<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Multivariate Negative Binomial Weighted Lindley Generalized Linear Model: Methodological Innovations and Applications in Traffic Safety</title>
      <link>https://trid.trb.org/View/2712069</link>
      <description><![CDATA[The concept of a multivariate distribution is essential in statistics, allowing the simultaneous analysis of related variables. In transportation safety, such models are effective for studying crash data across multiple categories, improving predictions and evaluations of safety measures. This paper extends the negative binomial weighted Lindley (NB-WLindley) distribution, known for handling highly dispersed or sparse data, into a multivariate framework. The proposed multivariate NB-WLindley generalized linear model treats each crash category as a random variable dependent on other categories within a joint framework while preserving the marginal distributional properties. It is hierarchically defined as a mixture of NB and multivariate weighted Lindley distributions and incorporates a dependence structure to explain correlations among categories. Applications to two crash datasets show that the multivariate NB-WLindley model can simultaneously capture different crash types and severities, identifying dependencies that univariate models cannot. The study also develops a random parameters version of the model to address unobserved heterogeneity, which consistently outperforms the fixed-parameter version and yields stronger predictive performance. Overall, this work demonstrates that the proposed multivariate model offers a more flexible and accurate approach to crash analysis, enhancing the ability to capture variability and interdependence across crash categories. It provides a practical tool for improving safety assessments and supporting data-driven decision-making in transportation safety research.]]></description>
      <pubDate>Wed, 10 Jun 2026 09:06:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712069</guid>
    </item>
    <item>
      <title>Soil Amendment Guidance for Infiltration and Stormwater Treatment</title>
      <link>https://trid.trb.org/View/2696129</link>
      <description><![CDATA[Pollutants from roadway runoff are the leading cause of surface water impairments. Thus, treatment of road runoff by building roadside stormwater best management practices (BMPs) could prevent pollution and turn the road infrastructure into a sustainable water solution. However, limited infiltration in compacted roadside soil poses a significant challenge to designing roadside BMPs. To overcome this challenge, roadside or curbside soil where compaction is required could be mixed with amendments to alleviate the negative impact of compaction and increase infiltration and stormwater treatment. Compaction could decrease the amendment’s particle size if it crumbles under pressure. Furthermore, the amendment amount could vary based on soil hydraulic properties. This study aims to provide selection guidance for amendments and their quantity to achieve stormwater treatment goals in curbside soil where compaction is required for road design. To create a study that is representative of all California soil types, soils were collected within Caltrans Right of Way (ROW) from 8 sites, with two soil sites from each of the four hydrologic soil groups (HSG). The selected physical, geotechnical, and chemical properties of all soils were measured to verify their HSG type. To improve their infiltration capacity, four types of bulking agents were tested: coarse sand, vermiculite, perlite, and expanding shale clay silt aggregate (ESCS).]]></description>
      <pubDate>Tue, 05 May 2026 10:19:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696129</guid>
    </item>
    <item>
      <title>Evaluation of Alternative Intersection and Interchange Options for Nebraska </title>
      <link>https://trid.trb.org/View/2689408</link>
      <description><![CDATA[The Nebraska Department of Transportation (NDOT) has implemented and developed guidance for a limited set of unconventional designs (e.g., certain RCUTs and diverging diamond interchanges) and has accumulated experience through selected projects and research studies. NDOT lacks robust, Nebraska-specific tools to: (1) screen and select candidate unconventional facilities; (2) quantify trade-offs in operations, safety, and cost; and (3) develop practical guidelines that can be directly used by designers, planners, and district staff. This gap creates uncertainty when considering unconventional options and may limit NDOT’s ability to fully leverage designs that could offer meaningful safety and mobility benefits. The proposed research will provide NDOT with a structured and evidence-based approach for identifying the most suitable unconventional intersection and interchange options for Nebraska. By evaluating both the designs already implemented within the state and additional alternatives that may be considered soon, the study will broaden NDOT’s understanding of how various unconventional treatments perform under different traffic, geometric, and environmental conditions in Nebraska.]]></description>
      <pubDate>Tue, 02 Jun 2026 12:26:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689408</guid>
    </item>
    <item>
      <title>A pre/post evaluation of fatigue, stress and vigilance amongst commercially licensed truck drivers performing a prolonged driving task</title>
      <link>https://trid.trb.org/View/2661767</link>
      <description><![CDATA[The main purpose of this research study was to evaluate changes in fatigue, stress and vigilance amongst commercially licensed truck drivers involved in a prolonged driving task. The secondary purpose was to determine whether a new ergonomic seat could help reduce both physical and cognitive fatigue during a prolonged driving task. Two different truck seats were evaluated: an industrial standard seat and a new truck seat prototype. Twenty male truck drivers were recruited to attend two testing sessions, on two separate days, with each session randomized for seat design. During each session, participants performed two 10-min simulated driving tasks. Between simulated sessions, participants drove a long-haul truck for 90 min. Fatigue and stress were quantified using a series of questionnaires whereas vigilance was measured using a standardized computer test.  Seat interactions had a significant effect on fatigue patterns. The new ergonomic seat design holds potential in improving road safety and vehicle accidents due to fatigue-related accidents.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661767</guid>
    </item>
    <item>
      <title>Ergonomic evaluation of a new truck seat design: A field study</title>
      <link>https://trid.trb.org/View/2661761</link>
      <description><![CDATA[A postural evaluation of commercial licensed truck drivers was conducted to determine the ergonomic benefits of a truck seat prototype in comparison with an industry standard seat. Twenty commercially licensed truck drivers were recruited to perform a 90-min driving task. Postures were assessed using accelerometers and a backrest and seat pan pressure mapping system. Subjective discomfort measurements were monitored using two questionnaires: ratings of perceived discomfort (RPD) and the automotive seating discomfort questionnaire (ASDQ). Participants reported significantly higher discomfort scores when sitting in the industry standard seat. Participants sat with more lumbar lordosis and assumed a more extended thoracic posture when seated in the prototype. Pairing the gluteal backrest panel with the adjustable seat pan also helped reduce the average sitting pressure on both the seat pan and the backrest. The prototype provided several postural benefits for commercially certified truck drivers, as it did for a young and healthy population.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661761</guid>
    </item>
    <item>
      <title>Comparative analysis of risk factors and scenarios for different types of ship contact accidents using a data-driven Bayesian network model</title>
      <link>https://trid.trb.org/View/2644084</link>
      <description><![CDATA[This paper develops a data-driven Bayesian network model to investigate the individual and combined impact of risk factors on ship contact accidents involving terminals, bridges, reefs, and other objects. Based on a dataset of 234 ship contact accidents from China, the United States, Australia, and the United Kingdom, the model's structure and parameters are derived from the Tree-Augmented Naive Bayes and the Expectation-Maximisation algorithms, respectively. Subsequently, the developed model is assessed through a comparison between learned parameters and statistical data, axiomatic validation, k-fold cross-validation, and two subsample tests. A comparative analysis of different types of ship contact accidents is conducted by identifying high-frequency risk factors through diagnostic reasoning, high-sensitivity risk factors through sensitivity analysis, and high-risk scenarios using multi-scenario simulation. The research results indicate that (1) human factors, such as deficient safety awareness and inadequate technical experience, and insufficient risk perception and contact hazard assessment, are identified as high-frequency risk factors; (2) spatiotemporal factors, such as areas with contact accidents, and ship factors, such as operation, type, speed, and width, are identified as high-sensitivity risk factors; (3) the combined effects of key risk factors are investigated through the most probable explanation analysis and multi-factor joint scenario simulation. Moreover, the comparative analysis reveals differences in the high-frequency risk factors, high-sensitivity risk factors, and high-risk scenarios for different types of ship contact accidents. These findings have crucial implications for formulating both universal and tailored safety strategies for ship contact accidents.]]></description>
      <pubDate>Fri, 13 Mar 2026 08:46:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2644084</guid>
    </item>
    <item>
      <title>Understanding Driving Behaviors and Traffic Crashes among University Commuter Drivers</title>
      <link>https://trid.trb.org/View/2562246</link>
      <description><![CDATA[The fast expansion and urbanization of the Najran region of Saudi Arabia have made road safety a serious concern. The main goal is to provide a thorough analysis of the dynamics of traffic safety in the Najran area, with an emphasis on understanding the driving behaviors among university commuter drivers. A survey was conducted within the campus of Najran University to gather responses from students, faculty, and staff members, enabling subsequent analysis of the responses. Statistical techniques were employed to study the cumulative number of crashes, the relative distribution of crash types, and the total number of affected individuals, and to find risk factors. The survey gathered over 600 responses; however, only 199 responses involving vehicle crashes were included in the analysis. During the study period, survey analysis revealed a cumulative toll of 199 vehicle accidents, including 38 injury crashes and 7 fatalities. Based on the relative proportions of crash severity, 19% resulted in injuries, 4% in fatalities, and 77% were crashes causing property damage only. The statistical analysis results revealed that 5 factors out of 20 were significant in increasing the likelihood of vehicle crashes. Drivers involved in vehicle crashes were significantly affected by the time of day, driver factors, type of crash, presence of passengers, and seat belt usage. This study undertakes a comprehensive analysis of road safety dynamics in the Najran region, providing foundational insights that policymakers, practitioners, and researchers can use to make evidence-based decisions.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562246</guid>
    </item>
    <item>
      <title>Understanding the Causes of Autonomous Vehicle Crashes in California</title>
      <link>https://trid.trb.org/View/2562194</link>
      <description><![CDATA[This study investigates crash patterns involving autonomous vehicles (AVs) in California, using 94 publicly available collision reports from 2024. The analysis explores crash types, timing, environmental conditions, and human-AV interactions. Results reveal that rear-end collisions (35%) and side-swipe crashes (32%) are the most common, often caused by human drivers’ inability to adapt to AV behavior. Temporal trends indicate that most crashes occur during daylight (60%) and clear weather (58%), with peaks from noon to mid-afternoon (26%) and on Thursdays and Fridays (31%). Despite frequent low-severity crashes with minor vehicle damage and less than 1% injury rate, these incidents could negatively influence public perceptions of AV safety. Challenges persist in AV performance under adverse conditions, interactions with vulnerable road users, and mixed traffic environments. The findings highlight the need for technological advancements, public education, and regulatory oversight to address these challenges and support the safe integration of AVs into transportation systems.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562194</guid>
    </item>
    <item>
      <title>A new data-driven model for vehicle and pedestrian safety: statistical approach based on spatial decision-making</title>
      <link>https://trid.trb.org/View/2622028</link>
      <description><![CDATA[Minimizing the losses that occur after traffic accidents is a primary duty for all humanity. To do so, it is necessary to examine and analyse the potential risk factors that affect the severity of traffic accidents. In this article, a new spatial decision-making-based statistical solution methodology is proposed to determine the accident risk factors that occur in three different accident types using 5-year (2015–2019) accident data. (i) 22 independent variables and 157 sub-variables were determined for the traffic accident categories where vehicle-vehicle, vehicle-pedestrian and vehicle-other collision types occurred, (ii) the fuzzy simple weight calculation method was preferred to determine the effects of risk factors on accident categories, (iii) spatial analyses of risk factors were provided via geographical information system and combined with the obtained effect values, (iv) the current effect of risk factors on accident categories was tested with the multinomial logistic regression model. The multinomial logistic regression model results revealed a strong model fit (McFadden 𝘙² = 0.749) and identified the variables that significantly increase or decrease the probability of each crash type compared to the reference category. For instance, while the geo-intersection had the highest effect for vehicle-vehicle crashes, the pedestrian defect had the highest impact for vehicle-pedestrian crashes. Spatial analysis results also showed that accident severity tends to be higher in the western, southern, and central regions of Türkiye. The proposed methodology offers a comprehensive framework that supports evidence-based policy development for improving traffic safety. The resulting findings serve as a guide for local administrators, policy makers, and traffic safety experts with regard to vehicle and pedestrian safety.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622028</guid>
    </item>
    <item>
      <title>Accident severity prediction on arterial roads via multilayer perceptron neural network</title>
      <link>https://trid.trb.org/View/2622020</link>
      <description><![CDATA[Traffic accidents continue to be a major cause of death in urban areas. While recent research has demonstrated the utility of predictive modelling in rural, express and highway environments, a gap remains in understanding the factors that influence accidents in urban areas, particularly on arterial roads. This study developed multilayer perceptron (MLP), random forest (RF) and multinomial logistic regression (MLR) models to predict accident severity on urban arterial roads in Amman, Jordan’s capital. The MLP demonstrates clear superiority over RF and MLR, achieving 97.3% training accuracy and 96.55% testing accuracy. Additionally, a Sobol Global Sensitivity Analysis (GSA) for the MLP model identified critical interactions between variables, especially between collision types and weather conditions. This study provides an in-depth understanding of the key factors influencing accident severity, which can be used to develop new safety regulations and countermeasures to prevent crashes.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622020</guid>
    </item>
    <item>
      <title>Impact of weather conditions and road type on traffic safety</title>
      <link>https://trid.trb.org/View/2633903</link>
      <description><![CDATA[This study investigates the combined impact of weather conditions and road type on traffic safety in Poland. Using police accident data from 2018 to 2024, we analyzed correlations between nine weather categories and five road types. Results show that single carriageway, two-way roads account for 73 % of incidents and 85 % of fatalities. The highest absolute number of deaths occurs in clear and dry weather (64.6 % of all incidents). Fog on single carriageway, two-way roads and snowfall on motorways are associated with fatality rates up to three times higher than the national average. The findings highlight the need for targeted infrastructure upgrades, adaptive speed limits, and improved driver awareness in both adverse and seemingly safe conditions. Descriptive statistics, correlation analysis and ANOVA were applied to 45 road–weather combinations.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633903</guid>
    </item>
    <item>
      <title>Sex-Based Differences in Odds of Motor Vehicle Crash Injury Outcomes</title>
      <link>https://trid.trb.org/View/2652161</link>
      <description><![CDATA[Several studies have documented the relative risk or odds of injury and fatality for females versus males in motor  vehicle crashes, but none have combined the National Automotive Sampling System–Crashworthiness Data  System (NASS-CDS) and the Crash Investigation Sampling System (CISS). The study aimed to document the  odds of various injury outcomes for females versus males while considering a range of crash types, pre-crash and  crash variables, and occupant demographics. Multivariable logistic regression was used for this purpose. The  Approximate Bayesian Bootstrap hot-deck imputation method was applied as part of efforts to create  multivariable logistic regression models for 25 different injury outcomes associated with occupants 13 years and  older. These models were applied to passenger vehicle crashes published in NASS-CDS (2000 to 2015) and CISS  (2017 to 2022). Twenty-four predictor variables (including sex and 23 other occupant, crash, and vehicle covariates) were used across the models. Six crash-type models were produced for each injury outcome; one for  each of four different planar crashes (frontal, near-side, far-side, rear), one that included the planar crashes  combined, and one for primary rollover crashes. Different than other recent studies, a broader range of crash  types, occupant restraint conditions, and seating positions were considered. The results suggest that females  tended to have higher injury odds than males, but this varies by injury outcome and the associated crash type.]]></description>
      <pubDate>Tue, 27 Jan 2026 08:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652161</guid>
    </item>
    <item>
      <title>Investigating the Role of Human Factors, Vehicle Safety Features, and Types of Crashes on Injury Severity in Kansas</title>
      <link>https://trid.trb.org/View/2652672</link>
      <description><![CDATA[The Safe System Approach emphasizes designing countermeasures with an in-depth understanding of the human factors associated with traffic crashes. At the same time, it is important to investigate the role of better safety metrics, including the Insurance Institute for Highway Safety (IIHS) crash-worthiness and the National Highway Traffic Safety Administration (NHTSA safety ratings, in preventing serious injury crashes. Since crash injury severity is affected by multiple factors, it is important to account for vehicle crash worthiness (as defined by the IIHS), human factors, and the environment (network and the detailed sequence of most-harmful events as well as the types of crashes: rear-end, side-swipe, head-on) within an integrated modeling framework. A comprehensive crash severity model integrated with ArcGIS StoryMap will enable the Bureau of Transportation Safety of the Kansas Department of Transportation to promote effective safety countermeasures and create behavioral and instructional safety campaigns for drivers of various vehicle models. The research aims to develop a crash severity model accounting for vehicle attributes (make, model, year) and crash attributes (collision types, sequence of harmful events) with the ten years of crash data from Kansas (2012 – 2023) using the state crash database (the data can be extended to the most recent year based on availability). The goals of the project are as follows: 1. Perform statistical analyses of the relationship between accident type and vehicle year, model, and manufacturer using ten-year crash data; 2. Investigate possible correlations between vehicle attributes (make, model, year) and crash severity (sensitivity and cluster analyses); 3. Compare the percentage of vehicle types registered in Kansas to the percentage of crashes by vehicle types (representation ratio); 4. Compare findings of the estimated injury severity model with crash-worthiness scores by the IIHS. Examine the performance of certain safety features that may have been available within the vehicle types. The option to leverage NHTSA ratings data for comparison purposes will also be explored.]]></description>
      <pubDate>Tue, 13 Jan 2026 16:16:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652672</guid>
    </item>
    <item>
      <title>Characteristics analysis of autonomous vehicle pre-crash scenarios</title>
      <link>https://trid.trb.org/View/2618082</link>
      <description><![CDATA[To date, hundreds of crashes have occurred in open-road testing of autonomous vehicles (AVs), highlighting the need for improving AV reliability and safety. However, current studies predominantly analyze crash data based on oversimplified classification schemes that lack clear scenario definitions. Consequently, they impede an in-depth investigation of crash characteristics. Pre-crash scenario typology classifies crashes based on vehicle dynamics and kinematics features. Building on this, characteristics analysis can identify similar features under comparable crashes, offering a more effective reflection of general crash patterns and providing more targeted recommendations for enhancing AV performance. In this paper, the authors initially collected the latest 774 California AV crash reports, then selected 384 autonomous mode crashes, and used the newly revised pre-crash scenario typology to identify AV pre-crash scenarios. To improve the efficiency of scenario identification and adaptability to future updates in scenario typology, the authors proposed a set of mapping rules to extract pre-crash scenarios automatically. The authors successfully identified 27 types of AV pre-crash scenarios with an accuracy of 98.1%. Through detailed analysis, the authors obtained two key groups of AV pre-crash scenarios: rear-end scenarios and intersection scenarios. Based on the abundance of crash data, the authors adopted different analysis methods to analyze the features of key scenarios. Association analysis of rear-end scenarios showed that the significant environmental influencing factors were traffic control type, location type, light, etc. For intersection scenarios prone to severe crashes with detailed descriptions, the authors employed causal analysis to obtain the significant causal factors: habitual violations and temporary obstruction of view. The extracted scenarios in this paper and their features can assist in constructing the AV simulation test with precise environmental parameters and realistic interactions with other traffic parties. The resulting optimization recommendations can inform regulators and reveal control-algorithm weaknesses across diverse real-world conditions, thereby enhancing the AV safety.]]></description>
      <pubDate>Mon, 24 Nov 2025 10:19:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2618082</guid>
    </item>
    <item>
      <title>Comparison of Waymo Rider-Only crash rates by crash type to human benchmarks at 56.7 million miles</title>
      <link>https://trid.trb.org/View/2617057</link>
      <description><![CDATA[SAE Level 4 Automated Driving Systems (ADSs) are deployed on public roads, including Waymo’s Rider-Only (RO) ride-hailing service (without a driver behind the steering wheel). The objective of this study was to perform a retrospective safety assessment of Waymo’s RO crash rate compared to human benchmarks, including disaggregated by crash type. Eleven crash type groups were identified from commonly relied upon crash typologies that are derived from human crash databases. Human benchmarks were developed from state vehicle miles traveled (VMT) and police-reported crash data. Benchmarks were aligned to the same vehicle types, road types, and locations as where the Waymo Driver operated. Waymo crashes were extracted from the NHTSA Standing General Order (SGO). RO mileage was provided by the company via a public website. Any-injury-reported, Airbag Deployment, and Suspected Serious Injury + crash outcomes were examined because they represented previously established, safety-relevant benchmarks where statistical testing could be performed at the current mileage. Data were examined over 56.7 million RO miles through the end of January 2025; resulting in a statistically significant lower crashed vehicle rate for all crashes compared to the benchmarks in Any-Injury-Reported and Airbag Deployment, and Suspected Serious Injury + crashes. Of the crash types, V2V Intersection crash events represented the largest total crash reduction, with a 96% reduction in Any-injury-reported (87–99% confidence interval) and a 91% reduction in Airbag Deployment (76–98% confidence interval) events. Cyclist, Motorcycle, Pedestrian, Secondary Crash, and Single Vehicle crashes were also statistically reduced for the Any-Injury-Reported outcome. There was no statistically significant disbenefit found in any of the 11 crash type groups. This study represents the first retrospective safety assessment of an RO ADS that made statistical conclusions about more serious crash outcomes (Airbag Deployment and Suspected Serious Injury+) and analyzed crash rates on a crash type basis. The crash type breakdown applied in the current analysis provides unique insight into the direction and magnitude of safety impact being achieved by a currently deployed ADS system. This work should be considered by stakeholders, regulators, and other ADS companies aiming to objectively evaluate the safety impact of ADS technology.]]></description>
      <pubDate>Wed, 19 Nov 2025 17:09:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617057</guid>
    </item>
  </channel>
</rss>