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    <title>Transport Research International Documentation (TRID)</title>
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    <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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Pipeline Investigation Report: Enbridge Inc. Natural Gas–Fueled Home Explosion and Fatality, South Jordan, Utah, November 6, 2024</title>
      <link>https://trid.trb.org/View/2689424</link>
      <description><![CDATA[On November 6, 2024, about 3:09 p.m., a natural gas–fueled explosion fatally injured one person and destroyed a home in South Jordan, Utah. Several nearby residences were damaged, and families displaced. The South Jordan Fire Department responded, arriving on scene about 3:15 p.m. Technicians from Enbridge Inc. (Enbridge) subsidiary Enbridge Gas Utah, the natural gas service provider to the home, responded and arrived on scene about 3:45 p.m. Enbridge isolated the leak about 12:16 p.m. on November 7. At the time of the explosion, conditions were daylight and clear; the temperature was 41°F with no precipitation. The National Transportation Safety Board (NTSB) determined the probable cause of the explosion was a through-wall crack that formed in the 1976 vintage 4-inch-diameter Aldyl A polyethylene gas distribution main from a rock impingement, allowing natural gas to leak, migrate about 150 feet, and accumulate in a home, where it was ignited by the home’s furnace. Contributing to the severity were insufficient safeguards to mitigate hazards presented by the leak.  ​]]></description>
      <pubDate>Thu, 16 Apr 2026 16:54:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689424</guid>
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    <item>
      <title>Place of Last Drink (POLD) Implementation: Examination of Five Sites Using the POLD Implementation Framework</title>
      <link>https://trid.trb.org/View/2691795</link>
      <description><![CDATA[Alcohol-impaired driving continues to be a significant problem in the United States. In 2022, over 13,500 fatalities were attributed to crashes where at least one driver was alcohol-impaired. Alcohol-impaired drivers were involved in 32% of traffic fatalities in 2022. Alcohol is a serious public health problem related to injury, death, and violence. One factor contributing to impaired driving is overservice at licensed alcohol establishments—research shows that over 80% of bars and restaurants will sell alcohol to someone who appears obviously intoxicated. Place of Last Drink (POLD) is an approach that law enforcement agencies can use to address alcohol-impaired driving incidents by identifying alcohol establishments that overserve alcohol. To implement POLD, a law enforcement officer collects information on the last place someone consumed alcohol before an alcohol-related traffic stop or incident. Authorities can work with an establishment identified as the POLD, require corrective action, or impose sanctions. Only a handful of studies have examined implementation of POLD, but they identify substantial differences in implementation and use of POLD data. To assess its effectiveness, it is essential to understand how POLD is implemented. This study examines case studies from five sites that are implementing POLD using a POLD implementation framework to compare and contrast implementation, identify differences. common strategies, strengths, and areas for improvement to inform and improve future implementation. The study shows there is a need to better understand how POLD is implemented and used in other sites, to develop clearer recommendations for its use.]]></description>
      <pubDate>Wed, 15 Apr 2026 11:31:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691795</guid>
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    <item>
      <title>Converting structured data to point cloud data: A traffic accident severity prediction model based on sparse 3D convolution</title>
      <link>https://trid.trb.org/View/2686115</link>
      <description><![CDATA[The convolutional neural network (CNN) is currently the most popular model for predicting traffic accident severity. Existing studies typically convert numerical data into single-channel images using dimensionality reduction techniques, which are then used as input for CNN-based prediction models. In this study, the authors propose a novel 3D-CNN model, T2pNet, which maps traffic accident features to a higher dimensional space, converts them to point cloud data, and adjusts and optimizes the coordinates of points in the point cloud based on feature correlation. Compared to the transformation technique employed in 2D-CNN models, this approach reduces the information loss generated during the dimensionality reduction process and introduces a richer spatial feature representation for the traffic accident data. The authors compare the performance of T2pNet against state-of-the-art models. Results demonstrate that T2pNet outperforms the other models, particularly in predicting the severity of serious and fatal accidents, with a substantial lead in F1 score and recall metrics. Furthermore, the authors visualize the convolutional layers of T2pNet using the gradient-weighted class activation mapping technique, which explains the model’s advantages in capturing the correlation and combination relationships among traffic accident features.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686115</guid>
    </item>
    <item>
      <title>Undercounts stemming from misclassification derived from fatal injuries in traffic crashes in Colombia, 2010 to 2021</title>
      <link>https://trid.trb.org/View/2680649</link>
      <description><![CDATA[To identify and address potential misclassification of traffic fatalities in Colombia from 2010 to 2021. For an ecological study, the authors employed national records and databases. A database was consolidated to include information on the fatality occurrence site, area, place of death, year of occurrence, marital status, age, and enrollment in social security. Generalized linear regression models were used to detect and adjust possible errors in records due to misclassification starting from existing data, allowing reclassification with a high probability of specific garbage codes being valid, potentially associated with mortality caused by traffic. In 2010; there was a mortality rate of 13.3 deaths per 100,000 population, while in 2021; it was 15.1/per 100,000 population. In 2020; from the effects of pandemic-related confinement, the risk came down to 11.5/100.000 population. With the imputation, these records increased from 14.9 (2010) to 16.4 (2021); the most notable rise was among motorcyclists, who contributed 62%, with a marked increase in 2021:13/100.000 population, while pedestrians contributed 27.2%, cyclists: 4% and vehicle occupants: 6.5%. Over the past decade, Colombia has stood out as one of the few countries worldwide that have been unable to reduce traffic-related mortality. The potential underestimation of the problem likely exacerbates this challenge due to record misclassification or measurement errors, which may be as high as 10%. Motorcyclists are particularly vulnerable, facing a significantly increased risk of death. To address this critical issue, cross-sectoral and inter-institutional policies, and plans are urgently needed to mitigate the high incidence of motorcycle fatalities and break the cycles of poverty and orphanhood they can cause.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680649</guid>
    </item>
    <item>
      <title>Research on nighttime road visibility monitoring based on video images</title>
      <link>https://trid.trb.org/View/2680643</link>
      <description><![CDATA[Road traffic accidents have become a serious social problem, with a significant proportion of accidents caused by insufficient visibility on roads at night. Therefore, nighttime road visibility detection based on video images has become one of the difficulties and a key issue in domestic and international research. This study analyzes the importance of nighttime road visibility monitoring, introduces the structure, working principle, and monitoring method of a video image nighttime visibility monitoring system, and proposes a nighttime road visibility monitoring method based on video images. Based on the characteristics of nighttime images, an improved dark channel prior method was adopted to calculate the nighttime road visibility. This method mainly includes eight steps: video image acquisition, image grayscale processing, calculation of image average variance, image average gradient, drawing grayscale histograms, image enhancement based on the calculated values, calculation of transmittance, and calculation of visibility. The experimental results show that the proposed night road visibility monitoring method based on video images can effectively realize real-time monitoring of night road visibility, effectively overcome the inherent defects of traditional methods, and the constructed night visibility monitoring framework can realize high-precision visibility calculation, and has broad application prospects. Through adaptive threshold and adaptive filtering technology, the improved dark channel algorithm has shown competitive advantages in both image quality index and practical application effect, especially in noise suppression and edge preservation. However, under extreme illumination conditions, the algorithm still has room for improvement in the processing of the strong light source region, and the dark channel prior may lead to bias in the transmission estimation.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680643</guid>
    </item>
    <item>
      <title>Exploring run-overs in two-wheeler–motor vehicle crashes: Injury severity, mediating effects, and implications for prevention</title>
      <link>https://trid.trb.org/View/2680642</link>
      <description><![CDATA[Concerns regarding the injuries and fatalities of two-wheeler riders are rising in China, particularly when the riders are run over by motor vehicles. This article aims to: (1) examine the impact of being run over on the severity of injuries sustained by two-wheeler riders, and to ascertain whether run-overs act as mediators in the relationship between injury severity and other contributing factors, and (2) identify the main causes leading to run-over crashes. 2,281 two-wheeler–motor vehicle crashes were collected from the China In-Depth Accident Study Database from 2017 to 2020. Random-parameter binary logit model and random-parameter ordered logit model were used to investigate factors influencing run-overs and injury severity. Based on the marginal effects of the two models, path analysis was conducted to quantify the direct and indirect relationships between the contributing factors and both run-overs and the severity of injuries sustained. Model results reveal that being run over is a predominant factor exacerbating the severity of injuries among two-wheeler riders. Furthermore, the occurrence of run-overs is significantly correlated with riders’ age, crash location, two-wheeler length, and the presence of work zones. Additionally, the influence of certain factors on injury severity is mediated through the occurrence of run-overs, with both partial and full mediation effects observed. For example, the impacts of riders’ age and two-wheeler’s primary crash position on injury severity are partially mediated by run-overs. This indicates that both the age of riders and the primary crash position contribute to severe injuries, operating through direct effects as well as in the context of run-overs. Findings of this study highlight the mediating role of run-overs in the injury severity of two-wheeler riders. Practically, the findings provide insights for roadway design and installation of proactive equipment aimed at mitigating the risk of run-overs and the associated severe injuries.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680642</guid>
    </item>
    <item>
      <title>Driving behavior performance from tunnel main road to underground merging area: A real vehicle study based on speed and lateral offset</title>
      <link>https://trid.trb.org/View/2680637</link>
      <description><![CDATA[Multi-entry underpass road tunnels feature long entrance downhill sections and underground merging areas where main and secondary roads converge. These complex driving environments can lead to variations in driver speed and lateral offset, increasing the risk of traffic accidents. Therefore, this study aims to analyze the speed and lateral offset characteristics in different tunnel sections and their impact on traffic safety, providing support for traffic control and safety improvements in multi-entry underpass tunnels. This study conducted real-vehicle natural driving tests using test vehicles equipped with an inertial navigation system and Mobileye. Based on changes in tunnel alignment and road parameters, the study divided the test sections into five segments: tunnel external section, entrance downhill section, entrance internal section, underground merging section, and tunnel internal section. By analyzing the speed variation trends, lateral offset characteristics, and their interrelationships across these sections, a standardized relative deviation fraction was introduced to quantitatively compare driving behavior in key sections, revealing differences in driving patterns and potential safety risks across different road segments. The speed growth rate in the entrance downhill section was the highest at 15.09%. In contrast, drivers in the underground merging section had the lowest average speed at 54.057 km/h and the highest speed dispersion. The underground merging section had the lowest rate of lateral offset change but the highest dispersion in lane offset within this section. Conversely, the entrance downhill section showed the smallest dispersion, with a standard deviation of only 0.111. In addition, research found that the driving distance in each road section is positively correlated with vehicle speed and negatively correlated with lane offset. Through real-vehicle tests, this study analyzed the speed, lateral offset, and driving safety characteristics of different sections in multi-entry tunnels. The results indicate that the entrance downhill section and underground merging section pose higher driving risks, as fluctuations in speed and lateral offset contribute to driving instability. These findings reveal the driving risks associated with specific sections of multi-entry underpass road tunnels and provide important references for tunnel traffic management and safety optimization.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680637</guid>
    </item>
    <item>
      <title>Analyzing extreme multi-vehicle rear-end collision risks in adverse weather through Generalized Pareto Regression Trees</title>
      <link>https://trid.trb.org/View/2686646</link>
      <description><![CDATA[The study seeks to explore rear-end collision risks in multi-vehicle car-following scenarios under adverse weather conditions by proposing an integrated framework. The integrated framework is applied to a case study of three-vehicle car-following scenario in Norway without loss of generality. For identifying car-following groups with extreme collision risks, the collision risk of each group in the raw dataset is evaluated using an extended probabilistic driving risk field. Quantitative collision risks are analyzed to fit the Generalized Pareto distribution, and high-risk scenarios screened via mean residual life plots and threshold stability plots. To determine risk-contributing factors, Generalized Pareto Regression Trees (GPRT) are constructed to pinpoint significant influences on rear-end collision risks. By integrating the classification and regression trees with extreme value theory, the GPRT discards data assumptions and covariate continuity requirements of most extreme value analysis (e.g., extreme quantile regression). Moreover, the GPRT not only identifies the hierarchical structure of variables affecting rear-end collision risks but also determines risk-impact thresholds for covariates, offering superior interpretability and engineering applicability. The results show that revealed risks conform well to the Generalized Pareto distribution, allowing for the formulating Generalized Pareto regression trees. Compared to the Generalized Additive Model (GAM) and Negative Binomial Regression (NBR) methods, the GPRT approach demonstrates superior performance in balancing risk fitting accuracy and model complexity. Vehicle speeds, weights, and headways emerge as critical factors for collision risks under clear, rainy, and snowy conditions. As weather conditions deteriorate from clear to rainy or snowy, the influence of vehicle speed and weight diminishes, while the influence of headway and road surface conditions becomes more pronounced. Collision risks are high on sunny days, regardless of whether the middle vehicles of three-vehicle groups are light or heavy vehicles. The integrated evaluation framework developed in this study provides a tool for car-following safety assessment under extreme weather conditions.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686646</guid>
    </item>
    <item>
      <title>Performance of AEB systems in preventing car-pedestrian collisions</title>
      <link>https://trid.trb.org/View/2686645</link>
      <description><![CDATA[This study quantifies the performance of autonomous emergency braking (AEB) systems in preventing car-pedestrian collisions in Japan. The study uses data on Japanese traffic accidents compiled by Japan’s National Police Agency, restricting the analysis to collisions for which the authors could determine whether the primary party’s car was equipped with an AEB system. Poisson mixed-effects regression analyses are conducted using data for 2022 and 2023 to quantify the collision avoidance performance of cars that were first registered in 2021 and equipped with AEB systems compared with cars without AEB systems that were first registered in 2015 or 2016. Our analysis of collisions of all injury levels reveals that AEB-equipped cars that were first registered in 2021 had 12.3% (95% confidence interval [CI] 6.2%–18.0%) fewer accidents than non-AEB-equipped cars that were first registered in 2015 or 2016. The reduction for fatal and serious injury collisions alone was higher, at 15.6% (95% CI 2.2%–27.2%). A day/night analysis demonstrates a 7.6% (95% CI −0.9%–15.3%; ns) reduction in daytime collisions and a 19.0% (95% CI 9.9%–27.1%) reduction in nighttime collisions. Analysis of three car-pedestrian collision scenarios reveals a 12.1% (95% CI −0.3%–23.0%; ns) reduction in collisions of all injury levels between forward-moving cars and street-crossing pedestrians, an 9.7% (95% CI −2.2%–20.2%; ns) reduction in collisions between right-turning cars and street-crossing pedestrians, and a 21.8% (95% CI 4.8%–35.9%) reduction in collisions between forward-moving cars and pedestrians walking in the same or opposite direction. The results demonstrate that AEB systems are effective in reducing car-pedestrian collisions; however, performance remains low compared with the effectiveness of AEB in reducing rear-end collisions. This indicates that considerable room remains for technological improvement.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686645</guid>
    </item>
    <item>
      <title>Trends in road traffic injury mortality and age-period-cohort analysis among children and adolescents in China and globally</title>
      <link>https://trid.trb.org/View/2686643</link>
      <description><![CDATA[Road traffic injuries are a leading cause of death among children and adolescents under 19. Despite a decline over the past 30 years, mortality rates remain high. This study analyzes road injury mortality rates trends among children and adolescents aged 0–19 in China and globally from 1990 to 2021. Using the Global Burden of Disease (GBD) 2021 database, the authors gathered age-standardized and crude mortality rates for road injuries among children and adolescents in China and worldwide from 1990 to 2021. Joinpoint regression models were used to describe trends, while age-period-cohort (APC) models and Estimated Annual Percentage Change (EAPC) analyses evaluated mortality trends and changes from 1990 to 2019. In 2021, the age-standardized mortality rate (ASMR) for road traffic injuries in China was 4.38 per 100,000 population, lower than the global ASMR of 5.96 per 100,000. Joinpoint regression showed a decrease in road injury mortality for the under-5 age group in China from 1990 to 2021. For the 5–9, 10–14, and 15–19 age groups, mortality rates initially decreased, rose, and declined. Globally, road injury mortality rates decreased for all age groups under 15 and for females aged 15–19; for males aged 15–19, rates decreased, rose slightly, and then decreased again. The APC model indicated that the risk of road injury death varied by age, period, and cohort, with age-specific increases and decreases and a general downward trend across periods in China and globally. EAPC analysis from 2010 to 2019 showed a significant reduction in China (EAPC = −6.92%) compared to a modest decline globally (EAPC = −3.39%). From 1990 to 2021, China’s road traffic injury mortality rate demonstrated a continuous decline. Since 2014, China’s mortality rate from road traffic injuries has remained below the global average. However, the mortality rate among males remains higher than that of females. Notably, the mortality rate among Chinese children under the age of 5 has shown an upward trend.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686643</guid>
    </item>
    <item>
      <title>Systematic review of interventions to increase the visibility of pedestrians to prevent injuries caused by traffic accidents</title>
      <link>https://trid.trb.org/View/2686642</link>
      <description><![CDATA[Road accidents cause millions of deaths and disabilities globally, especially among pedestrians who are more vulnerable. Effective measures are needed to protect them, and this study focuses on identifying interventions to improve pedestrian visibility in traffic injuries. This systematic review followed PRISMA guidelines and used search terms related to injury, pedestrian, visibility, and study design. Searches were conducted in databases like PubMed, ScienceDirect, Web of Science, Scopus, SAGE journals, and Cochrane Library until March 2024. Studies included were in English, peer-reviewed, and focused on pedestrians of all ages. Various experimental and quasi-experimental studies were considered. Interventions aimed at improving pedestrian visibility to prevent traffic injuries were classified into five categories. The quality of selected articles was assessed for potential biases using JBI checklists. Initially, 5,144 abstracts were identified. After applying exclusion criteria, 5,028 were removed, leaving 116 abstracts for review. Out of these, 21 were selected for full-text review. Finally, nine studies were chosen for further evaluation. These studies included one randomized controlled trial, six quasi-experimental studies, and two pretest/post-test interventions. Follow-up periods varied from immediate to one year, and outcomes were primarily measured by observation in eight studies. The interventions employed included educational/behavioral, technological, and multifaceted approaches, all of which significantly improved outcomes over the duration of the studies. All studies were conducted in high-income countries. The quality of the studies varied, with two studies rated as strong quality, six as medium quality, and one as weak quality. The study emphasizes the importance of visibility-enhancing measures for reducing pedestrian injuries but identifies shortcomings in study design, theoretical frameworks, and generalizability. It suggests future research should adopt robust methods, incorporate validated models, and evaluate legal and community-specific factors to create more effective safety measures.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686642</guid>
    </item>
    <item>
      <title>Analysis on Operation Mechanism of Emergency Rescue for Rail Passenger Train Traffic Accidents</title>
      <link>https://trid.trb.org/View/2113847</link>
      <description><![CDATA[The high-speed railway has a large passenger flow, so it is necessary to ensure the public travel safety and improve the effectiveness of emergency rescue methods. The feasible solutions to improve the emergency rescue effect of rail passenger train traffic accidents are as follows: 1. Establish the government led emergency rescue coordination mechanism, improve the high-speed railway emergency rescue system, mainly establish and improve the authoritative and efficient flexible emergency rescue regional linkage system. 2. Improve the accident emergency response mechanism, build the emergency rescue force system centered on the fire department, including the accident emergency response mechanism, formulate the emergency rescue plan, and quickly mobilize the rescue force system. 3. Improve the application mechanism of the rescue team, and realize the coordinated and unified rescue force, including the construction of professional rescue team and the volunteer rescue force, with the fire force as the main force and the volunteer rescue force as the auxiliary force. 4. Improve the high-speed railway emergency rescue dispatching command system, improve the high-speed railway emergency rescue dispatching command information construction system, establish the emergency command system, including digital information network system, emergency rescue early warning platform, emergency communication guarantee system.]]></description>
      <pubDate>Wed, 15 Apr 2026 08:31:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113847</guid>
    </item>
    <item>
      <title>Injury patterns in motor vehicle collision-adult pedestrian deaths</title>
      <link>https://trid.trb.org/View/2686635</link>
      <description><![CDATA[To describe fatal pedestrian injury patterns in adults 25–64 years old and correlate them with motor vehicle collision (MVC) dynamics and pedestrian kinematics using medicolegal death investigations data of MVCs occurring in the current Canadian MV fleet. MVC-pedestrian injuries were collated in an Injury Data Collection Form (IDCF) and coded using the Abbreviated Injury Scale (AIS) 2015 revision. The AIS of the most frequent severe injury was noted for individual body regions. The Maximum AIS (MAIS) was used to define the most severe injury to the body overall and by body regions (MAISBR). This study focused on serious to maximal injuries (AIS 3–6), that had an increasing likelihood of causing death. The IDCF was used to extract collision and injury data from the Office of the Chief Coroner for Ontario database of postmortem examinations done at the Provincial Forensic Pathology Unit in Toronto, Canada and other provincial facilities between 2013 and 2019. Injury data were correlated with data about the MVs, and MV dynamics and pedestrian kinematics. The study was approved by the Western University Health Science Research Ethics Board. There were 318 adults: 200 (62.9%) males and 118 (37.1%) females. Adult pedestrians comprised 47.5% (318/670) of all autopsied pedestrians. Vehicle type was known in 292 cases, and cars (n = 99/292, 33.9%) were the most frequent type of vehicle in single vehicle impacts; however, collectively vehicles with high hood edges (i.e., greater distance between the ground and hood edge) such as light trucks, heavy trucks and buses were in the majority. Pedestrian kinematics were known in 288/299 single impact-related deaths. Forward projection (n = 113/288, 39.2%) was the most frequent type and resulted from impacts with high hood edge vehicles. Compared to car impacts, pedestrians struck by high hood edge vehicles were more likely to be runover. Based on MAISBR ≥3 injuries, the head was the most severely injured (median MAISBR = 4), followed by neck (median MAISBR = 3), thorax (median MAISBR = 4), abdomen/retroperitoneum (median MAISBR = 4) and pelvis (median MAISBR = 3). About 70% of the pedestrians were in circumstances which increased their risk of being struck. More than half (176/318, 55.3%) had a positive toxicology result. About ¼ (27.4%) had a positive blood ethanol result. Nearly all pedestrians with positive alcohol results did not have the right of way when struck. The current study was a comprehensive analysis of fatal injury patterns and specific injuries in adult pedestrians struck by motor vehicles. By collation and analysis of comprehensive data derived from postmortem examinations, associations between injury patterns in the adult age group were correlated with a range of factors related to motor vehicle types, reflective of the current Canadian fleet, collision dynamics and pedestrian post-collision kinematics.]]></description>
      <pubDate>Tue, 14 Apr 2026 16:59:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686635</guid>
    </item>
    <item>
      <title>Study on the critical rollover conditions of trucks on curved highway segments under sand-accumulated road conditions based on LSTM</title>
      <link>https://trid.trb.org/View/2686634</link>
      <description><![CDATA[Sand on highways changes friction and superelevation, increasing rollover and skid risks. This study explores how sand accumulation affects truck driving stability and predicts the critical threshold at which rollover may occur under different road conditions and load scenarios. This paper is based on Trucksim simulations of truck driving conditions on the Umal Highway’s sand-prone curved sections (with curve radii of 60/100/215/400 m) under sand accumulation. Combining simulation data and using Long Short-Term Memory (LSTM) neural network algorithms, it predicts the lateral load transfer ratio (LTR) of a six-axle truck on the test section. The LSTM algorithm outperformed others, with superior accuracy metrics (R2 = 0.99644, MAE = 0.0050118, MAPE = 0.00026711, RMSE = 0.0063982). Sand accumulation is classified into thin and thick stages. The thin stage primarily affects road friction, while the thick stage increases curve superelevation. When the sand just covers the asphalt pavement pores and the thickness of the sand is more than 166 mm or more, the loading quality of more than 25 tons six-axle trucks are more prone to rollover, when the rollover speed and the normal road state rollover speed compared to significantly lower, compared with the standard speed limit, and the magnitude of the drop even up to 33%. The impact of varying sand accumulation conditions on speed thresholds differs significantly. Failure to promptly adjust speed limits during sand accumulation events may lead to rollovers even when drivers adhere to standard limits. The findings provide critical guidance for sand-prone highway management, recommending adaptive variable speed limits based on real-time sand thickness and road conditions to mitigate desert-related safety risks.]]></description>
      <pubDate>Tue, 14 Apr 2026 16:59:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686634</guid>
    </item>
    <item>
      <title>Unified Analytical Model for Predicting Internal Forces in a Bridge Column Subjected to Vehicular Collision</title>
      <link>https://trid.trb.org/View/2683035</link>
      <description><![CDATA[Numerical modeling for analyzing complex phenomena occurring under transient conditions is well established. Even then, simplifying a vehicular collision to an equivalent static load is prevalent in the structural design of a highway bridge. Use of algebraic expressions for determining the magnitude of the equivalent static load is preferred in practice, because the relative significance of various controlling parameters is automatically transparent. However, the equivalent static load approach has fundamental limitations, which are exemplified when dealing with changes to road traffic conditions requiring adjustments. The new analytical model introduced in this article is unique because it features the use of algebraic expressions founded on the theory of dynamics of structures for determining deflection and force demand resulting from a vehicular collision. The theoretical model, which is versatile and scale insensitive, has been verified by comparison against results from numerical simulations. The numerical model, in turn, has been verified experimentally. Application of the proposed model for predicting internal forces in a bridge column is illustrated. Input to the model is an idealized forcing function of time representative of the collision of a heavy truck. The proposed model, although simple to use, is shown to accurately capture the predominant effects that the inertial resistance of the bridge deck has on the shear force and bending moment of the bridge column. This inertia phenomenon cannot be accounted for by any existing calibrated static force model. Further, the effects of the crumbling of the vehicle resulting in prolonged contact with the column can also be captured but are neglected in a static analysis.]]></description>
      <pubDate>Tue, 14 Apr 2026 16:59:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683035</guid>
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