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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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    <item>
      <title>Headlight glare in police-reported crash data: Prevalence, contributing factors, and potential effects</title>
      <link>https://trid.trb.org/View/2720727</link>
      <description><![CDATA[This study investigates the prevalence and contributing factors of headlight glare in police-reported crash data across 11 U.S. states from 2015 to 2024. The purpose was to evaluate the degree to which glare is reported as a contributing factor in nighttime crashes and to identify conditions and populations most commonly associated with glare. Rates of reported glare in all crashes were calculated across sun-altitude categories. Glare in nighttime crashes was examined using crash data combined across multiple states, matched-pair comparisons within states, and narrative reviews of officer reports. Glare was reported in only 0.1%–0.2% of nighttime crashes, with little variation over time despite the widespread improvement in headlight visibility that took place during the study years. Overall, most glare-related crashes occurred during daylight hours when the sun was close to the horizon. Nighttime glare crashes were disproportionately associated with older drivers, older vehicles, and undivided low-speed roads. Narrative analysis revealed that lane departures were the most common driver response to headlight glare, accounting for over half of cases. While already at a low reported level, the results suggest the effect of headlight glare on crash risk could be further reduced by targeted countermeasures such as adaptive lighting systems, improved lane markings, and more advanced lane departure technologies. Additionally, future research should explore whether increased visibility from better illumination reduces drivers’ susceptibility to glare from other vehicles. Previous research has demonstrated that improved headlight visibility reduces the risk of single-vehicle nighttime crashes. This study found no indication that such improvements have led to an increase in glare-related crashes.]]></description>
      <pubDate>Tue, 21 Jul 2026 09:49:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720727</guid>
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    <item>
      <title>Design and implementation of an ultrasonic sensor-based automated braking system for manual transmission vehicles: A case study of the Toyota Land Cruiser HZJ79</title>
      <link>https://trid.trb.org/View/2701321</link>
      <description><![CDATA[Road traffic accidents remain a major public safety and economic concern, particularly in low- and middle-income countries where aging vehicle fleets and limited access to advanced driver-assistance systems exacerbate the risk. This study presents a low-cost, retrofittable automated braking system designed to enhance safety in older manual-transmission vehicles without relying on ABS or ESC. The system combines ultrasonic sensors, an Arduino-based control unit, and a solenoid actuator to automatically apply brakes, effectively eliminating human reaction delays and improving collision mitigation under dusty or low-visibility conditions. The proposed approach targets vehicles such as the Toyota Land Cruiser HZJ79, commonly used in Ethiopia’s military and commercial sectors. Analytical modeling, CAD-based design (SolidWorks), and hardware simulations (Proteus) were employed to evaluate actuator force, braking time, and stopping distance. Results indicate consistent reductions in stopping distance, achieving up to 85% improvement at low speeds and an average reduction of 14% across all tested conditions. Comparative analysis with existing low-cost automated braking systems demonstrates superior performance under realistic constraints. The primary contribution of this work is twofold: it extends the service life and safety of existing vehicles in resource-limited settings, and it provides a scalable, practical solution that reduces accident-related social and economic burdens without requiring fleet replacement. A cost-benefit assessment further confirms the system’s feasibility and potential impact. Overall, this study demonstrates that intelligent braking technologies can be effectively integrated into older vehicles, bridging a critical gap in road safety and offering a sustainable approach to reducing traffic-related injuries and fatalities in low- and middle-income countries.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701321</guid>
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    <item>
      <title>Spatiotemporal diagnosis towards mismatch between traffic crash risks and safety governance in rapidly urbanizing cities</title>
      <link>https://trid.trb.org/View/2697017</link>
      <description><![CDATA[The velocity of urban functional expansion often outpaces the evolution of safety governance, creating a critical spatiotemporal mismatch that undermines traffic safety. However, existing studies typically rely on static snapshots, failing to capture the dynamic migration of these traffic accident risks or decode the evolving underlying mechanisms behind the governance deficit. To investigate the spatiotemporal evolution of this mismatch and analyze its underlying behavioral and structural causes, this research proposed a novel diagnostic framework integrating Geographically Weighted Regression with Random Forest interpretation. Using a decade of crash data (2013–2022) from a rapidly urbanizing Chinese city, the authors reconstructed the spatiotemporal trajectory of high-risk mismatch zones, where observed crash frequencies systematically exceed the levels predicted by static built environment factors. The empirical results indicate that this safety-governance mismatch manifests through three critical dimensions: spatiotemporal migration, mechanism heterogeneity, and spatial variation in crash patterns. First, spatially, the center of gravity of safety risks has shifted systematically from the mature city center to the expanding suburban fringe, empirically verifying the lagging governance hypothesis. Second, temporally, the dominant risk factors have fundamentally shifted. Behavioral factors and conflict patterns consistently dominate the risk hierarchy over the decade, while infrastructure factors remain marginal. This confirms that the governance deficit is a management deficit rather than a physical infrastructure gap. Finally, through mechanism deciphering, a differential diagnosis isolates the unique crash patterns of these high-risk zones. Unlike the congestion-induced passive errors in the core, these fringe zones are characterized by Improper Operation and severe Vehicle-Non-Motor conflicts, reflecting a critical gap in managing mixed traffic flows. The successful application of this diagnostic framework demonstrates its efficacy in identifying the blind spots of traditional static management. These findings challenge the traditional “build-first, manage-later” paradigm. Instead, a dynamic, precision-based governance framework is advocated to synchronize safety management with the spatiotemporal evolution of urban risks.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697017</guid>
    </item>
    <item>
      <title>Exploring the endogeneity between the autonomous vehicle takeover and crash severity: comparative analysis of structural equation modeling and generalized linear logit model</title>
      <link>https://trid.trb.org/View/2663655</link>
      <description><![CDATA[ObjectivesUnderstanding the factors influencing crash severity of autonomous vehicles is important for increasing road safety. This study focuses on a multi-source accident dataset of vehicles equipped with autonomous driving systems to explore the endogenous relationship between manual takeover of autonomous vehicles and the severity of crash, as well as the influencing factors.MethodsBy screening and summarizing data on autonomous vehicle accidents. We choose self-driving car takeover and crash severity as potential variables to build a structural equation model to explore the influences of crash severity through continuous variable updating and path improvement. We select autonomous vehicle takeover and crash severity as potential variables and designed a structural equation model to explore the factors affecting crash severity through continuous variable updating and path improvement. Meanwhile, we establish a generalized linear logit model to analyze the factors affecting manual takeover. Finally, the intrinsic link between crash severity and manual takeover is discussed through path analysis and comparison of model results.ResultsCloudy and rainy weather, left rear of vehicle contact area, and daylight lighting significantly impact manual takeover and crash severity. Specifically, wet road surface, rainy weather, and daylight have relatively more significant effects on takeover in the structural equation model. And takeover, roadway type including non-freeway and intersection can significantly impact crash severity. Additionally, the study demonstrates the endogeneity between crash severity and takeover at the time of autonomous vehicle crash.ConclusionsThis study analyzes the potential relationships and influencing factors between takeover events of autonomous vehicles and crash severity. It is found that the frequency of takeover events significantly increases when driving in rainy weather and at night. It is suggested that a real-time monitoring module for adverse weather or lighting conditions should be added to the autonomous driving system to provide early warnings and reduce the occurrence of takeover events, thereby enhancing the safety and reliability of autonomous vehicles.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663655</guid>
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    <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>Multi-Scale Temporal Analysis of Connected Vehicle Data for Safety Analysis</title>
      <link>https://trid.trb.org/View/2562187</link>
      <description><![CDATA[Highway safety analysis and crash modeling present significant challenges due to the complex variability of roadway, vehicle, and driver characteristics. Relying solely on traditional crash data can often overlook essential risk factors, including near-misses and minor collisions that fall below typical reporting thresholds. To address these gaps, this study leverages Connected Vehicle (CV) data collected in Oklahoma to assess traffic parameters, such as hard braking events, vehicle speeds, and traffic volume, in order to identify potential safety hazardous locations. The study analyzed 3 months of CV data from January, May, and August 2021, aggregated at both hourly and daily intervals to capture seasonal variations and their effects on traffic behavior. Additionally, data from May 2020 were also investigated to assess the impacts of the COVID-19 pandemic on traffic streams. Comparison analysis found distinct temporal patterns of different hours (rush versus non-rush), days of the week (weekdays versus weekends), and seasons. By correlating these patterns with crash data from the Oklahoma highway collision database, spatial and statistical analyses identified associations between CV data and crash frequency. The research proposed the potential surrogate safety measures for identifying crash hotspots and underscored the critical factors influencing highway safety.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562187</guid>
    </item>
    <item>
      <title>Investigating the Influencing Factors of Crash Frequency Considering Spatial Heterogeneity</title>
      <link>https://trid.trb.org/View/2613269</link>
      <description><![CDATA[To clarify the influencing factors of crash frequency, this paper collects the traffic crashes and some relevant spatial data in a certain area and constructs a Geographically Weighted Regression (GWR) model and a Multiscale Geographically and Temporally Weighted Regression (MGWR) model based on that. Through the models, the degree of impact of various spatial influencing factors and the spatial heterogeneity are examined. The results show that the MGWR model is superior to the GWR model in terms of fitting effect and spatial scale; road density and the number of hospitals have positive promoting effects, meaning that the areas with higher road density or more hospitals tend to possess more traffic crashes, while grid GDP exhibits an inverse inhibitory effect, meaning that the economically prosperous areas possess fewer traffic crashes; besides that, the proportion of crashes during the morning rush hours and the proportion of crashes with motorcycle involvements have significant impacts and spatial heterogeneity on traffic crashes.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613269</guid>
    </item>
    <item>
      <title>Infrastructure associations of crash frequency and types in Sweden’s national road network with temporal instability during COVID-19</title>
      <link>https://trid.trb.org/View/2633608</link>
      <description><![CDATA[Despite advancements in road infrastructure and ongoing efforts to improve traffic safety, how infrastructure features relate to crash frequency and crash types, and how these associations vary across regions and over time, remains insufficiently understood. In this study, Sweden’s national-level road network is treated as an integrated system, and traffic, land-use, meteorological, and socio-demographic data are fused to analyze crash frequency and type distributions from 2018 to 2022. An integrated crash analysis framework is proposed, coupling a hierarchical hurdle model for crash frequency prediction (comprising a binary logit for crash occurrence and a truncated negative binomial for crash counts) with a hierarchical multinomial logit for crash-type classification. To demonstrate temporal instability of how external shocks (such as the COVID-19 pandemic) affected both crash-frequency and crash-type predictions, out-of-sample simulations are performed. Key findings include: (1) Crash frequency is strongly associated with road segment design, land use, and socio-demographic variables, while crash-type distributions are mostly influenced by roadway design and occurrence time; (2) Unobserved heterogeneities are found to significantly enhance model reliability and predictive performance; and (3) The COVID-19 pandemic is shown to notably alter crash frequencies but to have a comparatively modest effect on shifts in crash-type proportions. The results can provide a foundation for infrastructure design, risk-informed policy interventions, and dynamic maintenance strategies aimed at improving the safety of national-level road network in Sweden.]]></description>
      <pubDate>Fri, 09 Jan 2026 14:44:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633608</guid>
    </item>
    <item>
      <title>Assessment of driver fatigue as a contributory factor in articulated truck accidents in Nigeria</title>
      <link>https://trid.trb.org/View/2570869</link>
      <description><![CDATA[Driver fatigue is a significant factor contributing to road traffic accidents, especially among drivers of articulated trucks who often operate under intense time and operational pressure. This investigation evaluates the influence of driver fatigue on truck-related accidents within the Nigerian context, scrutinizing pivotal fatigue-associated elements such as duration of rest, travel duration, and their association with the frequency of accidents. A structured questionnaires was used to gather data from 250 truck drivers traversing major freight routes in Nigeria. Quantitative data were subjected to analysis through descriptive statistics and multiple regression techniques. The results indicate that 74 % of truck drivers obtain merely 1 to 3 h of rest during their journeys, with 65 % indicating travel durations that exceed 11 h for each trip. Statistical evaluations reveal a noteworthy correlation between fatigue-related factors and the frequency of accidents, with travel duration (B = 0.269, 𝘱 < 0.001) and rest duration (B = 0.210, 𝘱 = 0.088) identified as principal predictors. Drivers who partake in extended driving periods without sufficient rest demonstrate a 26.9 % heightened probability of being involved in accidents. The research underscores that erratic work schedules, postponed meal intervals, and trip-based remuneration frameworks intensify levels of fatigue. The policy implications underscore the pressing necessity for the rigorous enforcement of obligatory rest periods, the integration of fatigue-monitoring technologies, and the creation of designated resting areas for trucks. The implementation of these measures could alleviate fatigue-related accidents and enhance road safety within Nigeria's transportation sector. Subsequent research should investigate the long-term ramifications of fatigue mitigation strategies and analyze comparative frameworks from other regions to optimize policy efficacy.]]></description>
      <pubDate>Fri, 18 Jul 2025 15:45:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2570869</guid>
    </item>
    <item>
      <title>Short-term safety analysis and interdependencies of mixed-operation freeways with fully separated express lanes: A copula-based poisson lognormal lindley approach</title>
      <link>https://trid.trb.org/View/2540256</link>
      <description><![CDATA[The I-4 Ultimate Express Lanes (ELs) are part of a major improvement project on Interstate 4, introducing innovative freeway design that impacts traffic safety and operations. This research examines safety performance and the interdependencies between general-purpose lanes (GPLs) and (ELs). Utilizing crash data from February 2022 to February 2024, short-term crash frequency models are developed at the lane-level, leveraging microscopic traffic detector data and unique geometric design features to estimate annual average weekday crash frequencies and capture temporal safety variations. The study employs Poisson Lognormal Lindley (PLN-L) model to address excessive zeros in crash data, particularly prevalent in ELs, while copula-based framework analyzes the dependency between GPL and EL crash frequencies. Frank copula provided the best fit among five tested copula structures, revealing significant dependency, especially at access points. The analysis incorporates lane-level traffic characteristics, geometric data (e.g., standard and I-4 Ultimate specific segment types (interaction between ELs and GPLs at access points), their lengths, and ramp lengths), and time-period effects. Significant variables for GPL and EL segments include lane-level traffic exposure and other factors. Key findings indicate that the average lane-occupancy in the rightmost lane significantly impacts EL safety, with higher crash rates at merge segments. For GPLs, I-4 Ultimate segments and their shorter lengths (i.e., Long [5000ft >= (length) > 3000ft]) are associated with GPL-related crashes. These findings contribute to developing safer, more efficient managed lane systems and offer guidance for future freeway design improvements. The study gives policymakers and engineers insights into EL safety, design, and operation.]]></description>
      <pubDate>Wed, 28 May 2025 16:23:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2540256</guid>
    </item>
    <item>
      <title>Association between e-scooter temporal usage patterns with injuries resulting in admission to a level one trauma center</title>
      <link>https://trid.trb.org/View/2449405</link>
      <description><![CDATA[As e-scooters have become common modes of transportations in urban environments, riding e-scooters has become a common mechanism of injury. This study examines the relationship between when riders are using these devices (i.e. day of week, and time of the day) and injury incidence based on data from a large U.S. city. This study is a retrospective cohort study of patients in the trauma registry at a level one trauma center. Registry data were combined with a publicly available dataset of all e-scooter trips that occurred during the study period. Frequency of injuries and trips were analyzed using ANOVA. Poisson regressions were conducted to calculate incidence rate ratios associated with injury incidence by day of the week and time of day. A total of 194 injured e-scooter patients were admitted to the trauma center during the study period. Patients were injured most often on Fridays (21%) and most often presented between 18:00-23:59 (38%). E-Scooter riders in general, most often rode on Saturdays (20%) and between 12:00-17:59 (44%). There was no significant relationship between day of week and injury. Riders in the early morning (IRR = 16.7, p < .001 95% CI: 10.5, 26.6), afternoon (IRR = 2.0, p = .01 95% CI: 1.2, 3.4), and evening (IRR = 3.7, p < .001 95% CI: 2.3, 6.2) had significant increased injury incidence compared to morning riders. E-Scooter injury incidence varies by the time of day. The time of day in which a person rides an e-scooter can have a significant impact on the likelihood that the person will sustain an injury.]]></description>
      <pubDate>Fri, 15 Nov 2024 11:01:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2449405</guid>
    </item>
    <item>
      <title>Black Spot Cluster Analysis of Road Crash involving Public Utility Vehicles (PUV) along Commonwealth Avenue using Kernel Density Estimation</title>
      <link>https://trid.trb.org/View/2394718</link>
      <description><![CDATA[The Commonwealth Avenue in Quezon City is known as the “killer highway” in Metro Manila with an average of 17 fatal road crash per year from 2017-2021. The high occurrence of vehicular road crashes in Commonwealth Avenue has led to several research interests in identifying and analyzing road crash hot spots using the Geographical Information Systems (GIS). This study used data from the Metro Manila Accident Reporting and Analysis System (MMARAS) data of Metropolitan Manila Development Authority (MMDA) and employed kernel density estimation using free open-source software to identify public utility vehicle (PUV) road crash hotspots along Commonwealth Avenue. The study revealed five fatal crash locations along Commonwealth Avenue. High density road crashes occurred during the time period of 7:00 to 10 am and 5:00 to 8:00 pm. specifically on Fridays at the Regalado Avenue The study's results have significant implications that can be used to identify black spots, develop targeted interventions, and take measures to reduce the severity of PUV crashes.]]></description>
      <pubDate>Mon, 30 Sep 2024 17:21:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2394718</guid>
    </item>
    <item>
      <title>Analysis of Geographic, Temporal, and Socioeconomic Shifts in Pedestrian &amp; Bicyclist Traffic Injuries</title>
      <link>https://trid.trb.org/View/2401753</link>
      <description><![CDATA[Understanding where, when, and to whom crashes are occurring is essential to preventing future pedestrian and bicyclist injuries. This project will cover eight states but have additional focus on shifts in pedestrian and bicyclist injuries in Wisconsin. It will complement quantitative data analysis with practitioner interviews in Wisconsin to explore transportation infrastructure, policy, or land development changes that may have contributed to shifts in patterns of K&A pedestrian and bicyclist crashes. We will investigate the following questions: 1) How much did fatal, severe, and non-severe pedestrian and bicyclist injuries change over the last decade? 2) What geographic shifts occurred in pedestrian and bicyclist crashes at each injury severity level over the last decade? 3) What time-of-day shifts occurred in pedestrian and bicyclist crashes at each injury severity level over the last decade? 4) Why did these geographic and temporal shifts in different injury levels occur? To explore this key question, we will attempt to connect shifts in crash locations and times with shifts in the socioeconomic characteristics of pedestrians and bicyclists involved in crashes. We will examine the characteristics of individual pedestrians and bicyclists involved in crashes as well as characteristics of the area surrounding crash locations (e.g., analyze the types of jobs and socioeconomic characteristics in the census tracts near specific crash locations). We will also compare urban versus rural areas.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2401753</guid>
    </item>
    <item>
      <title>Analyzing the time-varying patterns of contributing factors in work zone-related crashes</title>
      <link>https://trid.trb.org/View/2386996</link>
      <description><![CDATA[Work zones are crucial for maintaining and enhancing road infrastructure, but they also pose a significant risk to traffic safety. Between 2016 and 2020, work zone-related crashes in the United States increased by 13%, highlighting the pressing need for effective safety measures. This study examines the factors that influence work zone crashes, including traffic control devices, geometric configurations, traffic operations, and human factors. The study also investigates how these factors vary by work zone type, day of the week, and time of day. To explore these temporal and spatial impacts, the study utilized five years of fatal crash data from the Fatality Analysis Reporting System (FARS) and applied association rules mining. The findings demonstrate that rear-end crashes and collisions with other vehicles are the primary contributing factors. Although some common patterns emerged in the association rules, the study revealed temporal instability, highlighting the importance of developing work zone-specific safety countermeasures. These findings will inform the development of targeted safety interventions and ultimately reduce the risk of work zone crashes.]]></description>
      <pubDate>Mon, 24 Jun 2024 09:24:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2386996</guid>
    </item>
    <item>
      <title>How do drunk-driving events escalate into drunk-driving crashes? An empirical analysis of Beijing from a spatiotemporal perspective</title>
      <link>https://trid.trb.org/View/2384534</link>
      <description><![CDATA[Drunk-driving events often escalate into drunk-driving crashes, however, the contributing factors of this progression remain elusive. To mitigate the likelihood of crashes stemming from drunk-driving events, this paper introduces the notion of ‘the severity of drunk-driving event’ and examines the complex relationship between the severity and its contributing factors, considering spatiotemporal heterogeneity. The study utilizes a Geographically and Temporally Weighted Binary Logistic Regression (GTWBLR) model to conduct spatiotemporal analysis based on police-reported drunk-driving events in Beijing, China. The results show that most factors passed the non-stationary test, indicating their effects on the severity of drunk-driving event vary significantly across different spatial and temporal domains. Notably, during non-workday, drunk-driving events in northeast of Beijing are more likely to escalate into crashes. Furthermore, severe weather during winter in the northwest of Beijing is associated with high risk of drunk-driving crashes. Based on these insights, the authorities can strengthen drunk-driving checks in the northeast region of Beijing, particularly during non-workdays. And it is crucial to promptly clear accumulated snow on the roads during severe winter weather to improve road safety. These insights and recommendations are highly valuable for reducing the risk of drunk-driving crashes.]]></description>
      <pubDate>Mon, 17 Jun 2024 09:40:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2384534</guid>
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