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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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    <item>
      <title>Driving visual information in highway tunnel entrances: A computational method based on optical flow and color quantification</title>
      <link>https://trid.trb.org/View/2680652</link>
      <description><![CDATA[The environmental landscape of highway tunnel entrance zones is closely related to driving performance. To investigate the impact mechanism of environmental information volume on drivers’ visual workload in tunnel entrance zones, this study proposes a novel computational method for quantifying visual information. The aim is to provide a theoretical basis for improving tunnel entrance environments and enhancing driving safety. Field experiments on highways collected environmental images, vehicle dynamics, and drivers’ speed and psychological data from eight tunnel entrances. Visual field images were divided into five regions based on attention range: upper portal, central portal, left/right roadside, and pavement. HSV values were extracted to describe color and texture features. A model combining optical flow, sight distance, lane width, and speed quantified visual information volume, including traffic signs, and analyzed its relationship with visual workload. The subjective questionnaire results were consistent with the objective computational findings, verifying the reliability of the proposed method. Tunnel entrances with complex landscapes and diverse traffic signs exhibited higher levels of visual information, with drivers’ gaze distributed across four areas: both sides of the road, the tunnel entrance center, and the roadway. In contrast, entrances with simpler landscapes and fewer signs had lower visual information levels, and drivers’ gaze was mainly concentrated on the roadway and the tunnel entrance center. The proposed visual information quantification method effectively evaluates the impact of tunnel entrance environmental characteristics on driving visual workload. Appropriately controlling the proportion of traffic sign information (15%–25.55%) helps balance visual workload and comfort, while excessive or insufficient information may lead to discomfort due to underload or overload. These findings provide theoretical guidance and practical recommendations for optimizing tunnel entrance landscape design, traffic sign arrangement, and traffic safety enhancement.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680652</guid>
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    <item>
      <title>Patterns of substance use among non-offender drivers: Associations with demographics, aberrant driving behaviors, and near-collisions</title>
      <link>https://trid.trb.org/View/2680651</link>
      <description><![CDATA[A substantial body of literature documents the association between alcohol and drug use with impaired driving, increased risky driving behavior, and traffic crashes. However, most research has focused on offender drivers, with limited attention given to non-offenders. Thus, the applicability of these findings to non-offender drivers remains unclear. This study aimed to: (a) examine alcohol consumption patterns, drug problems, and non-prescription medication use among non-offender drivers; (b) investigate the relationship between driver characteristics (age, sex, education, and driving experience) and substance use; and (c) explore the associations between substance use, near-collisions, and aberrant driving behaviors. A total of 360 non-offender drivers (52.5% male) aged 18–75 years (M = 33.52) from Spain participated in the study. Participants provided data on their sociodemographic characteristics, substance use (alcohol, drugs, and non-prescription medications), and self-reported driving behavior, including near-collisions and aberrant behaviors, as measured by the Driver Behavior Questionnaire (DBQ). Over 85% of participants reported consuming alcohol, with approximately 60% drinking regularly or occasionally (2–4 times a month). The majority (80.1%) had low-risk alcohol consumption as indicated by AUDIT scores. A small proportion (5%) reported drug problems, and most participants (84.4%) did not use non-prescription medications. Alcohol consumption patterns varied significantly by sex, age, and driving experience. Men, younger drivers, and less experienced drivers were more likely to engage in high-risk alcohol consumption. Notably, a high proportion of women reported moderate risk levels of alcohol consumption. No significant differences were found in drugs or non-prescription medication use. Multiple regression analysis revealed that alcohol use, but not drug or non-prescription medication use, predicted violations, aggressive violations, errors, and lapses. Findings indicate a high prevalence of alcohol consumption among non-offender drivers and a significant association between alcohol use and aberrant driving behaviors, confirming alcohol as a significant risk factor among non-offenders. While young and male drivers represent the primary risk group, attention should also be given to non-offender women due to their increased alcohol consumption and the associated risks for road safety. Gender-specific research and targeted strategies to reduce alcohol consumption and drink driving among women are discussed.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680651</guid>
    </item>
    <item>
      <title>Research on interactive design strategy of home intelligent cockpit in nonlinear driving scenarios</title>
      <link>https://trid.trb.org/View/2680650</link>
      <description><![CDATA[The increasing demand for family travel highlights the importance of intelligent cockpit interaction design in this context. This study aims to meet the diverse needs of family users in non-linear driving scenarios through interactive design of intelligent cockpits, enhancing the situational awareness and collaborative performance of drivers and passengers. Scenario research and user research methods were employed to summarize typical nonlinear driving scenarios and analyze the needs of family-oriented users for intelligent cockpit interaction design. A design framework and strategies were proposed to guide interaction design schemes. Usability testing was conducted to validate the usability and user acceptance of the design scheme, followed by iterative optimization. Experimental verification demonstrated that the design significantly improved the situational awareness and human-machine collaboration performance of drivers and passengers in typical nonlinear driving scenarios. This led to enhanced driving safety. The study provides practical guidance and theoretical support for intelligent cockpit interaction design, ensuring better driving safety and a more user-friendly experience in family travel scenarios.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680650</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>Spatial-temporal planning of road traffic speed management mobile resources: Enhancing road traffic safety by optimizing resource utilization</title>
      <link>https://trid.trb.org/View/2680648</link>
      <description><![CDATA[Traffic safety on rural roads in various countries, particularly in developing countries, is a pressing concern, with speeding being a major contributing factor to traffic safety issues and crashes. This study introduces a framework for improving rural road traffic safety through the spatial-temporal planning of mobile traffic safety resources with consideration of fixed ones as part of speed management programs. The framework involves converting traffic data into inputs for an optimization model, which serves as a safety tool for traffic safety decision-makers. This tool indicates the time for mobile resources to visit each location. First, potential locations and their relative shares are determined based on a comprehensive analysis of road crash records, road properties, and fixed speed management resource locations, using the location-allocation model in ArcGIS. Then, the optimization model allocates traffic safety resources, considering both distance and time halo effects, which increase the unpredictability of these resources for drivers. A mathematical tool within the proposed framework is introduced to use mobile traffic safety resources in rural areas. This framework is particularly beneficial for developing countries, where resource allocations are planned solely based on the expertise of local professionals, rather than analytical methods. The results demonstrated through a case study on the Arak-Salafchegan Road show effective allocation and relocation of these resources to hotspot locations over time, considering real-world limitations, halo effects, and resource distribution, to improve rural road traffic safety. The framework offers a novel tool to tackle rural road traffic safety challenges. By integrating historical data analysis, integer programming, and real-world insights, the approach provides a robust solution that can be adopted by traffic authorities to make roadways safer.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680648</guid>
    </item>
    <item>
      <title>Comparison of male and female SUV-driver injury rates in similar crashes</title>
      <link>https://trid.trb.org/View/2680647</link>
      <description><![CDATA[The current study sought to determine the extent of differences in serious injury risk by sex using crash data maintained by individual U.S. states. As with many earlier studies, crash and vehicle differences were controlled. The vehicles of interest were restricted to SUVs, the most popular vehicle type in the U.S. Records of SUV-driver crash involvements during 2017 to 2023 were obtained from motor-vehicle crash files maintained by 13 states. Logistic regressions were used to model the odds of a serious or fatal injury for each of the states (A or K on the KABCO scale). Common predictors were light condition (dark vs. daylight), road surface condition (dry vs. slippery), vehicle age (7–12 years old vs. younger), vehicle weight ratio (case vehicle to partner vehicle), driver age (< 25 years vs. 25–64 years vs. 65+ years), and driver sex (female vs. male). An overall female-to-male injury odds ratio was computed from the weighted average of the logarithms of individual state odds ratios. The data were restricted to safety-belt-restrained SUV drivers in head-on crashes with another passenger vehicle. Serious and fatal injuries were coded for 3.8% of the female drivers and 3.4% of the male drivers. Crashes in darkness and crashes of older drivers were significantly more likely to result in serious/fatal injuries, while crashes of younger drivers were significantly less likely to result in serious/fatal injuries. Female drivers were 17% more likely than males to incur serious/fatal injuries (95% confidence limits 8% to 27%). When the opposing vehicle was another SUV, female drivers were only 11% more likely than males to incur serious/fatal injuries (95% confidence limits −4% to 28%). However, female drivers were 20% more likely than males to incur at least minor injuries (95% confidence limits 13% to 28%). Observed differences in serious injury rates for female and male drivers declined after accounting for other driver, vehicle, and crash characteristics. In similar crash circumstances, female drivers are more likely than males to be injured, but this difference is clear only for minor injuries.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680647</guid>
    </item>
    <item>
      <title>Driving style characteristics based lane-changing intention recognition research for truck drivers near highway ramps</title>
      <link>https://trid.trb.org/View/2680645</link>
      <description><![CDATA[The research aims to analyze the driving styles and lane-changing intentions of truck drivers near the highway ramps. Using principal component analysis (PCA), three principal components were selected for cluster analysis, examining driving styles from the perspectives of risk tolerance, longitudinal, and lateral driving characteristics. An intention recognition model for lane-changing was developed and trained, and its validity was verified with High-D dataset. The proposed model in this study achieves an accuracy of 93.7% and an F1 score of 0.891, demonstrating its excellent performance in precision-related metrics. Moreover, the study compares the differences in driving styles and lane-changing intentions between truck drivers and sedan drivers. The main conclusions are as follows: the lane-changing process consists of two stages: intention and execution. Driving style is a critical factor in the establishment of lane-changing intention models. Four seconds is a proper time window for lane-changing intention prediction. The lane-changing behavior characteristics of truck drivers differ significantly from those of sedan drivers. The study results improve the understanding of truck lane-changing behavior near highway ramps, and they also help to figure out the safety mechanisms in the future human-vehicle cooperative traffic scenarios.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680645</guid>
    </item>
    <item>
      <title>Effects of confidence level and hazard type on the visual search patterns and hazard response times of young drivers</title>
      <link>https://trid.trb.org/View/2680646</link>
      <description><![CDATA[While numerous studies have reported that overconfidence affects young drivers’ crash risk, direct comparisons of hazard perception differences among overconfident, underconfident young drivers and their peers with relatively accurate self-rated confidence remain limited. This study addressed this gap by exploring the effects of hazard type and confidence level on the hazard perception of young drivers. A total of 72 young drivers aged 18–25 years agreed to participate in this study. A 2 (hazard type: environmental prediction hazards/EP, behavioral prediction hazards/BP) × 4 (driver group: overconfident, very confident, moderately confident, underconfident) mixed experimental design was adopted. Twelve video clips with BP hazards and 12 with EP hazards were presented to the four groups of drivers. Response time and eye movement were recorded. Under-confident drivers had longer response times than very confident and moderately confident drivers did, regardless of hazard types. Overconfident drivers took longer to fixate the AOIs that contained EP hazards and responded slower to EP hazards than moderately confident drivers did. Although overconfident drivers responded slower to BP hazards compared to very confident and moderately confident drivers did, all three groups took similar times to fixate the AOIs that contained BP hazards. Additionally, compared to very confident drivers, overconfident drivers had a higher no-response rate and fewer fixations on the hazards. These findings indicate that moderately confident drivers outperformed both overconfident and underconfident drivers in response times, highlighting how confidence level influences hazard perception and response efficiency depending on hazard type. The results provide valuable insights for hazard perception training programs tailored to young drivers, emphasizing the need to address both overconfidence and under-confidence in driver education.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680646</guid>
    </item>
    <item>
      <title>Prevalence and predictors of driving under the influence of alcohol in Switzerland: A mandatory roadside survey</title>
      <link>https://trid.trb.org/View/2680644</link>
      <description><![CDATA[This nationwide roadside survey aimed to determine the prevalence of driving under the influence of alcohol (DUI) among car drivers in Switzerland and identify associated factors. Adhering to the European study Baseline guidelines, the survey was stratified by road type, time window, and language region. Fourteen police corps conducted controls across areas representing 59% of the Swiss population. Locations were determined in randomly selected municipalities. Drivers were stopped randomly or via road blockage, and all were tested for alcohol. Logistic regression models were used to analyze predictors of DUI, both in terms of driving with a detectable alcohol concentration [>0 milligram of alcohol per liter of breath (mg/l)] and driving with concentrations above the legal limit (0.25 mg/l for most drivers; 0.05 mg/l for specific groups, e.g., novice drivers). The models accounted for potential influencing factors, and data were weighted to ensure representativeness. Of 4,847 drivers tested, 3.6% had detectable alcohol levels, and 0.4% exceeded the legal limit. Male drivers, individuals aged 31 years or older, and those departing from restaurants, bars, or parties had a higher prevalence of driving with detectable alcohol levels or levels above the legal limit. Drivers in the French-speaking region were more likely to have detectable alcohol concentrations than those in the German-speaking region, although no significant regional difference was found for exceeding the legal limit. The prevalences of driving with a detectable alcohol concentration and of exceeding the alcohol limit were higher at night, on weekends, and tended to be higher on urban roads compared to rural roads or motorways. Exceeding the limit was more common in fine weather than in cloudy or rainy conditions. Switzerland has a relatively low prevalence of exceeding the alcohol limit compared to other European countries. However, male gender, older age, nighttime and weekend driving, and social drinking contexts increased DUI risk. Continued monitoring and targeted interventions addressing high-risk groups, locations, and times are essential for enhancing road safety.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680644</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>Longitudinal assessment of post-concussion driving reaction time</title>
      <link>https://trid.trb.org/View/2680640</link>
      <description><![CDATA[Concussed patients present multiple neurocognitive and motor impairments including slowed reaction time (RT), a function essential to driving. The authors compared driving RT between concussed and non-concussed individuals across their concussion recovery (aim 1) and explored whether clinical concussion outcomes were correlated with driving RT uniquely in the concussion group (aim 2). The authors recruited collegiate athletes (26 concussed and 23 age- and sex-matched controls) to complete the sport concussion assessment tool (SCAT5), a computerized neurocognitive test (CNS Vital Signs), and a driving simulation across 3 timepoints: ≤72 h, asymptomatic, and unrestricted medical clearance. RTs were recorded in response to 4 unanticipated driving events. CNSVS included 10 measures of cognitive function. General linear mixed models assessed interaction between group and time for aim 1 and group and concussion assessment outcome for aim 2 (α = 0.05). Pairwise comparisons with Cohen’s d values were used following significant interactions and main effects. There was a significant main effect for timepoint, such that pedestrian RT was slower at the ≤72-h timepoint relative to both the asymptomatic (p value = 0.023) and unrestricted medical clearance (p- value = 0.022). There were no other significant group-by-timepoint interaction or timepoint main effects for yellow stoplight RT (p-value range = 0.334–0.798), vehicle incursion RT (p-value range = 0.234–0.925) or vehicle cross RT (p-value range = 0.177–0.364). There was no significant group main effect (p-value range = 0.077–0.955), assessment outcome main effect (p-value range = 0.099–0.999) or interaction (p-value range = 0.103–0.998) for predicting any of the RTs, except for executive function (p = 0.046), motor speed (p = 0.006), and psychomotor speed (p = 0.027) predicting vehicle cross RT regardless of group. This study demonstrates that driving RT may not differ between acutely concussed and healthy individuals or may not be detected on a short, simulated drive. Current clinical concussion outcomes poorly relate to driving RT. More research is needed to determine when it is safe to return to driving post-concussion.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680640</guid>
    </item>
    <item>
      <title>Examining the spatial correlations between road traffic crash severity and their causative factors: A GIS-based geographically weighted regression approach for Southwest Nigeria</title>
      <link>https://trid.trb.org/View/2680639</link>
      <description><![CDATA[This study explored the spatial relationship between Road Traffic Crash (RTC) severity and their determinants in southwest Nigeria with a view to accounting for how the influence of these determinants varies across the study area for the implementation of target-oriented crash reduction strategies. Real-time RTC data for the area was collected over five months using a mobile app developed by the author. Federal Road Safety Corps (FRSC) rescue officers across the 28 commands in the study area were trained to use the app, allowing them to capture and send real-time RTC reports to the app’s central server. The datasets were analyzed using a GIS-Based Geographically Weighted Regression (GWR) model. The results reveal that 13 factors account for 80% of RTC severity in the area with speeding being the main predictor, accounting for as high as 98% of the severity-weighted crashes on single-carrier highways, while fatigue (75–78%) and wrong-way driving (6.5–8.9%) contributed to most of the crashes on dual-carrier highways. The study calls for immediate action to reduce RTCs and recommends that the FRSC design place-specific interventions targeting prevalent RTC causative factors along each road in the study area.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680639</guid>
    </item>
    <item>
      <title>Kinematic analysis of volunteers in a highly reclined rigid seat in limited load frontal sled tests</title>
      <link>https://trid.trb.org/View/2680638</link>
      <description><![CDATA[The goal of the study was to investigate the kinematic response patterns of human volunteers in highly reclined postures with a safe limited load. The sled testing environment consisted of an adjustable rigid seat and an integrated 3-point seat belt, using a pulse with a nominal peak deceleration of 3.5 g. Preliminary tests with anthropomorphic test devices and simulations with human body model were performed to verify the safety of the testing environment. Various sensors were set up to record static data and kinematic responses from three 50th percentile male volunteers. A total of 36 tests were carried out under 4 seat configurations, including standard posture, semi-reclined posture, reclined posture, and zero-gravity posture (a modern term for a highly reclined vehicle seat design mimicking a comfortable recliner with leg support). All procedures were approved by the relevant ethics committees. The results indicated that as the reclining degree increased, the initial position of the hip moved backward and downward. The maximum displacement in the Z-axis of the head and neck increased, as well as the forward excursion of the upper torso and hip also significantly increased, while the shoulder and lap belts forces decreased. This illustrates that the integrated 3-point seat belt fails to effectively restrain the torso and hip of the occupants in highly reclined postures, particularly in the zero-gravity posture. These responses mirror those of a real human body in the early stage of a collision, providing insights into the potential injury risks for reclined occupants in crash.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:29:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680638</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>
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