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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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      <title>Structural Brain Biomarker on Driving Safety in Healthy Older Adults: An MRI-Based Review</title>
      <link>https://trid.trb.org/View/2697813</link>
      <description><![CDATA[This review synthesizes current evidence on the relationship between structural brain changes and driving safety in cognitively healthy older adults. Using magnetic resonance imaging (MRI) structural biomarkers—such as white matter hyperintensities (WMH), brain atrophy (BA), and regional gray matter volumes (rGMVs)—recent studies have demonstrated that even mild or asymptomatic alterations are associated with impaired driving behavior and increased crash risk. Emerging machine learning models that incorporate rGMVs have achieved high accuracy and specificity in identifying high-risk drivers, though sensitivity and precision remain limited when relying solely on structural MRI data. Japan’s Brain Dock system, an MRI-based brain healthcare screening program developed uniquely in Japan, provides a valuable infrastructure for large-scale neuroimaging-based risk assessment. Importantly, structural MRI biomarkers can be influenced by lifestyle improvements, such as quitting smoking and reducing alcohol consumption, as well as by the treatment of lifestyle-related diseases, including diabetes and hypertension. Overall, this review highlights the potential of neuroimaging-informed approaches to identify at-risk drivers and to support targeted preventive strategies, thereby contributing to both brain healthcare program and traffic safety in aging societies.]]></description>
      <pubDate>Fri, 28 Aug 2026 08:35:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697813</guid>
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    <item>
      <title>Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making</title>
      <link>https://trid.trb.org/View/2685886</link>
      <description><![CDATA[The inflexible human-autonomy relationship within autonomous driving scenarios still has not realized synergetic intelligence, therefore unable to provide adaptive and context-sensitive decision-making and sometimes leading to violation of human pReferences or even hazards. In this paper, we utilize functional near-infrared spectroscopy (fNIRS) signals as real-time human risk-perception feedback to establish a brain-in-the-loop (BiTL) trained artificial intelligence algorithm for decision-making. The proposed algorithm uses the result of driving risk reasoning as one input of reinforcement learning combining fNIRS-based risk and driving safety field model-based risk, realizing integrating human brain activity into the reinforcement learning scheme, then overcoming the disadvantage of machine-oriented intelligence that could violate human intentions. To achieve policy learning within limited BiTL training periods, we add two modification features to the proposed algorithm based on TD3. The experiment involving twenty participants has been conducted, and the results show that in continuously high-risk driving scenarios, compared to traditional reinforcement learning algorithms without human participation, the proposed algorithm can maintain a cautious driving policy and avoid potential collisions, validated with both proximal surrogate indicators and success rates.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:33:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685886</guid>
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    <item>
      <title>A neurobehavioral study of bus crowding valuations in picture-based and immersive choice experiments</title>
      <link>https://trid.trb.org/View/2706962</link>
      <description><![CDATA[This paper investigates how picture-based and virtual reality (VR) experiment types influence individuals’ valuation of bus crowding in stated preference settings and explores the cognitive mechanisms underlying heterogeneity in crowding perceptions, using a sample of 38 participants, each completing 40 choice scenarios in each of the two experimental formats. We estimate individual-level crowding multipliers, that is, the ratio of travel time valuation in crowded versus uncrowded scenarios, across two experiment types and investigate their correlations with neural biomarkers. We find systematic differences in cognitive processing across formats: participants in the picture-based experiment tend to rely more on internal beliefs and prior experiences, while those in the VR-based experiment are more influenced by the visual information presented in choice experiments. However, these cognitive and perceptual differences did not translate into significant behavioral differences in crowding valuations, i.e., they resulted in similar crowding multipliers across both experiment types. One potential explanation is the static nature of the experimental stimuli, which may limit the capacity of VR to evoke personal beliefs and richer experiential responses. Our findings suggest that picture-based experiments are sufficient for capturing preferences in the static choice contexts considered here, while experiments incorporating dynamic or interactive stimuli (e.g., pedestrians’ route choices under different built environments) warrant further research.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706962</guid>
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    <item>
      <title>Intelligent Driver Drowsiness Detection Using Brain Imaging: A Systematic Review for Enhanced Road Safety in Intelligent Transportation Systems</title>
      <link>https://trid.trb.org/View/2734861</link>
      <description><![CDATA[Driver drowsiness poses a significant threat to road safety, contributing to numerous accidents globally, according to historical statistical data. This study provides an exhaustive overview of driver drowsiness, encompassing its symptoms, causes, prevention strategies, and underlying physiological and neural changes that occur when transitioning from wakefulness to a drowsy state. This review paper explores the complexities of detecting driver drowsiness, with a focus on brain imaging-based methodologies, and addresses four key research questions. We systematically analyze and review existing research on driver drowsiness detection using machine learning algorithms at both macro and micro levels, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We categorize brain imaging modalities into four main groups: electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging, and magnetoencephalography. Micro analysis (from 2020 to 2025) explores the application of these modalities in detecting driver drowsiness, discussing their strengths and limitations, and evaluating their effectiveness in simulated and real-world driving experiments. Our evaluation reveals that EEG and fNIRS have emerged as the most prevalent brain imaging methodologies for detecting driver drowsiness, due to their non-invasive and portable nature, high temporal and spatial resolution, and real-time capability. However, challenges persist, including the need for more robust machine learning algorithms, improved signal processing techniques, and enhanced sensor technologies. Furthermore, there is a need for more comprehensive studies that integrate multiple brain imaging modalities and machine learning approaches to detect driver drowsiness. By addressing these key areas, future research can advance the field of driver drowsiness detection and pave the way for safer and more efficient transportation systems.]]></description>
      <pubDate>Thu, 20 Aug 2026 16:53:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2734861</guid>
    </item>
    <item>
      <title>Driving behavior experiments using functional magnetic resonance imaging: A systematic review</title>
      <link>https://trid.trb.org/View/2731158</link>
      <description><![CDATA[Driving a vehicle requires complex brain functions, including attention, motor coordination, and decision-making. Human factors are the main cause of 93% of crashes. Nowadays, it is possible to scan the brain using functional magnetic resonance imaging (fMRI) while performing driving-related tasks, often in simulated environments. This study tries to provide a comprehensive review of the application of brain fMRI in the field of driving behavior. Scopus, PubMed, and Google Scholar were searched until September 2025. Original articles that studied driving using fMRI were included. Screening and selection followed PRISMA guidelines. Seven major topics were identified. For each topic, a unique extraction table was designed. The objectives, participant characteristics, task design, outcome measurements, and behavioral findings were extracted. The final 80 original studies were included. Seven major topics, including distracted driving, intoxicated driving, risk-taking behavior while driving, simulated driving with brain impairment, drivers' physiological state, general brain activity while driving, and simulated driving environment and simulator characteristics, were identified. fMRI studies suggest the involvement of dynamic brain networks underlying driving behavior, influenced by cognitive load, impairment, and environment. Across domains, driving impairment appears to emerges as a network-level phenomenon characterized by disrupted executive control, altered reward valuation, and reduced visuospatial predictive processing. The authors interpret these findings within Menon's triple network framework, in which driving impairment is characterized as a disruption in the dynamic interactions among the central executive, salience, and default mode networks. Future studies should work on driving environment effects and overcoming methodological constraints of fMRI for more realistic simulation.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:04:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731158</guid>
    </item>
    <item>
      <title>A case study reviewing the critical role of meaningful employment in brain injury rehabilitation: An occupational therapy perspective</title>
      <link>https://trid.trb.org/View/2752230</link>
      <description><![CDATA[Background Meaningful employment is essential to recovery, self-esteem, and life satisfaction for individuals with traumatic brain injuries (TBIs), particularly those who are high-functioning. Engagement in purposeful work can foster emotional and functional gains during rehabilitation. Objective This single-case retrospective study explored how meaningful employment impacted one client with TBI and examined how transportation barriers limited access to work. Methods Data were collected from December 2022 through March 2023 using the Canadian Occupational Performance Measure, Beck Depression Inventory, and daily SOAP notes during a three-month occupational therapy intervention. Results The client showed significant improvements in mood, occupational performance, and satisfaction when supported in accessing meaningful work. Conclusions Occupational therapists and interdisciplinary teams play a critical role in facilitating vocational engagement. Structural transportation barriers should be addressed as part of rehabilitation planning to support access to meaningful employment, overall well-being, and long-term community participation.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752230</guid>
    </item>
    <item>
      <title>Effective measures for evaluating safe driving ability with a driving simulator and eye movement tracking</title>
      <link>https://trid.trb.org/View/2704810</link>
      <description><![CDATA[Stroke survivors often wish to resume driving, but objective and reliable indicators for assessing fitness to drive are lacking. The authors aimed to establish effective measures for evaluating safe driving ability in brain-injured patients by integrating driving simulator (DS) performance and eye movement analysis. Participants included brain-injured patients, classified into mild and severe groups using Trail Making Test-B scores and the presence of visual field defects, neglect, or aphasia, alongside healthy controls. Neuropsychological assessments (Trail Making Test-A/B, Kohs Block Design Test, Mini-Mental State Examination) were conducted. Driving performance was evaluated using the Honda Safety Navi DS, focusing on the standard deviation (SD) of steering angle on straight roads and the SD of velocity on curved roads. Eye movements were recorded with Tobii Pro Glasses 2 during hazard detection and dangerous situation scenes, with particular attention to saccade amplitude. Group differences were analyzed using Kruskal–Wallis and Mann–Whitney U tests. The SD of steering angle on straight roads and the SD of velocity on curved roads were significantly higher in mild or severe brain-injured groups compared with healthy controls. However, saccade amplitude was significantly lower in both mild and severe brain-injured groups than in healthy controls during hazard detection scenes, indicating impaired visual exploration. To measure driving ability in both mild and severe brain-injured patients, saccade amplitude provided a promising objective indicator for evaluating driving ability instead of DS alone. These findings support the development of evidence-based fitness to drive assessments for clinical and rehabilitation applications.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704810</guid>
    </item>
    <item>
      <title>Demographic and causal patterns in child cyclist head Injuries: Informing helmet test methods</title>
      <link>https://trid.trb.org/View/2694786</link>
      <description><![CDATA[Children’s cycle helmets are certified using the same impact conditions as adult helmets, which can overlook important factors contributing to child head injuries. The objective is to identify common patterns in traumatic brain injury pathologies, age, sex, riding environment, cause of injury, helmet use, and helmet injury reduction in child cyclists to inform child-specific test methods. The authors reviewed 48,074 head injury cases in cyclists under 17 years across 24 studies. An aggregate data meta-analysis was conducted to identify recurring patterns overall and in studies with a high proportion of severe injuries (n = 3,542 cases).Cases most often involved male riders (71.8%, CI: 71.6–72.1%), aged 10–13 years (40.2%, CI: 39.1–41.3%), occurring on paved roads (75.0%, CI: 74.2–75.9%) without prior collision (84.4%, CI: 84.1–84.8%). Injuries were predominantly intracranial (73.7%, CI: 71.6–75.8%). Studies with mostly severe injuries included significantly more males, on-road incidents, motor vehicle collisions, intracranial hemorrhages, and skull fractures. Helmets reduced odds of head injuries (OR = 0.44, CI = 0.41–0.47), but the efficacy was lower for severe injuries (OR = 0.61, CI = 0.58–0.65), which contrasts most findings for adult helmets. The identified factors associated with severe injuries in child cyclists, such as vehicle collisions and intracranial injuries with rotational mechanisms, are not represented in current child helmet test procedures. This work provides a foundation for further work aimed at quantifying representative head impact biomechanics in typical and severe child cycling incidents, with the ultimate goal of developing helmet test procedures tailored specifically to children.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694786</guid>
    </item>
    <item>
      <title>Longitudinal assessment of post-concussion driving reaction time</title>
      <link>https://trid.trb.org/View/2680640</link>
      <description><![CDATA[ObjectivesConcussed patients present multiple neurocognitive and motor impairments including slowed reaction time (RT), a function essential to driving. We 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).MethodsWe 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 (a?=?0.05). Pairwise comparisons with Cohen’s d values were used following significant interactions and main effects.ResultsThere 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.ConclusionThis 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>Pleasant walking environments enhance emotional experiences and reduce brain activation - an application of fNIRS in urban studies</title>
      <link>https://trid.trb.org/View/2647533</link>
      <description><![CDATA[Promoting walking as a mode of transport is crucial to creating healthy, liveable cities. However, little research has examined how the built environment influences people's experiences, partly because of a lack of methods that directly capture this effect. Functional near-infrared spectroscopy (fNIRS), an optical brain imaging technique, measures neurological responses by monitoring changes in blood oxygenation. Although fNIRS has been employed to compare built and natural environments, its application to studying built environments remains underexplored. This study addresses this gap by investigating how different built walking environments affect participants' emotional experiences and brain activation. The environments included 1) a mixed-use residential area, 2) a mixed-use old town street, 3) a monotonous residential street, and 4) a city centre environment. An expert panel assessed their quality using urban indicators. In an experiment (N = 51), participants watched four 20-s videos of each environment while we measured prefrontal cortex oxygenated and deoxygenated haemoglobin concentrations and collected data on emotional experiences. Results showed that pleasantly perceived walking environments (1 and 2) decreased prefrontal cortex activation, while unpleasantly perceived environments (3 and 4) produced the opposite effect. While our findings demonstrate cognitive differences between urban scenes, further research is needed to identify which environmental factors drive these effects. Overall, viewing different walking environments elicits measurable cognitive responses, highlighting the potential of fNIRS to study urban experiences. Evidence-based research on neurourbanism can inform the creation of urban spaces that promote walking and enhance emotional well-being and health.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:14:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647533</guid>
    </item>
    <item>
      <title>A Novel Nonlinear Smooth Controller for a Brain-Controlled Driving System in Complex Driving Scenarios</title>
      <link>https://trid.trb.org/View/2591118</link>
      <description><![CDATA[With the rapid advancement of technology, brain-controlled driving (BCD) has emerged as a contemporary focal point of research in academia and industry. BCD refers to the application of brain-machine interface (BMI) technology to driving, where control commands from the human brain are decoded by BMI technology and used to assist in the control of vehicles. However, existing BCD systems display inadequate performance in joint lateral and longitudinal control, and BCD systems in complex driving scenarios with other vehicles have not been studied. In this study, a nonlinear smooth controller is proposed, and a BCD system for complex driving scenarios is developed based on it. First, the BCD system is built from three modules, namely, the vehicle module, the BMI module and the controller module. Subsequently, the nonlinear smooth controller is developed based on the BMI controller, the proximal policy optimization (PPO) controller, and the self-adaptive collaborative (SAC) controller. The SAC controller is designed based on a sigmoid function to achieve nonlinear smoothness in the process of allocating control authority between the PPO controller and the BMI controller. The results of online driving experiments demonstrate that the proposed controller is better equipped to handle complex driving scenarios, exhibiting superior performance, heightened safety, and improved user experience compared to the PPO controller and BMI controller. This study holds significant value in advancing the practicality of BCD and providing a foundation for future research on BMI control.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591118</guid>
    </item>
    <item>
      <title>The difference in the ability to perceive danger while driving and when crossing a road between patients after a stroke and a healthy population</title>
      <link>https://trid.trb.org/View/2663652</link>
      <description><![CDATA[Clinicians should be aware of the decrease in driving as well as road-crossing abilities of post-stroke patients as they often resume driving and crossing roads while they have impaired skills. So, they should examine road-crossing and ensure safe crossing as they evaluate driving abilities before approving them to drive again.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663652</guid>
    </item>
    <item>
      <title>Reduced Motion Sickness Using Vestibular EEG-Guided tACS Under Mismatched Physical Rotation and VR Visual Motion</title>
      <link>https://trid.trb.org/View/2591257</link>
      <description><![CDATA[The increasing use of virtual reality (VR) in public transportation enables travelers to engage in immersive entertainment or productive tasks, thereby enhancing the overall travel experience. However, mismatched VR visual motion and physical car motion can cause motion sickness (MS), leading to nausea, postural instability and reduced time for enjoyment or productive tasks. Thus, the benefits of using VR in transportation systems are currently limited to individuals who do not experience MS. Thus, effective MS mitigation is essential for improving travel safety, maximizing travel time, and expanding VR accessibility. Neuromodulation targeting the vestibular apparatus, such as bone-conducted vibration (BCV), has shown promise in reducing MS. However, it remains unclear whether neuromodulation directly targeting vestibular cortical regions, such as transcranial alternating current stimulation (tACS), is superior. This paper focuses on the design and validation of a novel vestibular cortical neuromodulation approach using tACS in mitigating MS in a novel simulated in-car VR environment. Eighty participants were recruited to evaluate the tACS approach. The results demonstrate that the proposed tACS approach effectively reduces nausea, enhances postural stability, and extends survival time in MS. Compared to BCV, tACS demonstrates a faster onset of action and longer mitigation effects. However, from an applied perspective, the effects of our tACS approach were relatively short-lived and accompanied by side effects such as tingling and itching.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:17:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591257</guid>
    </item>
    <item>
      <title>Crash typology of professional cycling crashes</title>
      <link>https://trid.trb.org/View/2636598</link>
      <description><![CDATA[Mild traumatic brain injury (mTBI) is a frequent but underreported consequence of professional cycling crashes, yet current helmet testing standards primarily simulate head-first impacts, and their representation of real-world head impact scenarios is unclear. This study explores crash typology of professional cycling crashes involving head-ground contact through systematic video analysis of 128 head impacts occurring between 2012 and 2024. Most head impacts occurred during road races (113/128, 88%) and were associated with multi-cyclist collisions rather than single-cyclist crashes, with topple-over crashes representing the most common mechanism (49%), followed by skid-outs. Riders predominantly landed front or front-side relative to their direction of travel, with 66% of impacts occurring in a sideways body posture, and head contact most frequently involved the helmet’s side and rim regions (>50% of impacts). Notably, body-first head impacts dominated the crash profiles (92%), with the torso or arms contacting the ground before the head, while direct head-first impacts comprised 8 % of cases. Impact severity was distributed relatively evenly across low (30 %), medium (33%), and high (36%) categories, with collision-related crashes being more likely to result in high-severity outcomes than non-contact crashes. These findings reveal a potential mismatch between current helmet testing protocols and the predominant mechanisms observed in professional cycling crashes. Video-based analysis provides critical insights into impact mechanisms that are overlooked by traditional injury reporting methods, particularly highlighting the prevalence of body-first impacts and side-rim head impacts. This crash typology may provide a foundation for future biomechanical studies and could support the development of helmet testing methods that better represent real-world cycling impact scenarios.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636598</guid>
    </item>
    <item>
      <title>Factors associated with mortality of hospitalized road traffic injury patients in 4 low- and middle-income countries</title>
      <link>https://trid.trb.org/View/2625330</link>
      <description><![CDATA[Road traffic injuries (RTIs) are an important public health problem, especially in low- and middle-income countries (LMICs), and are highly preventable with evidence-based interventions. This study aimed to describe the sociodemographic characteristics, risk factors, and patterns of injury that are associated with in-hospital mortality among patients with RTIs. A prospective observational study was conducted at 8 hospitals in Cambodia, Ethiopia, Mexico, and Zambia with adult patients who sustained moderate to severe RTIs and were admitted to participating hospitals for at least 24 h. Bivariate and multivariable logistic regression models were used to examine the association between relevant variables and death in-hospital. The majority of RTI deaths occurred among males aged 18 to 44 who were pedestrians or riders of 2- or 3-wheeled vehicles. The following variables were associated with in-hospital mortality: Riding a 2- or 3-wheeler (adjusted odds ratio [AOR] 3.30, 95% confidence interval [CI] 1.06–10.23), moderate–severe Glasgow Coma Scale (GCS; AOR 10.27, 95% CI 4.72–22.33), and low systolic blood pressure (AOR 5.97, 95% CI 1.97–18.04). The findings reinforce the important role of traumatic brain injury (TBI) in RTI deaths and highlight the need for capacity building to develop local neurosurgery expertise to manage and treat TBI in LMICs. Evidence-based prevention strategies such as lowering speed limits in urban areas, protecting users via dedicated footpaths and cycle paths, and increasing helmet use are recommended to mitigate the impact of RTIs and reduce mortality among vulnerable road users. In addition, triage systems should be in place to identify patients with moderate–severe GCS and low systolic blood pressure for immediate and intensive care.]]></description>
      <pubDate>Thu, 18 Dec 2025 15:37:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625330</guid>
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