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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Implementing the Safe System Approach for Speed Management in Utah</title>
      <link>https://trid.trb.org/View/2685460</link>
      <description><![CDATA[Speed management is central to reducing fatal and serious injury crashes. The Safe System Approach, an emerging roadway safety paradigm in the United States, recognizes human error and vulnerability and focuses on minimizing crash severity through coordinated policy, design, and operational strategies. This research examines how Safe System Approach principles can be applied to speed management, with a focus on identifying practical countermeasures and implementation strategies relevant to the Utah Department of Transportation (UDOT). A compendium of practice was conducted to evaluate speed management countermeasures and document how cities and state Departments of Transportation are implementing Safe System Approach-based strategies. Thirty-two countermeasures were identified, including policy-based programs, automated enforcement, and roadway design treatments such as road diets, roundabouts, curb extensions, and gateway features. Findings show that implementing multiple, coordinated strategies is more effective than isolated interventions. The use of high-quality, context-sensitive speed and safety data supports proactive alignment of speed limits, roadway design, and safety goals. Establishing a clear Safe System Approach vision, supported by available federal and state tools, provides agencies with clear guidance for implementation. Based on the results of this research, several recommendations were provided for UDOT, including continuing to implement countermeasures for speed management in speed limit setting policies, evaluating current policies related to speed safety cameras in the state, incorporating Safe System Approach practices in the Strategic Highway Safety Plan, creating a speed management action plan, and placing a strong emphasis on community education tied to speed management.]]></description>
      <pubDate>Fri, 08 May 2026 17:09:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685460</guid>
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    <item>
      <title>A simulation-based testing framework for autonomous driving: ensuring realism and priority of test scenarios</title>
      <link>https://trid.trb.org/View/2616217</link>
      <description><![CDATA[Autonomous Vehicles (AVs) require extensive testing to ensure their safe integration onto public roads. Simulation provides a viable approach for AV testing, with the fidelity of simulations to real-world driving environments and the testing priority of simulation scenarios being of paramount importance. This study introduces a robust framework designed to enhance simulation-based AV testing by integrating a wide range of potential driving scenarios from real-world AV driving and accident data. We built a novel autonomous driving simulation test framework with MetaDrive simulator and ScenarioNet platform. The core module includes a set of scenario score calculation and update rules that consider multi-dimensional metrics; in addition, the framework has a built-in set of scenarios with real-world AV driving anomalies and an easy-to-use benchmark autonomous driving algorithm. Results indicate that the scenario scoring rule effectively prioritizes scenarios based on their criticality. The baseline algorithm demonstrates robust performance, achieving an average success rate of approximately 80% in simulation trials. The scenarios generated by the simulation testing platform closely mirror real-world conditions. The proposed framework provides substantial support for the advancement of autonomous driving algorithms and the thorough safety testing of AVs, thereby expediting the AV validation process.]]></description>
      <pubDate>Thu, 07 May 2026 11:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616217</guid>
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    <item>
      <title>Preventative &amp; Proactive National Safe Roads Challenge Program to Improve Driver Behaviours &amp; Tailor Safety Interventions</title>
      <link>https://trid.trb.org/View/2643340</link>
      <description><![CDATA[This Safe Roads Challenge (SRC) project used a mobile app to gamify driver behavior improvement. Participants (1,112 drivers) were assigned to teams and competed in the Great Canadian Driving Games during March-September 2025. Weekly challenges included interventions such as driving tips, motivational messages, and prizes to encourage safe driving. Data was collected on phone handling, acceleration, braking, cornering, steadiness, swerving, and speeding. Two of the project goals were to determine which types of messages and incentives resulted in driver behavior change and to use real-time driver data to identify high-risk locations. Data was used to develop Safety Performance Functions for signalized intersections in Alberta, Canada. Overall, participating in the challenges gradually improved driver behavior.]]></description>
      <pubDate>Tue, 05 May 2026 13:15:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643340</guid>
    </item>
    <item>
      <title>The Safety Impact of Road Debris: Updated Prevalences of Crashes, Injuries, and Deaths in the United States, 2018–2023</title>
      <link>https://trid.trb.org/View/2698320</link>
      <description><![CDATA[Road debris is a traffic safety concern for all road users. While there is no singular definition of road debris, it generally refers to any object on a roadway that does not belong in the driving environment. Common examples include unsecured cargo that fell from a vehicle or parts that separated from a vehicle. Road debris can contribute to crashes in three main ways: (a) one vehicle may be struck by debris that fell from or was set in motion by another vehicle; (b) a vehicle may strike debris on the roadway; or (c) a driver may crash into another road user or object after trying to avoid debris in the road. The current study seeks to quantify the contribution of road debris to motor vehicle crashes, injuries, and deaths in the United States using the most recent available data. The current study performed an in-depth analysis of police crash report narratives and diagrams from crashes that occurred in the state of Michigan in years 2018 through 2023 to examine the role of road debris in crashes with general characteristics suggestive of possible debris involvement, and then used those results in conjunction with national-level crash data to estimate how many crashes, injuries, and deaths nationwide were attributable to road debris. Results suggest that road debris was a factor in an average of 53,000 police-reported crashes resulting in 5,467 injuries and 72 deaths in each year from 2018 through 2023. This Research Brief discusses the role of road debris in traffic crashes, the nature and sources of the debris, and actions that both transportation professionals and the general public can take to reduce the risk of crashes involving road debris.]]></description>
      <pubDate>Tue, 05 May 2026 10:18:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698320</guid>
    </item>
    <item>
      <title>A Hybrid Expert System for Predicting and Controlling Traffic Crashes in Residential Areas</title>
      <link>https://trid.trb.org/View/2662677</link>
      <description><![CDATA[Residential traffic safety poses critical challenges due to dense pedestrian activity, irregular road layouts, and dynamic vehicle movement. Traditional static traffic control measures are often inadequate for such complex environments. This study presents a hybrid expert system that integrates Artificial Neural Networks (ANN), Fuzzy Neural Networks (FNN), and rule-based reasoning to predict and prevent traffic accidents in residential areas. The system utilises multi-source data—including road geometry, traffic density, and weather conditions— to generate short-term (real-time–24 h), medium-term (1–14 days), and long-term (15–90 days) accident-risk forecasts. Unlike conventional time-series models, the system leverages a hybrid data strategy, combining historical accident records, simulation-based scenarios, and real-time environmental inputs, to provide robust forecasts without requiring sequential tracking of prior risk scores. Applied to real-world data from Baghdad, the model achieved strong predictive performance, with AUC scores of 0.84, 0.87, and 0.78 across the respective time horizons. Beyond prediction, the system supports proactive interventions, such as optimised signal timing and targeted pedestrian safety enhancements, validated through traffic simulations. The results demonstrate the system's potential to inform intelligent, data-driven planning for accident prevention in urban neighbourhoods.]]></description>
      <pubDate>Fri, 01 May 2026 14:33:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2662677</guid>
    </item>
    <item>
      <title>A hybrid deep learning and large language models framework for ship collision accident analysis</title>
      <link>https://trid.trb.org/View/2664370</link>
      <description><![CDATA[Innovative artificial intelligence technology has shown potential in ship accident research. Deep learning (DL) is an effective method used to analyse structured accident data. Meanwhile, the development of Large Language Model (LLM) has brought new breakthroughs to the study of accident occurrence processes in reports. A novel framework combining the two is proposed in this study to provide new solutions to ship collision accident analysis. Firstly, a structured accident analysis method, named Hierarchical graph attention neural network with Transformer encoding (HGAT-Transformer) model, is proposed. Secondly, a pre-trained LLM is fine-tuned based on low-rank adaptation and a retrieval-augmented generation technology is adopted. Finally, a hybrid framework, named Deep learning-large language model combined framework to Ship Collision Analysis (DLSCA), is developed to guide better accident analysis. Real accident report data from different regions are used for DL model training and LLM fine-tuning. The results show that the generated analysis of the proposed framework is consistent with real accident reports in terms of accident analysis and responsibility assignment. The research demonstrates the potential of LLMs in providing guidance for accident analysis, and will provide new insights for research on ship collision accidents.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664370</guid>
    </item>
    <item>
      <title>Harnessing the Integrated Statistical Machine Learning for Traffic Crash Injury-Severity Modeling</title>
      <link>https://trid.trb.org/View/2664364</link>
      <description><![CDATA[Modeling the severity of traffic crash remains challenging due to the complexity, uncertainty, and heterogeneity inherent in crash datasets. Traditional statistical models often overlook interactions and structural dependencies, while machine learning methods, though effective with large datasets, struggle to capture spatial and temporal dynamics. To address these gaps, we propose the Latent Gaussian Process with Tree-Boosting Model (LGPBoost), which integrates tree-based machine learning with Gaussian process mixed effects models. This framework accounts for spatial, temporal, and grouped dependencies while capturing nonlinear feature–outcome relationships. To demonstrate the superiority of LGPBoost, we conducted a well-designed simulation experiment focused on datasets characterized by complex feature relationships and latent grouped random effects, as well as spatial and temporal variabilities. Applying the method to Florida motorcycle crashes (2014–2023) revealed that rural and less urbanized areas face significantly higher severe and fatal crash risks, underscoring the need for targeted enforcement and infrastructure improvements. Temporal instability analysis further showed evolving crash risks across regions, particularly in non-urban regions. By unifying spatial heterogeneity and temporal variability, LGPBoost provides a rigorous benchmark for reliability-oriented crash severity modeling, offering a comprehensive framework to identify risk factors, quantify non-linear effects, and capture intrinsic spatial-temporal dynamics.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664364</guid>
    </item>
    <item>
      <title>Time-clustering behavior in the time dynamics of maritime accidents for maritime safety management in the Yangtze River</title>
      <link>https://trid.trb.org/View/2697742</link>
      <description><![CDATA[Inland waterway transportation on the Yangtze River is vital for regional economic activity, yet maritime accidents remain a critical safety concern. Despite extensive research on accident causes, a gap remains in understanding the non-linear temporal clustering and memory effects of accidents. This study applies the Allan Factor (AF) method to analyze maritime accident time series in the Yangtze River, covering the period from 2011 to 2020, with a longitudinal robustness check extending to 2025. To ensure methodological rigor, the AF results are validated against the Fano Factor and confirmed via surrogate data testing. Findings reveal significant scale-dependent behaviors: the middle reaches exhibit persistent long-term clustering driven by hydrological constraints and traffic density, confirmed by a high scaling exponent, whereas the lower reaches demonstrate strong resilience with rapid dissipation of accident risks. Based on these distinct temporal patterns, precision-based safety strategies are proposed. Ultimately, quantifying these time-clustering behaviors provides a scientific basis for shifting from passive response to proactive traffic management.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:39:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697742</guid>
    </item>
    <item>
      <title>Toll road crash severity using mixed logit model incorporating heterogeneous mean structures</title>
      <link>https://trid.trb.org/View/2663007</link>
      <description><![CDATA[The current study examined 1,465 crash observations (2017–2021) from Louisiana, identifying significant variables grouped into three major categories: drivers’, crash, and road characteristics. Considering crash injury severity as a dependent variable, we employed classic Multinomial Logit (MNL) model, and several other models to address unobserved heterogeneity in crash data including Random Parameter Logit (RPL), Random Parameter Logit with Heterogeneity in Means (RPLHM), and Random Parameter Logit with Heterogeneity in Means and Variance (RPLHMV). Our findings highlight the impact of factors such as driver gender, age, traffic violations, driver distractions, crash types, surface conditions, and roadway attributes on crash injury severity. These insights emphasise the complexity of toll road safety and inform targeted interventions to mitigate crash injury severity. Notably, male drivers and those under 25 years old increased property damage likelihood, while factors like driver distractions and lower posted speed limits reduced the likelihood of severe injuries or fatalities.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663007</guid>
    </item>
    <item>
      <title>How to improve the AV safety: From the understanding of AV crashes to safety enhancement strategy</title>
      <link>https://trid.trb.org/View/2661857</link>
      <description><![CDATA[Autonomous vehicles (AVs) are increasingly prioritized in national transport strategies for their potential to improve safety and sustainability. Yet real-world crashes show that AVs remain vulnerable to safety risks, highlighting the need to understand their crash patterns and develop evidence-based countermeasures. The objective of this study is to investigate the factors associated with AV crashes and develop safety countermeasure to improve AV safety. 335 AV crash data from 2021 to 2022 was collected from collision reports from San Francisco at Traffic Analysis Zones (TAZ) level. Sociodemographic, built environment, land-use, and exposure variables were incorporated into Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. Compared with OLS, the GWR model provides a superior fit, as reflected by its lower AIC (535.17) and residual sum of squares (580.66). Model results revealed diverse effects of build environment on AV crashes across TAZs. Specifically, bus stop and transit lane densities exhibit strong negative associations with crash frequency, particularly in the southwestern regions. Bicycle parking density is negatively correlated with crashes. In contrast, wider sidewalks and higher proportions of speed limit zones are positively associated with AV crashes in certain urban areas. The impact of traffic signal density is spatially inconsistent—showing a crash-reducing effect in northeastern urban areas but a positive association in southwestern regions. Safety countermeasures were proposed from the perspective of understanding the AV crash influencing factors. The study underscores the significance of well-planned transportation facilities in enhancing AV safety.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:38:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661857</guid>
    </item>
    <item>
      <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>
      <guid>https://trid.trb.org/View/2661790</guid>
    </item>
    <item>
      <title>Drowsy Driving in Fatal Crashes, United States, 2017–2021</title>
      <link>https://trid.trb.org/View/2696853</link>
      <description><![CDATA[The contribution of drowsy driving in motor vehicle crashes is difficult to measure. Although reports by police officers who investigate crashes sometimes indicate that a driver was drowsy, data derived from these reports are widely regarded as substantial underestimates of the true scope of the problem. The current study used data derived from in-depth crash investigations conducted for the National Highway Traffic Safety Administration to develop and validate a model to impute driver drowsiness in cases when the driver’s pre-crash alertness or drowsiness could not be ascertained. The model was then used to impute the involvement of drowsiness in all fatal crashes nationwide that involved at least one car, pickup truck, van, minivan, or sport utility vehicle. Results show that an estimated 17.6% of all fatal crashes in years 2017–2021 involved a drowsy driver. These drowsy driving crashes resulted in 29,834 fatalities. The percentage of fatal crashes involving drowsy driving remained approximately constant over the study period; however, the annual number of fatal drowsy driving crashes increased significantly over the study period due to a large increase in total annual fatal crashes.]]></description>
      <pubDate>Thu, 30 Apr 2026 09:06:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696853</guid>
    </item>
    <item>
      <title>A Review of Safety and Operational Impacts of Various Speed Limits</title>
      <link>https://trid.trb.org/View/2581530</link>
      <description><![CDATA[Speed is the uttermost element influencing the incidence and ferocity of road accidents. The mitigation of the frequency of over speeding is seen as an essential goal for mitigating the number and severity of collisions, and the conventional method for doing so is through posted speed zoning or posted speed limit. Various speed control strategies are now being incorporated on roads to curb accidents’ frequency and risk. Some of the speed control strategies used worldwide are Uniform Speed Limit (USL), Differential Speed Limit (DSL), Variable Speed Limit (VSL), and Lane-Based Speed Limit (LBSL). In this paper, previous research based on the implementation of DSL on different classes of roads at various road stretches has been summarised, and all the conclusions are considered for an upcoming virgin project titled “Determining safety aspects of differential speed limit on Indian roads”.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:47:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581530</guid>
    </item>
    <item>
      <title>Comprehensive Analysis of Road Accidents and Surrogate Measures to Enhance Road Safety</title>
      <link>https://trid.trb.org/View/2581523</link>
      <description><![CDATA[Worldwide, more than 50 million casualties occur in road crashes each year, in which 80% of road crashes are in developing countries. The country ranks one in the number of road accident deaths and an increase of about 47% in road crashes is expected in the next 20 years. Kerala is one of the top five accident-prone states in the country at the present time. Road safety becomes more and more important every year as the annual growth rate of traffic is more than 10% in Kerala. Road crashes tend to result in personal injury, loss of life, or damage to property. The challenging factors in the traffic conditions existing in our road networks are the mixed traffic conditions and vulnerable road users. The state needs a comprehensive road safety plan, which in turn requires enormous data with respect to the accident and its severity over a period of time in order to reduce the accident scenario. Contributing factors of road accidents need to be documented in order to decide the most appropriate solutions. The objective of this study is to analyze the accident data over a period of time, identify the causative factors, and suggest appropriate solutions. A 60 km stretch of National Highway NH 66 passing through the Kollam district of the state was selected for this purpose. Data from the State Crime Records Bureau (SCRB), over a period of three years from 2017 to 2019 was collected and analyzed. Year-wise accident statistics, trends and causative factors were identified. Certain locations were identified to be blackspots as per MoRTH standards were later taken up for detailed study and appropriate solutions were suggested.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:47:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581523</guid>
    </item>
    <item>
      <title>A hybrid SEM–ANN framework for predicting crash involvement and prioritizing safety strategies among LCV drivers in India</title>
      <link>https://trid.trb.org/View/2664089</link>
      <description><![CDATA[Road traffic crashes (RTCs) are major public concerns causing societal and economic burden worldwide. According to empirical data, most crashes are caused by drivers engaging in risky driving behaviour. However, in developing nations like India, limited research exists on crash involvement of professional drivers, including light commercial vehicle drivers (LCVs). Therefore, the aim of the present research to study the determinants of crash involvement and develop a prediction model for LCV drivers. Further, the study also recommends suitable strategies for improving the safety of LCV drivers. To accomplish this, face-to-face interviews with LCV drivers were carried out using a questionnaire form in Nellore city, Andhra Pradesh, India. The questionnaire consisted of items related to personality traits, work safety climate, driver safety attitude, risky driving behaviour and crash involvement of LCV drivers. Data collection yielded 605 valid samples for further study. The exploratory factor analysis obtained a four-factor structure (sensation seeking, normlessness, anxiety, and anger) for the personality traits items, which was subsequently validated by confirmatory factor analysis. To analyze the complex relationships among several latent constructs and develop a prediction model for crash involvement, a hybrid Structural Equation Modeling and Artificial Neural Network (SEM-ANN) methods were adopted in this study. The ranking results from the ANN model demonstrated that risky driving behavior as the utmost influential predictor of crash involvement. Further, the analysis of safety strategies using the ANN model identified GPS-ANPR integrated enforcement of traffic rules as the most effective interventions, with normalized importance scores of 100%, followed by regular health checkup of drivers (94.19%), and installing of telematic systems (82.90%). The outcomes indicate the importance of enforcement-driven and technology-based strategies for improving LCV drivers’ safety. The outcomes of this study offer valuable insights for Indian trucking companies and road safety authorities to enhance the safety of LCV drivers.]]></description>
      <pubDate>Wed, 29 Apr 2026 16:34:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664089</guid>
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