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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Overview of Motor Vehicle Traffic Crashes In 2024</title>
      <link>https://trid.trb.org/View/2686627</link>
      <description><![CDATA[There were 1,771 fewer people killed in motor vehicle traffic crashes on U.S. roadways during 2024, a 4.3- percent decrease from 41,025 in 2023 to 39,254 in 2024. The fatality rate per 100 million vehicle miles traveled  (VMT) decreased 5.6 percent from 1.26 in 2023 to 1.19 in 2024. It represents the third year-to-year decrease in  both fatalities and fatality rate since 2021. The estimated number of people injured on our roadways decreased in 2024 to 2.42 million, falling 0.8 percent from 2.44 million in 2023. This decrease was not statistically significant. The injury rate per 100 million VMT  decreased 1.3 percent from 75 in 2023 to 74 in 2024.  The estimated number of police-reported traffic crashes increased from 6.14 million in 2023 to 6.18 million in  2024, a 0.7-percent increase which was not statistically significant. VMT for 2024, reported by the Federal  Highway Administration (FHWA), increased 1.5 percent from 3,247 billion in 2023 to 3,294 billion in 2024.]]></description>
      <pubDate>Mon, 06 Apr 2026 10:25:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686627</guid>
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    <item>
      <title>Collision Risk Assessment Based on Uncertainty of Vehicle Models</title>
      <link>https://trid.trb.org/View/2659301</link>
      <description><![CDATA[This article proposes a vehicle collision risk assessment (VCRA) framework based on dual uncertainty, with the aim of improving the precision of collision risk prediction. Firstly, this study utilized models that considered the uncertainty of inputs such as road geometry, environmental factors, and driver operations, as well as the uncertainty of the vehicle model itself. Secondly, the model quantified the uncertainty of vehicle position using an extended Kalman filter and then determined whether to switch the vehicle model based on the weight of the yaw rate caused by the geometric characteristics of the road so that these models are more suitable for the current driving scenario. Thirdly, the collision risk of the predicted trajectory was evaluated in this article, and the collision risk was quantified into vehicle collision probability by Monte Carlo simulation. Meanwhile, this article defined some certain regions that were applied to help determine whether it was necessary to start the assessment algorithm. Finally, the study validated the effectiveness and accuracy of the method through three typical test scenarios specified by the China New Car Assessment Program (C-NCAP). Through comparison, the VCRA framework can accurately assess collision risk at time to collision (TTC)?=?2.2?s, which is earlier than the collision risk output time of TTC?=?1.7?s in C-NCAP (scenario b). The VCRA framework also provides a new upgrade approach for active safety test scenarios in regulations.]]></description>
      <pubDate>Fri, 30 Jan 2026 14:48:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659301</guid>
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    <item>
      <title>Kentucky Traffic Collision Facts Calendar Year 2023 Report</title>
      <link>https://trid.trb.org/View/2642335</link>
      <description><![CDATA[Kentucky’s Traffic Collision Facts is based on collision reports submitted to the Kentucky State Police Records Branch. As required by Kentucky Revised Statute 189.635: “Every law enforcement agency whose officers investigate a vehicle accident of which a report must be made...shall file a report of the accident...within ten days after investigation of the accident upon forms supplied by the bureau.” The stated purpose of this requirement is to utilize data on traffic collisions to improve the Commonwealth’s traffic safety program. Unless otherwise noted, data in this publication are for public roads only. Data contained in this report are based solely on the observations and judgements of the state and local police officers who investigated each collision. Collision data are contained in an automatic system called the Collision Report Analysis for Safer Highways (CRASH). This system carries out edit checks for accuracy, which may include manual adjustments based on the Fatal Accident Reporting System (FARS). Computer tabulations and summaries are again checked for accuracy before information is released or disseminated. It is hoped that the detailed information presented in this report will, in fact, “improve the traffic safety program within the Commonwealth.” The National Highway Traffic Safety Administration (NHTSA) Manual on Classification of Motor Vehicle Traffic Crashes is used to ensure uniformity and compliance with federal requirements.]]></description>
      <pubDate>Mon, 12 Jan 2026 09:13:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2642335</guid>
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    <item>
      <title>‘In the name, she lives on’: responsibilities and rehumanization in survivor narratives of vehicular violence</title>
      <link>https://trid.trb.org/View/2554298</link>
      <description><![CDATA[Motor vehicle crashes form a global, though unequally distributed, violence that has killed more people than World War II. Yet, dominant discourses in politics, industry, and media render invisible this violence itself and its political roots in the social and material reconstruction of space in favor of speed, efficiency, and, predominantly, automobility. The narratives of people impacted by vehicular violence remain unstudied, however. Crash survivors regularly participate in public debate, and survivor narratives more widely can have a strong influence on public perception. Drawing on mobilities literature as well as trauma and memory studies, this paper analyzes how survivors and deceased victims’ relatives in the Dutch context narrate three different themes of responsibility, and a fourth theme of rehumanization, in in-depth interviews. On the one hand, the authors find that the need to make sense of an impactful experience while surrounded by dominant discourses in society, leads survivors to adopt some of those discourses in their narratives. On the other hand, the authors identify their rehumanization of survivors and deceased victims and their absolution of individual drivers from culpability as hopeful starting points for resisting the automobility system’s dehumanization and for rethinking a-spatial perspectives on ‘safety’ that place responsibility solely on individuals.]]></description>
      <pubDate>Mon, 21 Jul 2025 14:42:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2554298</guid>
    </item>
    <item>
      <title>Navigating the Future: Creating a Compensation Framework for Autonomous Vehicle Incidents</title>
      <link>https://trid.trb.org/View/2543220</link>
      <description><![CDATA[While autonomous vehicles (AVs) are likely to significantly decrease automobile fatalities, accidents will inevitably still occur. Current fault-based insurance assumes driver liability, in a driverless vehicle there is uncertainly around who is liable in a crash. The authors recommend that the U.S. Congress establish a victim compensation fund for those injured by AVs. This report examines the benefits of autonomous vehicles and outlines how a victim compensation fund could support the adoption of AVs. Questions Congress would need to consider to establish the fund are suggested related to: administration, funding, regulations, eligibility, data, victim fault, determination of payment, and vehicle mix transition period.]]></description>
      <pubDate>Mon, 09 Jun 2025 09:33:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543220</guid>
    </item>
    <item>
      <title>Overview of Motor Vehicle Traffic Crashes In 2023</title>
      <link>https://trid.trb.org/View/2539752</link>
      <description><![CDATA[There were 1,820 fewer people killed in motor vehicle traffic crashes on U.S. roadways during 2023, a 4.3-percent decrease from 42,721 in 2022 to 40,901 in 2023. It represents the second year-to-year decrease since 2021. The fatality rate per 100 million vehicle miles traveled (VMT) decreased by 6.0 percent from 1.34 in 2022 to 1.26 in 2023. The estimated number of people injured on our roadways increased in 2023 to 2.44 million, rising 2.5 percent from 2.38 million in 2022. This increase was not statistically significant. The injury rate per 100 million VMT remained the same at 75 in 2022 and 2023. The estimated number of police-reported traffic crashes increased from 5.93 million in 2022 to 6.14 million in 2023, a 3.5-percent increase which was not statistically significant. VMT for 2023, reported through the Federal Highway Administration (FHWA), increased by 1.6 percent from 3,196 billion in 2022 to 3,247 billion in 2023.]]></description>
      <pubDate>Mon, 21 Apr 2025 12:04:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539752</guid>
    </item>
    <item>
      <title>Post-traumatic stress disorder and associated factors among road traffic accident survivors in Sub-Saharan Africa: A systematic review and meta-analysis</title>
      <link>https://trid.trb.org/View/2521919</link>
      <description><![CDATA[Road traffic accidents have become a global public health issue, especially in low- and middle-income countries (LMICs). According to the World Health Organization (WHO) Global Road Safety Report 2018, there are over 1.35 million deaths related to road traffic accidents (RTAs) annually. Although several primary studies have been conducted to determine the prevalence and associated factors of post-traumatic stress disorder (PTSD) among RTA survivors in sub-Saharan Africa (SSA), these studies have reported inconsistent findings. Therefore, this study aimed to determine the pooled prevalence and associated factors of PTSD among RTA survivors in SSA. The studies were accessed through the Google Scholar, Scopus, PubMed, and Web of Science databases using search terms. Moreover, citation tracking was also performed. A random-effects DerSimonian-Laird model was used to compute the pooled prevalence of PTSD and determine associated factors among RTA survivors in SSA. A total of 17 primary studies with a sample size of 9,056 RTA survivors were included in the final meta-analysis. The pooled prevalence of PTSD among RTA survivors in SSA was 23.36% (95% CI: 18.36, 28.36); I² = 96.73%; P < 0.001). Female gender [AOR = 2.33, 95% CI: 1.80, 3.01], depression symptoms [AOR = 2.96, 95% CI: 2.17, 4.03], duration since the accident (1-3 months) [AOR = 2.08, 95% CI: 1.23, 3.52], poor social support [AOR = 2.97, 95% CI: 1.09, 8.11], and substance use [AOR = 3.31, 95% CI: 1.68, 6.52] were significantly associated with PTSD. The pooled prevalence of PTSD was low in SSA compared to studies that have been conducted outside the region. Female gender, depression symptoms, duration since the accident (1-3 months), poor social support, and substance use were the pooled independent predictors of PTSD among RTA survivors in SSA. Those RTA survivors with these identified risk factors would be screened and managed early for PTSD using pharmacological treatment and brief psychological intervention. Future researchers shall conduct further studies using different methods, including qualitative studies to identify additional predictors of PTSD among RTA survivors in SSA.]]></description>
      <pubDate>Sat, 29 Mar 2025 17:32:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521919</guid>
    </item>
    <item>
      <title>Factors of road accident compensation and their relative importance: A case study of India</title>
      <link>https://trid.trb.org/View/2492932</link>
      <description><![CDATA[Road accidents have become a global problem and have been widely addressed in various reports and research. However, the detrimental impacts of road accidents are rarely discussed. In developed countries, the fatality rate due to road accidents is fairly low and the mechanism to compensate the victims is comparatively robust, on the other hand, the fatality rate due to road accidents in developing countries is quite high and the compensation mechanism is quite abject. In view of the same, this paper is an attempt to look into the various parameters that are to be used to provide compensation to road accident victims. In this paper experts from different domains were selected. which included road safety experts, medical experts, rehabilitation center pundits, economists, sociologists, Insurance experts, and trauma center experts. These experts were interviewed to weigh the parameters of the road accident compensation equation. To evaluate the data and determine the components' ranking, the technique applies the Analytic Hierarchy Process (AHP). Based on the findings of this paper it can be said that the most important factor that needs to be taken into account is victims’ related costs while the least important factor as per the findings of the paper is cost related to property damage.]]></description>
      <pubDate>Fri, 21 Feb 2025 17:08:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2492932</guid>
    </item>
    <item>
      <title>Photogrammetric Model and Technology Used in Road Traffic Accident Scene Measurement</title>
      <link>https://trid.trb.org/View/2264028</link>
      <description><![CDATA[In a newly developed system, a video camera is adopted as a road traffic accident scene data capturing and recording tool. The computer vision and photogrammetric principle and technologies are used. According to the analyses on relationship of a point in the world coordinate and its image in the lens coordinate, a fundamental photogrammetric model is deduced. The photogrammetric model is optimized and an efficient calculation algorithm is programmed to achieve high measurement accuracy. This technique presents an efficient non-destructive method for a wide range measurement. The main method to survey the road traffic accident scene is still with ruler and camera in the world nowadays. However, this method can't meet the need of modern survey condition, especially used in the highway accident measurement. In order to solve this problem, a new system is developed by the authors with the video camera as data capture tool. The photogrammetry and computer vision techniques are integrated in this system. The system can meet the requirement of the practical measurement, increase the data collection efficiency and decrease the accident processing time.]]></description>
      <pubDate>Wed, 29 Jan 2025 16:59:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2264028</guid>
    </item>
    <item>
      <title>Special Crash Investigations: Remote Move-Over-Law Crash Investigation; Vehicle: 2018 Pierce Fire Truck; Location: Colorado; Crash Date: January 2022</title>
      <link>https://trid.trb.org/View/2485239</link>
      <description><![CDATA[This report documents the remote investigation of a crash selected by the National Highway Traffic Safety Administration to be included in its Move-Over-Law investigations. The crash occurred in the pre-dawn morning in January 2022 in dark, snowy conditions in Colorado. The struck vehicle was a 2018 Pierce Manufacturing aerial fire truck, which was stopped in the right lane in response to a prior crash. Two fire fighters were in the Pierce’s second row, an unbelted 31-year-old male and unbelted 27-year-old male. The striking vehicle was a stolen 2005 Chevrolet Silverado C1500 pickup driven by a male of unknown age. A second male was seated in the front-right seat. Their belt usage is unknown. The second-row-right seat was occupied by a belted 25-year-old female. The Chevrolet was traveling westbound in the right lane at a police-estimated speed of 105 km/h (65 mph). The Chevrolet's front plane struck the Pierce’s left plane. The Pierce was displaced forward. The Chevrolet was displaced to the left, departed the roadway, and came to rest facing west in the grass median. The Chevrolet’s two front-row occupants fled the scene on foot. The Chevrolet's second-row occupant sustained “C” (possible) injuries and was transported to a local hospital by ambulance. Her treatment status is unknown. The Pierce's second-row-left occupant sustained “B” (suspected minor) injuries to his head. He was transported to a local hospital where he was treated and released. The second-row-right occupant was not injured or transported.]]></description>
      <pubDate>Tue, 07 Jan 2025 10:13:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2485239</guid>
    </item>
    <item>
      <title>NMDOT Pedestrian Safety on Arterials</title>
      <link>https://trid.trb.org/View/2422612</link>
      <description><![CDATA[New Mexico’s pedestrian fatality rate has been in the top ten for the United States (US) for 27 of the last 28 years. New Mexico Department of Transportation (NMDOT) roadways were host to 26.7% of the pedestrian crashes and 38.3% of the pedestrian fatal and serious injury (KA) crashes that occurred in New Mexico between 2015 and 2019. The authors identify an NMDOT Safety Priority profile consisting of NMDOT roads in urban areas with five lanes, signed at 35 mph, carrying about 10,000 or fewer vehicles per day, and with available roadway width (from outside of sidewalk to outside of sidewalk) of between 88-96 feet. Reconfigurations of similar roadways from across the country primarily consisted of reductions to three lanes (i.e., one travel lane in each direction with a two-way left-turn lane (TWLTL)), the addition of pedestrian amenities such as curb extensions or pedestrian refuges, and the installation of bicycle lanes. Based on the successful roadway reconfigurations that were identified and from other past research, the authors made design recommendations that fundamentally shift the configuration of NMDOT roadways in urban areas by reducing the number of travel lanes and adding pedestrian and bicycle facilities. Reducing the number of lanes should reduce motor vehicle speeds, reduce turning conflicts, reduce crossing distance and complexity for pedestrians, and provide safe dedicated space for both pedestrians and bicyclists. Fundamentally rethinking the lane configuration will be a more economical method of improving traffic safety outcomes than spot-treatments such as adding traffic-controlled crossings along still fundamentally unsafe roadway corridors. Such reconfigurations may also help to build a sense of place and generate investment in the downtown areas through which they pass.]]></description>
      <pubDate>Wed, 28 Aug 2024 16:59:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2422612</guid>
    </item>
    <item>
      <title>Modeling traffic fatalities to assess the significance of gender in road safety</title>
      <link>https://trid.trb.org/View/2400568</link>
      <description><![CDATA[Road crashes continue to be the leading cause of death across all age groups despite several efforts being taken by the government and non-government organizations. The brunt of traffic fatalities affects males and females differently. Studies on assessing gender-wise exposure to road safety are limited in the Indian context. Therefore, an attempt is made to understand the role of gender in fatality risk assessment. The aim is to evaluate the change in exposure to different motorization growth scenarios for males and females. The fatality-risk model is developed based on the interaction between the victim (pedestrian, bicyclist, two-wheeler, car, and bus) and threat (pedestrian, bicyclist, two-wheeler, car, bus, and environment) road users combined with the at-risk distance traveled by modes for Bangalore Metropolitan Region, India. Male and female traffic fatalities are estimated based on the interaction severity between victim and threat modes. The results show that pedestrians, bicyclists, and two-wheeler users are more vulnerable. Both males and females demonstrate an increased risk of traffic fatalities with the rise in the motorization mode share due to the severity of interaction of vulnerable modes with another motorized mode such as two-wheelers and cars. The most significant reduction in road crashes for both genders is observed for the High Bus scenario, which assumes that 80 % of the total motorized distance is traveled by bus. Maximum traffic deaths are estimated for the High Car scenario; the fatalities remained high even with 100 % motorization indicating higher risk with high motorization rates. The study outcomes would help practitioners and decision-makers to make informed policy decisions. Further, the fatality risk is assessed considering the interaction of at most two road users, and the study can be extended to the simultaneous evaluation of multiple road users.]]></description>
      <pubDate>Thu, 25 Jul 2024 11:40:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2400568</guid>
    </item>
    <item>
      <title>Accident victim characteristics and identification of key parameters for compensation in Indian context</title>
      <link>https://trid.trb.org/View/2301471</link>
      <description><![CDATA[Road accidents generate substantial personal and national losses. Road accidents reduce victims' and their families' productivity, which slows the nation's economic growth. The victim or victim's heir must be compensated for the mishap's losses. It is important to understand traffic accident victims' concerns before calculating and rewarding a suitable compensation. This study examines traffic accident victims' issues. In this paper, the interviews were conducted in two phases: a pilot survey of seven experts and 38 victims/relatives of victims, based on their opinions, the questionnaire was revised, and the final interviews were conducted in the second phase, which included 126 road accident victims and 27 expert interviews. This paper summarises the results of the second phase along with its statistical interpretations. This paper also emphasises traffic accident victims' debilitation and daily issues. Government and local authorities often announce compensation, although they rarely consider a victim's socioeconomic situation. The arbitrarily set amount may provide immediate relief, but it ignores traffic accident victims' socio-demographics. This study will highlight those aspects that will enable policymakers quickly and efficiently decide remuneration for each individual.]]></description>
      <pubDate>Fri, 26 Jan 2024 10:02:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2301471</guid>
    </item>
    <item>
      <title>Analysis of Injury Severity of Drivers Involved Different Types of Two-Vehicle Crashes Using Random-Parameters Logit Models with Heterogeneity in Means and Variances</title>
      <link>https://trid.trb.org/View/2277083</link>
      <description><![CDATA[This study proposes random-parameters multinomial logit models, with heterogeneity in means and variances, to explore the differences in the factors influencing injury severities of drivers involved in different types of two-vehicle crashes. The models are verified using crash data from the United Kingdom (UK) over three years (2016–2018). Three types of crashes are separately identified (car-car, car-truck, and truck-truck crashes). In this study, a wide variety of potential variables, including the driver, vehicle, road, and environmental characteristics, are considered, with two possible injury-severity outcomes: severe and slight injury. The results show that unobserved heterogeneity existed for young drivers in both car-car and truck-truck crash models and the 30 mph speed limit in the three separate models. Remarkably variations are observed in crashes involving different types of vehicles. The driver’s age and gender, speeding, sideswipes, presence of junctions, weekdays, unlit, and weather conditions significantly impact driver-injury severities in various types of vehicle crashes. These findings are expected to help policymakers seek to improve highway safety and implement proper safety countermeasures.]]></description>
      <pubDate>Mon, 13 Nov 2023 09:01:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2277083</guid>
    </item>
    <item>
      <title>Development of crash prediction models by assessing the role of perpetrators and victims: a comparison of ANN &amp; logistic model using historical crash data</title>
      <link>https://trid.trb.org/View/2186801</link>
      <description><![CDATA[Road traffic injuries cost countries 3% of their annual GDP. In developing countries like India, every year around 150,000 people die on roads. The type of vehicles involved in a crash contribute majorly to the outcome of casualty (injury/death). Barring few studies, literature are less regarding the role of vehicle as perpetrator and victim on road crash fatalities. Historical crash data has been used in the present study to examine the role of vehicles (both as perpetrator & victim). The study reveals that victim’s effect is more as compared to perpetrator/accused for determining the outcome of crash. Heavy vehicles as perpetrator, and self-hitting vehicles along with pedestrians as victims have higher fatality rates. Binary logistic regression and artificial neural network (ANN) has been utilized for developing prediction models. Binary logistic model predicted around 75% of outcomes correctly with default cut-off value (0.5). However, based on reported crash data, where 19% of total crashes lead to deaths, 0.19 has been proposed as cut-off value which increases the accuracy of the predictions. Accuracy of ANN technique directly depends on the number of crashes reported for a definite pair of perpetrator and victim and the type of validation technique used (Holdback/K-Fold) along with the type of hidden layer chosen for the study based on different types of sigmoid activation function. ROC curves in ANN suggest that the analysis can predict 75% of the outcomes which can be increased by deleting the pairs of vehicles which are present/have occurred in very less number. A comparison has been made between the two techniques based on their advantages and limitations. The developed models can be used as safety indicators based on composition of traffic flow on urban roads.]]></description>
      <pubDate>Mon, 23 Oct 2023 16:52:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2186801</guid>
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