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Open AccessJournal ArticleDOI

Examining Injury Severity of Pedestrians in Vehicle-Pedestrian Crashes at Mid-Blocks Using Path Analysis

TLDR
The results indicate some variables including number of road lanes and the age of pedestrian have indirect impacts on the injury severity through influencing the pre-crash behaviors, which may improve the overall understanding of pedestrian injury severity at mid-blocks.
Abstract
Walking is a sustainable mode of transport which has well established health and environmental benefits. Unfortunately, hundreds of thousands of pedestrians lose their lives each year over the world due to involvement in road traffic crashes, and mid-blocks witness a significant portion of pedestrian fatalities. This study examined the direct and indirect effects of various contributing factors on the pedestrian injury severity in vehicle-pedestrian crashes at mid-blocks. Data of vehicle-pedestrian crashes during 2002-2009 were extracted from the NASS-GES, with pre-crash behaviors and injury severity included. The SEM path analysis method was applied to uncover the inter-relationships between the pedestrian injury severity and various explanatory variables. Both the direct and indirect effects of these explanatory variables on the pedestrian injury severity were calculated based on the marginal effects in the multinomial and ordered logit models. The results indicate some variables including number of road lanes and the age of pedestrian have indirect impacts on the injury severity through influencing the pre-crash behaviors. Although most indirect effects are relatively small compared with the direct effects, the results in this study still provide some valuable information to improve the overall understanding of pedestrian injury severity at mid-blocks.

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Citations
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Examining the Associations between COVID-19-Related Psychological Distress, Social Media Addiction, COVID-19-Related Burnout, and Depression among School Principals and Teachers through Structural Equation Modeling

TL;DR: In this paper , the authors investigated the relationship between COVID-19-related psychological distress, social media addiction, and burnout, and found that burnout associated with COVID19 significantly and positively predicted depression.
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Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations

TL;DR: This study suggests that combining LightGBM and SHapley Additive exPlanations has the potential to develop an interpretable model for predicting road traffic injury severity.
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Using ordered and unordered logistic regressions to investigate risk factors associated with pedestrian crash injury severity in Victoria, Australia.

TL;DR: In this paper , the authors investigated and identified the influential factors determining the severity of pedestrian injuries in traffic crashes in Victoria by using crash data from 2010 to 2019, and found that male pedestrians, children, and older adults were more likely to sustain a higher level of injury in crashes.
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Assessing the Relationships between Internet Addiction, Depression, COVID-19-Related Fear, Anxiety, and Suspicion among Graduate Students in Educational Administration: A Structural Equation Modeling Analysis

Turgut Karakose
- 29 Apr 2022 - 
TL;DR: In this paper , the authors examined the relationship between depression, Internet addiction, depression, COVID-19-related fear, anxiety, and suspicion in graduate students and found that the increased suspicion of the participant students due to COVID19 led to an increase in their depression scores.
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Analysis of craniocerebral injury in facial collision accidents

TL;DR: The accident reconstruction method with the finite element analysis method is combined to study the injury mechanism of pedestrian head impact on the ground in vehicle pedestrian collision accidents to provide a theoretical basis for pedestrian protection and the improvement of vehicle shapes.
References
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Journal ArticleDOI

Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes.

TL;DR: It is revealed that there is a decreasing trend in pedestrian injury risk, controlling for the influences of demographic, road environment, and other risk factors.
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A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model.

TL;DR: Using police-reported collision data from 1997 through 2000 from North Carolina, several factors were found to more than double the average probability of fatal injury for pedestrians in motor-vehicle crashes including darkness without streetlights, speeding involved, and collisions involving a motorist who had been drinking.
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The risk of pedestrian injury and fatality in collisions with motor vehicles, a social ecological study of state routes and city streets in King County, Washington.

TL;DR: Adjusting for pedestrians' and drivers' characteristics and actions, neighborhood medium home values and higher residential densities increased the risk of injury or death, suggesting that pedestrians were not safer in areas with high pedestrian activity.
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The fatality and injury risk of light truck impacts with pedestrians in the United States.

TL;DR: Examination of pedestrian injury distributions reveals that, given an impact speed, the probability of serious head and thoracic injury is substantially greater when the striking vehicle is an LTV rather than a car.
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Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model.

TL;DR: A random parameters bivariate ordered probit model has been developed to examine factors affecting injury sustained by two drivers involved in the same rear-end crash between passenger cars and demonstrates that there indeed exists significant correlation between two drivers’ injuries.
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