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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>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Injury Patterns in Road Traffic Collisions: A Visual Investigation of
                    Affected Body Parts</title>
      <link>https://trid.trb.org/View/2608393</link>
      <description><![CDATA[
                
                Background. Road safety is a major public concern, as road traffic accidents
                    result in numerous casualties and significant economic losses. In traffic
                    collisions, the pattern of injuries sustained by drivers often varies depending
                    on various accident factors. The interactions between safety device use, alcohol
                    consumption status, and injury locations can reveal important association
                    patterns and insights. Therefore, we examine patterns in injury locations,
                    accounting for safety device use and alcohol consumption.
                Method. In this study, we applied two complementary graphical approaches,
                    including multiple correspondence (MCA) analyses and mosaic plots (MPs).
                Results. The MPs reveal the existence of meaningful patterns between injury
                    location, alcohol consumption, and safety device. Likewise, the MCA reveals that
                    head/neck injuries are more likely to be associated with alcohol impairment. In
                    particular, sober status and safety device used tend to be associated with all
                    injury locations, excepted head/neck injuries. Furthermore, the chi-square
                    statistic with p < 0.05 rejects the null hypothesis (
                        H
                    
                    
                        0
                    ) of non-association between injury location, safety devices, and alcohol
                    consumption factors. By providing concrete evidence and insights, these findings
                    can inspire policymakers and safety device manufacturers to improve the
                    effectiveness of safety devices.
            ]]></description>
      <pubDate>Tue, 14 Oct 2025 10:33:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608393</guid>
    </item>
    <item>
      <title>Can pedestrian headform test results reflect the distribution of head injuries in the real world?</title>
      <link>https://trid.trb.org/View/2367480</link>
      <description><![CDATA[Wrap around distance (WAD) is an important index to evaluate the contact position between pedestrian head and vehicle, and is also one of the key parameters of pedestrian accident reconstruction. The purpose of this paper is to explore whether the pedestrian headform testcan reflect the distribution of head injury in the real world. Firstly, in order to study the distribution of pedestrian head WAD in road accidents in China, a head WAD prediction model was established using logistic regression based on pedestrian height and vehicle collision speed. Secondly, in order to study the distribution of the risk of severe head injuries among pedestrians in accidents, the frequency of pedestrian head impact and the proportion of pedestrian head injury were counted respectively for sedans and SUVs. Subsequently, a risk curve for severe head injuries was constructed based on the head impact frequency and the proportion of severe injuries, utilizing a method that incorporates joint probability. Finally, to investigate the relationship between the headform test results and the distribution of severe head injury risks among pedestrians in road traffic accidents in China, a meticulous regional division of the head WAD was conducted based on the vehicle's front structure. A qualitative comparison was made between the distribution of headform test results in that area and the distribution of pedestrian injuries in the real world within that specific region. The results indicate that the location of pedestrian head impacts is primarily concentrated within the range of WAD 1500 mm to WAD 2300 mm. When pedestrians collide with sedans, the peak frequency of head impact occurs at WAD 1900 mm, whereas in collisions with SUVs, this peak occurs at WAD 1700 mm. In the areas of sedan windshields and A-pillars, as well as the rear portion of SUV hoods and windshield wiper regions, pedestrians' heads are most susceptible to severe injuries. It is noteworthy that within the WAD1000-WAD1500 mm range, the risk of severe head injuries for pedestrians is nearly zero. This study, through the analysis of severe head injury distribution among pedestrians in China, assessed the effectiveness and applicability of The China Insurance Automotive Safety Index (C-IASI) headform test. It provides targeted recommendations for the enhancement of the C-IASI pedestrian assessment protocol and offers crucial reference for optimizing the design of vehicle front structures.]]></description>
      <pubDate>Tue, 16 Apr 2024 09:52:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2367480</guid>
    </item>
    <item>
      <title>Frontal Crash Reconstruction Compared to Event Data Recorders in the
          Crash Investigation Sampling System Database and the Effect on Injury Risk
          Models</title>
      <link>https://trid.trb.org/View/2219000</link>
      <description><![CDATA[This study compares statistical models for frontal crash injuries based on                     delta-v data reported by the vehicle event data recorder (EDR) with injury                     probability models based on delta-v reconstructed by Crash Investigation                     Sampling System (CISS) investigators. Injury probabilities and their follow-on                     use in advanced automatic crash notification (AACN) systems have traditionally                     been based on delta-v obtained through accident reconstruction of field crashes                     in the National Automotive Sampling System Crash Data System (NASS-CDS)                     database. Field delta-v from EDRs in the CISS database is an alternative source                     of information for crash injury probability modeling. In this study, frontal                     impact injury risk probabilities computed from EDR and reconstructed delta-v                     were compared. All data came from the years 2017–2021 of the CISS database,                     which contains EDR downloads and also reconstructed delta-v using crush                     measurements and NHTSA’s WinSmash software. On average, CISS reconstructions                     overestimated delta-v below 16 kph and underestimated delta-v above 16 kph when                     compared to EDR delta-v. CISS also records detailed injury data using the 2015                     Abbreviated Injury Scale (AIS) developed by the Association for the Advancement                     of Automotive Medicine (AAAM). Statistical analysis was performed using logistic                     regression to calculate MAIS 1+, MAIS 2+, MAIS 3+, and ISS 16+ injury                     probabilities. EDR-derived injury probabilities showed a lower risk for serious                     injury at delta-v above 48 kph when compared to probabilities based on CISS                     reconstructed delta-v. AACN algorithms are currently based on reconstructed                     delta-v but are triggered by EDR delta-v in the field. An analysis performed to                     determine the effect of the difference between the source of delta-v on the AACN                     notification threshold showed differences for AACN algorithms trained on EDR                     delta-v compared to reconstructed delta-v. The results of this study showed that                     the trigger threshold for AACN notification will differ by delta-v source, and                     this difference leads to variation in injury prediction.]]></description>
      <pubDate>Mon, 24 Jul 2023 16:54:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2219000</guid>
    </item>
    <item>
      <title>Age-adjusted death rates for motor vehicle traffic injury - United States, 2019</title>
      <link>https://trid.trb.org/View/1940037</link>
      <description><![CDATA[In 2019, the death rate in the United States for motor vehicle traffic injury was 11.1 per 100,000 standard population. The four states with the highest age-adjusted death rates were Mississippi (24.2), Alabama (19.8), New Mexico (19.1), and South Carolina (18.9). The four jurisdictions with the lowest age-adjusted death rates were Rhode Island (6.1), District of Columbia (6.1), New York (5.1), and Massachusetts (4.9). Source: National Vital Statistics System, Mortality, 2019. https://www.cdc.gov/nchs/nvss/deaths.htm Age-adjusted death rates (deaths per 100,000 standard population) were calculated using the direct method and the 2000 U.S. standard population. The 2019 U.S. rate was 11.1. Motor vehicle traffic injuries are identified with International Classification of Diseases, Tenth Revision (ICD-10) codes V02-V04[.1,.9], V09.2, V12-V14[.3-.9], V19[.4-.6], V20-V28[.3-.9], V29-V79[.4-.9], V80[.3-.5], V81.1, V82.1, V83-V86[.0-.3], V87[.0-.8], and V89.2. Decedents included motor vehicle occupants, motorcyclists, pedal cyclists, and pedestrians.]]></description>
      <pubDate>Mon, 13 Jun 2022 13:14:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1940037</guid>
    </item>
    <item>
      <title>A pilot study for investigating the feasibility of supervised machine learning approaches for the classification of pedestrians struck by vehicles</title>
      <link>https://trid.trb.org/View/1903720</link>
      <description><![CDATA[This research focuses on the application of Artificial Intelligence (AI) methodologies to the problem of classifying vehicles involved in lethal pedestrian collisions. Specifically, the vehicle type is predicted on the basis of traumatic injury suffered by casualties, exploiting machine learning algorithms. In the present study, AI-assisted diagnosis was shown to have correct prediction about 70% of the time. In pedestrians struck by trucks, more severe injuries were appreciated in the facial skeleton, lungs, major airways, liver, and spleen as well as in the sternum/clavicle/rib complex, whereas the lower extremities were more affected by fractures in pedestrians struck by cars. Although the distinction of the striking vehicle should develop beyond autopsy evidence alone, the presented approach which is novel in the realm of forensic science, is shown to be effective in building automated decision support systems. Outcomes from this system can provide valuable information after the execution of autoptic examinations supporting the forensic investigation. Preliminary results from the application of machine learning algorithms with real-world datasets seem to highlight the efficacy of the proposed approach, which could be used for further studies concerning this topic.]]></description>
      <pubDate>Wed, 26 Jan 2022 14:14:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1903720</guid>
    </item>
    <item>
      <title>Injury Distributions of Belted Drivers in Various Types of Frontal Impact</title>
      <link>https://trid.trb.org/View/1832653</link>
      <description><![CDATA[Injury distributions of belted drivers in 1998-2013 model-year light passenger cars/trucks in various types of real-world frontal crashes were studied. The basis of the analysis was field data from the National Automotive Sampling System (NASS). The studied variables were injury severity (n=2), occupant body region (n=8), and crash type (n=8). The two levels of injury were moderate-to-fatal (AIS2+) and serious-to-fatal (AIS3+). The eight body regions ranged from head/face to foot/ankle. The eight crash types were based on a previously-published Frontal Impact Taxonomy (FIT).         The results of the study provided insights into the field data. For example, for the AIS2+ upper-body-injured drivers, (a) head and chest injury yield similar contributions, and (b) about 60% of all the upper-body injured drivers were from the combination of the Full-Engagement and Offset crashes. For the AIS2+ lower-body-injured drivers, (a) knee/thigh/hip and foot/ankle injury yield similar contributions, and (b) about 60% of all lower-body-injured drivers were from the combination of the Full-Engagement and Offset crashes.         This analysis may help engineers inform their assessments of candidate countermeasures (e.g., quantification of real-world weighting factors for related optimization studies). Moreover, the analysis may help provide context for regulatory and public-domain considerations - both existing and proposed.]]></description>
      <pubDate>Thu, 09 Dec 2021 10:38:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1832653</guid>
    </item>
    <item>
      <title>Integration of Commercial Pick Up Vehicle to Meet Pedestrian Safety Requirement</title>
      <link>https://trid.trb.org/View/1831687</link>
      <description><![CDATA[Globally, road traffic crashes kill about 1.24 million people each year. Pedestrians constitute 22% of all road deaths, and in some countries this is as high as 60%. The capacity to respond to pedestrian safety is an important component of efforts to prevent road traffic injuries. Pedestrian collisions, like other road traffic crashes, should not be accepted as inevitable because they are, in fact, both predictable and preventable. Examination of pedestrian injury distribution reveals that given an impact speed, the probability of fatal injuries is substantially greater when the striking vehicle is a pick-up rather than a passenger car. Given their utility areas, pickup vehicles require negotiating rough terrains and are therefore engineered with higher ground clearance and larger approach angle. The challenge is to optimize these design parameters and also style the vehicle for pedestrian safety while maintaining a low design cost at the same time.         This document presents methodology and a set of solutions to meet pedestrian impact safety for pickup vehicles as per the guidelines recommended by Regulation (EC) No 78/2009, of the European Parliament and of the Council of 14 January, 2009. Pedestrian lower leg simulation is performed on LS DYNA with an Impactor propelled at a speed of 40Km/h towards the front end of the pickup vehicle. Head injury risk is assessed separately for adults and for children by identifying Head Impact Zones for both in CAD software CAVA CATIA. Head impact simulation is then performed in the relevant parts of the bonnet top area using LS DYNA software. The results thus obtained are used to optimize vehicle's front end to meet pedestrian safety requirements.]]></description>
      <pubDate>Thu, 09 Dec 2021 10:37:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1831687</guid>
    </item>
    <item>
      <title>Evaluation of Ejection Risk and Injury Distribution Using Data from the Large Truck Crash Causation Study (LTCCS)</title>
      <link>https://trid.trb.org/View/1829973</link>
      <description><![CDATA[Three years of data from the Large Truck Crash Causation Study (LTCCS) were analyzed to identify accidents involving heavy trucks (GVWR >10,000 lbs.). Risk of rollover and ejection was determined as well as belt usage rates. Risk of ejection was also analyzed based on rollover status and belt use. The Abbreviated Injury Scale (AIS) was used as an injury rating system for the involved vehicle occupants. These data were further analyzed to determine injury distribution based on factors such as crash type, ejection, and restraint system use. The maximum AIS score (MAIS) was analyzed and each body region (head, face, spine, thorax, abdomen, upper extremity, and lower extremity) was considered for an AIS score of three or greater (AIS 3+).         The majority of heavy truck occupants in this study were belted (71%), only 2.5% of occupants were completely or partially ejected, and 28% experienced a rollover event. In the analyzed data set, none of the belted occupants experienced a complete ejection while 4.4% of unbelted occupants did experience a complete ejection. There was a higher, but not statistically significant, risk of complete and of partial ejection for occupants of heavy trucks that experienced a rollover than for occupants of heavy trucks that did not experience a rollover. An MAIS score of one or two (minor or moderate injury, respectively) accounted for 85% of the injuries to the heavy truck occupants. The MAIS score shifted towards greater injury severity in the cases of rollover, ejection, and unbelted occupants. The head, thorax, and lower extremities contained the highest percentages of AIS 3+ injuries. The head was the most commonly injured body region of ejected occupants, while injuries of belted occupants were most commonly found to the thorax.]]></description>
      <pubDate>Thu, 09 Dec 2021 10:37:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1829973</guid>
    </item>
    <item>
      <title>Finite Element Study of Post-crash Kinematics and Injury Mechanisms of Bus-Pedestrian Collisions</title>
      <link>https://trid.trb.org/View/1829745</link>
      <description><![CDATA[Road traffic accidents are major cause of fatalities and losing property. In many big cities, many common fatal accidents are involved buses and pedestrians. In order to increase safety for pedestrians, it is necessary to understand pedestrian-vehicle collisions and injury mechanisms. Injury characteristics and mechanisms depend on post-crash kinematics. It is obvious that post-crash kinematics resulting from passenger car-pedestrian crash is much different from bus-pedestrian crash. Bus-pedestrian collision study can be done through costly full scale crash tests. An alternative approach to these actual tests is finite element simulations which have been widely employed for vehicle components design and optimisation. This paper has addressed the development of bus-to-pedestrian collision finite element model. The model has been used to study influences of impact speed and impact angle on post-crash kinematics and injury mechanisms of a pedestrian. Relations between the injury risk, the impact speeds and the impact angles have been discussed. The analysis information will assist better design of the bus front structure.]]></description>
      <pubDate>Thu, 09 Dec 2021 10:37:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1829745</guid>
    </item>
    <item>
      <title>Influence of Vehicle Body Type on Pedestrian Injury Distribution</title>
      <link>https://trid.trb.org/View/1802846</link>
      <description><![CDATA[Pedestrian impact protection has been a growing area of research over the past twenty or more years. The results from many studies have shown the importance of providing protection to vulnerable road users as a means of reducing roadway fatalities. Most of this research has focused on the vehicle fleet as a whole in datasets that are dominated by passenger cars (cars). Historically, the influence of vehicle body type on injury distribution patterns for pedestrians has not been a primary research focus. In this study we used the Pedestrian Crash Data Study (PCDS) database of detailed pedestrian crash investigations to identify how injury patterns differ for pedestrians struck by light trucks, vans, and sport utility vehicles (LTVs) from those struck by cars. AIS 2+ and 3+ injuries for each segment of vehicles were mapped back to both the body region of the pedestrian injured and the vehicle source linked to that injury in the PCDS database. The findings indicate that the head is the most frequently injured body region for both vehicle segments, but the lower extremity is second for cars, whereas the torso is second for LTVs. Mapping the injuries back to the vehicles we find that the most frequent sources of injury for cars are the windshield and bumper, while for the LTVs it is the hood and hood leading edge.]]></description>
      <pubDate>Thu, 09 Dec 2021 10:25:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1802846</guid>
    </item>
    <item>
      <title>Evaluation of Injury Risks and Benefits of a Crush Protection Device (CPD) for All-Terrain Vehicles (ATVs)</title>
      <link>https://trid.trb.org/View/1778918</link>
      <description><![CDATA[An updated evaluation of the effects on predicted injuries of an example crush protective device (CPD) proposed for application to All-Terrain Vehicles (ATVs) is described. As in previous evaluations, this involved extending and applying the test and analysis methods defined in ISO 13232 (2005) for motorcycle impacts, to evaluate the effects of the example CPD in a sample of simulated ATV overturn events. Updated modeling refinements included lowering the energy levels of the simulated overturn events; accounting for potential mechanical/ traumatic (compressive) asphyxia mechanisms; refining and calibrating the force-deflection characteristics of helmet, head, legs and soil so as to reduce potential over-prediction of head and leg injuries; and calibrating the simulation against aggregated injury distributions from actual accidents. Approximately 3,080 computer simulations were run, and the results indicated that, for the simulation sample and in comparison to the helmeted baseline ATV, addition of the example CPD created injury and fatality risks that were greater than its injury and fatality benefits. This study also confirmed results of other research indicating that helmet wearing has substantial net injury benefits, and a low risk/benefit percentage, confirming the importance, effectiveness and low additional injury risk of helmet wearing.]]></description>
      <pubDate>Thu, 09 Dec 2021 10:12:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/1778918</guid>
    </item>
    <item>
      <title>Near and Far-Side Adult Front Passenger Kinematics in a Vehicle Rollover</title>
      <link>https://trid.trb.org/View/1776850</link>
      <description><![CDATA[In this study, U.S. accident data was analyzed to determine interior contacts and injuries for front-seated occupants in rollovers. The injury distribution for belted and unbelted, non-ejected drivers and right front passengers (RFP) was assessed for single-event accidents where the leading side of the vehicle rollover was either on the driver or passenger door. Drivers in a roll-left and RFP in roll-right rollovers were defined as near-side occupants, while drivers in roll-right and RFP in roll-left rollovers were defined as far-side occupants.         Serious injuries (AIS 3+) were most common to the head and thorax for both the near and far-side occupants. However, serious spinal injuries were more frequent for the far-side occupants, where the source was most often coded as roof, windshield and interior. Based on the injury sources for both situations, head injuries seem to occur from contact with the roof, windshield (in particular for unbelted occupants) and pillars, while thoracic injuries resulted from contact with steering assembly and the interior.         The field injury data was compared with the Hybrid III responses obtained from simulated mathematical rollovers to better understand occupant kinematics and injury biomechanics. These simulations were validated using laboratory tests. The laboratory tests included the FMVSS 208 dolly rollover, the ADAC corkscrew, curb and soil-trips, bounce-overs and fall-overs.         Based on the mathematical simulations, the kinematics of the front far-side occupant differed from that of the near-side. For the belted far-side occupant, the torso often slipped out of the belt which allows excursion towards the near-side occupant. For the belted near-side occupant, the shoulder belt remained on the upper body during the initial roll phase. The occupant nonetheless moved up and outwards and the head could contact the roof-rail and header areas depending on the rollover condition simulated. The near-side occupant's head crossed the window plane more frequently than the head of the far-side occupant. Dummy kinematics from the simulation help explain the frequency of serious head and thorax injuries reported in the field. Field data analysis and mathematical simulations are useful in understanding injury biomechanics and providing guidance for future testing.       ]]></description>
      <pubDate>Thu, 09 Dec 2021 10:12:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1776850</guid>
    </item>
    <item>
      <title>US and UK Field Rollover Characteristics</title>
      <link>https://trid.trb.org/View/1776841</link>
      <description><![CDATA[In this study, US and UK accident data were analyzed to identify parameters that may influence rollover propensity to analyze driver injury rate. The US data was obtained from the weighted National Automotive Sampling System (NASS-CDS), calendar years 1992 to 1996. The UK pre-roll data was obtained from the national STATS 19 database for 1996, while the injury information was collected from the Co-operative Crash Injury Study (CCIS) database.         In the US and UK databases, rollovers accounted for about 10% of all crashes with known crash directions. In the US and UK databases, most rollovers occurred when the vehicle was either going straight ahead or turning. The propensity for a rollover was more than 3 times higher when going around a bend than a non-rollover. In the UK, 74% of rollovers occurred on clear days with no high winds and 14% on rainy days with no high winds. In the US, 83% of rollovers took place in non-adverse weather conditions and 10% with rain. In the US and the UK, more than 50% of rollovers happened during daylight. However, compared with non-rollover incidents, US and UK rollovers had a higher propensity to occur with sleet/fog or snow and in the dark without streetlights. In the UK, 76% of rollovers took place on roads with a speed limit of 50 mph (80 kph) and above, while in the US, the rate was 51%. In the US, drivers were most often distracted by people inside and outside their vehicle or when eating and/or drinking, and falling asleep.         The results indicate that pre-rollover characteristics are somewhat similar in the US and in the UK. Rollovers were more likely to occur with sleet/fog or snow and in the dark than non-rollover crashes. Vehicles that were traveling straight on a highway or going around a curve were most likely to roll.         In this study, the overall injury distribution was similar in US and UK drivers. For belted and unbelted drivers, serious injuries were most frequent in the head and in the thorax, emphasizing the need to provide protection in both these areas. The pre-rollover and injury information obtained in this study may thus be useful for the development of global preventive measures.       ]]></description>
      <pubDate>Thu, 09 Dec 2021 10:12:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1776841</guid>
    </item>
    <item>
      <title>Characterization of fatal injuries in oil and gas industry-related helicopter accidents in the Gulf of Mexico, 2004-2014</title>
      <link>https://trid.trb.org/View/1888988</link>
      <description><![CDATA[Transportation events are the most common cause of offshore fatalities in the oil and gas industry, of which helicopter accidents comprise the majority. Little is known about injury distributions in civilian helicopter crashes, and knowledge of injury distributions could focus research and recommendations for enhanced injury prevention and post-crash survival. This study describes the distribution of injuries among fatalities in Gulf of Mexico oil and gas industry-related helicopter accidents, provides a detailed injury classification to identify potential areas of enhanced safety design, and describes relevant safety features for mitigation of common injuries.  Decedents of accidents during 2004-2014 were identified, and autopsy reports were requested from responsible jurisdictions. Documented injuries were coded using the Abbreviated Injury Scale (AIS), and frequency and proportion of injuries by AIS body region and severity were calculated. Injuries were categorized into detailed body regions to target areas for prevention.  A total of 35 autopsies were coded, with 568 injuries documented. Of these, 23.4% were lower extremity, 22.0% were thorax, 13.6% were upper extremity, and 13.4% were face injuries. Minor injuries were most prevalent in the face, neck, upper and lower extremities, and abdomen. Serious or worse injuries were most prevalent in the thorax (53.6%), spine (50.0%), head (41.7%), and external/other regions (75.0%). The most frequent injuries by detailed body regions were thoracic organ (23.0%), thoracic skeletal (13.3%), abdominal organ (9.6%), and leg injuries (7.4%). Drowning occurred in 13 (37.1%) of victims, and drowning victims had a higher proportion of moderate brain injuries (7.8%) and lower number of documented injuries (3.8) compared with non-drowning victims (2.9 and 9.4%, respectively).  Knowledge of injury distributions focuses and prioritizes the need for additional safety features not routinely used in helicopters. The most frequent injuries occurred in the thorax and lower extremity regions. Future research requires improved and expanded data, including collection of detailed data to allow characterization of both injury mechanism and distribution. Improved safety systems including airbags and helmets should be implemented and evaluated for their impact on injuries and fatalities.]]></description>
      <pubDate>Tue, 23 Nov 2021 11:49:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/1888988</guid>
    </item>
    <item>
      <title>List of Occupant Injury Criteria</title>
      <link>https://trid.trb.org/View/1862922</link>
      <description><![CDATA[This study constitutes a summary literature review of injury criteria currently used by regulatory agencies (aerospace and automotive) worldwide, and presents state-of-the-art injury criteria and associated research for most body regions. The literature review findings have been divided into two categories: state-of-the-art injury criteria and state-of-the-art injury research. The state-of-the-art injury criteria category collects those documents where injury criteria are well defined and includes adequate tolerance limits for the criterion in question. This injury criterion should be measurable or derived from physical test parameters. On the other hand, the papers documented as state-of-the-art injury research include those criteria that were or are currently being developed but do not have well defined limits or cannot be measured from physical test parameters. The study divides injury criteria into classifications of: head, neck, torso, upper extremities, and lower extremities. Each body region is then divided into sagittal and coronal loading. Sagittal and coronal loading are subdivided into existing-regulatory, state-of-the-art injury criteria, and state-of-the-art injury research according to the previous definitions.]]></description>
      <pubDate>Sun, 15 Aug 2021 18:07:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/1862922</guid>
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