with aggressive driving behaviors by truck drivers increased by 12 % in the post period, supporting the first model's results. Additionally, and as placebo exercises, we find no change in the average fatality and injury rates once we modify the timing of the policy (post-ELD period to the years before the phased-in compliance of 2017), the presence of outliers or the composition of the treated group.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:37 GMT</pubDate>
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      <title>Identifying Research Priorities to Improve Safety for Pedestrians and Bicyclists Accessing Bus Stops</title>
      <link>https://trid.trb.org/View/2596536</link>
      <description><![CDATA[Bus stops must be safely accessible to transit passengers who begin and end their trips as pedestrians. Prior studies have analyzed bus stop safety using crash data, but many assumed that all crashes near bus stops were directly related to the stop itself, largely due to the lack of transit-related information in crash data. This research analyzes pedestrian safety at bus stops using databases that explicitly report transit-related crashes. First, a detailed review of relevant datasets was conducted. Then, a nationwide analysis examined fatal “transit bus stoprelated” pedestrian crashes from the Fatality Analysis Reporting System (FARS) and compared them with other nearby fatal pedestrian crashes using a binary logit model and hierarchical clustering. The logit model indicated that midblock stops, pedestrians on the roadside, and bus-involved crashes are strong indicators of transit bus stop-related crashes. The clustering revealed three transit bus stop-related crash scenarios: (1) crossing to/from intersection stop, (2) waiting roadside at midblock stop, and (3) crossing to/from midblock stop. The clustering of both transit bus stop-related and nearby crashes showed that scenario 2 clustered separately from nearby crashes, suggesting this scenario is specific to bus stops. Next, using street-level imagery, bus stop infrastructure near fatal bus stop-related crashes was assessed. The results revealed that conditions generally improved after the crash. Last, a state-level analysis was conducted using crash data from Minnesota because this dataset specifically identified crashes with a pedestrian “going to or from public transit”. From 2016-2023, there were 38 of these transit-related pedestrian crashes reported in Minnesota, which varied in severity and included one fatal crash. In summary, this report highlights the need for improved data collection to assess pedestrian safety throughout the entire trip to and from transit.]]></description>
      <pubDate>Wed, 24 Sep 2025 15:31:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596536</guid>
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      <title>Existing Data Sources That Can Be Used in Conjunction With the Vehicle Inventory and Use Survey (VIUS)</title>
      <link>https://trid.trb.org/View/2596465</link>
      <description><![CDATA[The 2021 Vehicle Inventory and Use Survey (VIUS) provides the most comprehensive data on the physical and operational characteristics of the trucks being driven on U.S. roadways. The dataset is a rich resource that can be used to inform decisions regarding investments in transportation infrastructure, vehicle technologies and parts, safety, energy consumption, and more. This report summarizes exploratory research on where and how the value of VIUS could be strengthened through being used in conjunction with other national data sources. The research investigates which data sources are viable candidates for strengthening VIUS, what each dataset’s suitability is for being used in conjunction with VIUS, and what research questions could be answered with the tandem data that result from such effort.]]></description>
      <pubDate>Tue, 23 Sep 2025 17:10:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596465</guid>
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    <item>
      <title>Identifying the Critical Golden-Hour Zones in Rural Kansas</title>
      <link>https://trid.trb.org/View/2570641</link>
      <description><![CDATA[This study developed a data-driven geospatial tool to optimize emergency response strategies for vehicle crashes in Kansas. The tool analyzed more than a decade of crash data from the Kansas Department of Transportation (KDOT) and the Fatality Analysis Reporting System (FARS) to provide insight into T1 (crash to emergency medical services [EMS] notification), T2 (EMS notification to EMS arrival), and T3 (EMS arrival to hospital) intervals. The tool emphasized the importance of timely and efficient post-crash care, particularly in rural areas, where 36.6% of fatal crashes have response times that exceed 60 minutes, compared to only 10% in urban areas. Leveraging Python-based mapping and data analysis libraries, including OpenStreetMap and Dijkstra’s algorithm for shortest path calculations, the interactive tool allows users to visualize crash locations, EMS dispatch points, and hospital/trauma center locations. The tool also identifies high-crash regions with delayed response times and helps decision-makers improve emergency response strategies by simulating real-time EMS routing. Through its dynamic interface, the tool offers planning and assessment capabilities to decrease the number of fatalities and improve emergency care, especially in rural settings. This application specifically addresses the disparity in response times between rural and urban regions and can be adapted for similar efforts in other states, supporting life-saving strategies to enhance road safety.]]></description>
      <pubDate>Fri, 18 Jul 2025 09:05:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570641</guid>
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    <item>
      <title>Early Estimate of Motor Vehicle Traffic Fatalities in 2024</title>
      <link>https://trid.trb.org/View/2539758</link>
      <description><![CDATA[A statistical projection of traffic fatalities for 2024 shows an estimated 39,345 people died in motor vehicle traffic crashes, a decrease of about 3.8 percent compared to the 40,901 fatalities reported to have occurred in 2023. The fourth quarter of 2024 represents the 11th consecutive quarterly decline in fatalities beginning with the second quarter of 2022. Preliminary data reported by the Federal Highway Administration (FHWA) shows that vehicle miles traveled (VMT) in 2024 increased by about 32.3 billion miles, or about a 1.0- percent increase. The fatality rate for 2024 decreased to 1.20 fatalities per 100 million VMT, down from the reported rate of 1.26 fatalities per 100 million VMT in 2023. For the NHTSA regional differences, 8 of the 10 regions are estimated to have decreases in fatalities and fatality rate per 100 million VMT in 2024 as compared to 2023. Also, 35 States and Puerto Rico are projected to have decreases in fatalities. The fatality counts for 2023 and 2024 and the ensuing percentage changes from 2023 to 2024 will be further revised as the Fatality Analysis Reporting System (FARS) final file for 2023 and the FARS annual report file (ARF) for 2024 are available next year.]]></description>
      <pubDate>Mon, 21 Apr 2025 12:04:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539758</guid>
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      <title>2023 FARS/CRSS Coding and Validation Manual</title>
      <link>https://trid.trb.org/View/2539757</link>
      <description><![CDATA[NHTSA has collected motor vehicle traffic crash data since the early 1970s to support its mission to reduce motor vehicle traffic crashes, injuries, and deaths on our Nation’s trafficways. The two data systems included in this Coding and Validation Manual are the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS).]]></description>
      <pubDate>Fri, 18 Apr 2025 13:39:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539757</guid>
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      <title>2023 FARS/CRSS Pedestrian Bicyclist Crash Typing Manual: A Guide for Coders Using the FARS/CRSS Ped/Bike Typing Tool</title>
      <link>https://trid.trb.org/View/2539756</link>
      <description><![CDATA[Countermeasures to prevent pedestrian and bicyclist crashes are often hindered by State crash files that contain insufficient details about these crashes. To remedy this, Pedestrian and Bicycle Crash Typing was developed to describe the pre-crash actions of the people and organizations involved to better define the sequence of events and precipitating actions leading to crashes between motor vehicles and pedestrians or bicyclists. In 2010 NHTSA adopted parts of a stand-alone crash typing application called the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) into its two records-based data collection systems, the Fatality Analysis Reporting System, and the National Automotive Sampling System General Estimates System. In 2016 the Crash Report Sampling System replaced the legacy NASS-GES. This publication is the coding manual for that system. This manual provides information for the 2023 data year.]]></description>
      <pubDate>Fri, 18 Apr 2025 13:39:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539756</guid>
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    <item>
      <title>Fatality Analysis Reporting System Analytical User’s Manual, 1975-2023</title>
      <link>https://trid.trb.org/View/2539755</link>
      <description><![CDATA[This is the updated and revised Fatality Analysis Reporting System Analytical User’s Manual for the period 1975 to 2023.]]></description>
      <pubDate>Fri, 18 Apr 2025 13:39:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539755</guid>
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    <item>
      <title>Unraveling Crash Causation: A Deep Dive into Non-Motorists on Personal Conveyance</title>
      <link>https://trid.trb.org/View/2390051</link>
      <description><![CDATA[Non-motorists using personal conveyances, like skateboards, face increased safety challenges due to reduced visibility, particularly at intersections and in low light conditions. Safety studies on non-motorists using personal conveyances rely on limited hospital datasets or controlled/naturalistic riding conditions, lacking comprehensive identification of e-scooter riding risk factors. To address this research gap, this study collected real-world traffic crash data and quantified safety risks related to key contributing factors. Utilizing the 2020−2021 Fatality Analysis Reporting System (FARS) fatal crash data, this study examined the patterns of crashes associated with non-motorists using personal conveyances at segments and intersections. The findings provide valuable insights into key risk factors that can guide stakeholders, municipalities, and campus administrators in developing effective mitigation strategies to reduce safety risks associated with non-motorists using personal conveyances. By addressing these safety concerns, personal conveyances devices can be integrated as a safe and sustainable shared mobility option in urban and campus environments.]]></description>
      <pubDate>Thu, 22 Aug 2024 15:11:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2390051</guid>
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    <item>
      <title>Causal Insights into Speeding Crashes</title>
      <link>https://trid.trb.org/View/2389977</link>
      <description><![CDATA[Excessive speeding poses significant risks to road safety, impacting a driver’s ability to maneuver safely around obstacles and leading to longer stopping distances and delayed reactions to hazardous situations. It is a major contributor to fatal and serious road trauma, accounting for over 20% of such incidents in the US. Eliminating speeding entirely could potentially substantially reduce fatal injuries by 20% or more. This study leveraged the comprehensive National Highway Traffic Safety Administration’s (NHTSA) Fatality Analysis Reporting System (FARS) data, focusing on 50,081 speed-related motor vehicle traffic fatal crashes that occurred between 2016 and 2021, and a probabilistic graphical model to investigate the causal associations between key contributing factors involved in these speeding incidents. Ultimately, this high-impact research advanced the authors' understanding of the risks posed by speeding and impaired driving, guiding the way for evidence-based interventions and transformative policies to build safer roads and protect all road users from preventable tragedies.]]></description>
      <pubDate>Thu, 22 Aug 2024 15:11:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389977</guid>
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    <item>
      <title>Crash Prediction and Avoidance by Identifying and Evaluating Risk Factors from Onboard Cameras</title>
      <link>https://trid.trb.org/View/2378934</link>
      <description><![CDATA[Motor vehicle crashes are a huge concern of roadway transportation safety, resulting in over 37,000 fatalities and $800 million losses annually. In recent years, the number of road fatalities has been growing. Traditionally used and identifiable risk factor explanations no longer fully account for the causes of a recent increase in road fatalities. Human beings have bounded abilities in vision, cognition, making judgment, and simultaneously handling multiple tasks, particularly in complex, dynamic environments or in response to sudden situations. Therefore, assisting them in cognition of risks and making the right decisions rapidly in a near real-time manner is in need to advance transportation toward a zero fatality rate. This project’s motivation is to develop a data-driven, computer-vision (CV) empowered, verifiable system that can predict crashes, and thus improve drivers’ ability to avoid them. Pursuing a systematic approach, this project seamlessly integrates data analytics, deep learning, computer vision technology, and a rigorous verification process to achieve the goal. Specifically, this project creates a spatio-temporal attention guidance for CV-based crash risk assessment through analyzing fatal crash report data retrieved from Fatality Analysis Reporting System (FARS). The guidance informs the likelihood of crash and crash types given the time and location information of driving scenes, thus giving the driving scene analysis a clear focus. Then, a system of deep neural networks is developed to perform a driving scene analysis in support of crash risk assessment and prevention. The scene classification result allows for retrieving the relevant guidance for crash risk assessment and prevention. The joint results from the object detection and drivable area segmentation help identify risky pedestrians and vehicles in the surrounding traffic. Evaluation and examples demonstrate the effectiveness of the proposed technologies.]]></description>
      <pubDate>Wed, 15 May 2024 10:12:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2378934</guid>
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    <item>
      <title>Drug-Impaired Driving Data Collection: Report to Congress</title>
      <link>https://trid.trb.org/View/2373991</link>
      <description><![CDATA[This report was prepared in accordance with Section 25025 (Drug-Impaired Driving Data Collection) of the Infrastructure Investments and Jobs Act (IIJA), Pub. L. 117-58. The report summarizes what is known about the collection of drug-impaired driving data and its reporting to the Fatality Analysis Reporting System (FARS). The report describes the FARS data collection process and its toxicology reporting framework, the Recommendations for Toxicological Investigations of Drug-Impaired Driving and Motor Vehicle Fatalities – 2021 Update, identifies barriers that States encounter in submitting alcohol and drug toxicology results to FARS, provides recommendations on how to address those barriers, and describes the actions that the U.S. Department of Transportation and the National Highway Traffic Safety Administration are taking to assist States in improving toxicology testing in cases of motor vehicle crashes, and the reporting of alcohol and drug toxicology results in cases of motor vehicle crashes.]]></description>
      <pubDate>Thu, 09 May 2024 09:25:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2373991</guid>
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    <item>
      <title>Fatality Analysis Reporting System Analytical User’s Manual, 1975-2022</title>
      <link>https://trid.trb.org/View/2362006</link>
      <description><![CDATA[This is the updated and revised Fatality Analysis Reporting System (FARS) Analytical User’s Manual for the period 1975 to 2022. This manual presents the evolution of FARS coding from inception through present. It includes the data elements that are contained in FARS and other useful information that will enable the users to become familiar with the data system. The FARS/NASS GES and FARS/CRSS Coding and Validation Manuals provide more detailed definitions for each data element and attribute for a given year.]]></description>
      <pubDate>Thu, 04 Apr 2024 17:00:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2362006</guid>
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    <item>
      <title>2022 FARS/CRSS Coding and Validation Manual</title>
      <link>https://trid.trb.org/View/2362004</link>
      <description><![CDATA[NHTSA has collected motor vehicle traffic crash data since the early 1970s to support its mission to reduce motor vehicle traffic crashes, injuries, and deaths on our Nation’s trafficways. The two data systems included in this Coding and Validation Manual are the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS). FARS contains data derived from a census of fatal motor vehicle traffic crashes in the 50 States, the District of Columbia, and Puerto Rico. To be included in FARS, a crash must involve a motor vehicle traveling on a trafficway customarily open to the public and must result in the death of at least one person (occupant of a vehicle or a non-motorist) within 30 days of the crash. CRSS builds on the retired National Automotive Sampling System General Estimates System (NASS GES). CRSS is a sample of police-reported motor vehicle traffic crashes involving all types of motor vehicles, pedestrians, and cyclists, ranging from property damage-only crashes to those that result in fatalities. CRSS is used to estimate the overall crash picture, identify highway safety problem areas, measure trends, drive consumer information initiatives, and form the basis for cost and benefit analyses of highway safety initiatives and regulations. The target population of the CRSS is all police-reported traffic crashes of motor vehicles (motorcycles, passenger cars, SUVs, vans, light trucks, medium- or heavy-duty trucks, buses, etc.).]]></description>
      <pubDate>Thu, 04 Apr 2024 17:00:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2362004</guid>
    </item>
    <item>
      <title>Assessing the Relative Risks of School Travel in Rural Communities</title>
      <link>https://trid.trb.org/View/2265941</link>
      <description><![CDATA[This study examined school travel safety and risk and explored the potential differences between conditions that are present today with those that existed nearly two decades ago, when the Transportation Research Board published its landmark study on school travel safety. For this study, thirty transportation professionals were interviewed and a twenty-year crash data set from the Fatality Analysis Reporting System (FARS) was analyzed. The responses from the interviews were separated into ten common themes. The three most mentioned themes were education programs, concerns of roadway environments, and school bus safety. Based on the responses, concerns about the roadway environment, poor driver behavior, and the role of parents on mode choice have not changed in the last twenty years; however, safety education programs, vehicle centric travel, community planning, and pick up/drop off safety have evolved over time. With regard to the FARS data set, which was used as a benchmark to assess school transportation safety, the overall trends indicate that the trip to and from school remains a relatively safe activity, particularly along rural facilities where positive results were identified across four key metrics. Along urban facilities, slightly increasing trends were observed in the annual number of fatalities and in the number of non-motorists involved in a fatal crash, suggesting that opportunities remain to enhance and to improve the travel environment for school children.]]></description>
      <pubDate>Mon, 23 Oct 2023 08:51:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2265941</guid>
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