scholarly journals The relationship between driving volatility in time to collision and crash-injury severity in a naturalistic driving environment

2020 ◽  
Vol 28 ◽  
pp. 100136 ◽  
Author(s):  
Behram Wali ◽  
Asad J. Khattak ◽  
Thomas Karnowski
Keyword(s):  
Injury Severity ◽  
Time To Collision ◽  
Naturalistic Driving ◽  
The Relationship ◽  
Crash Injury Severity

Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models

2017 ◽  
Vol 108 ◽  
pp. 172-180 ◽  
Author(s):  
Wen Cheng ◽  
Gurdiljot Singh Gill ◽  
Taha Sakrani ◽  
Mohan Dasu ◽  
Jiao Zhou
Keyword(s):  
Injury Severity ◽  
Weather Data ◽  
Crash Frequency ◽  
Crash Injury Severity

scholarly journals Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Daiquan Xiao ◽  
Quan Yuan ◽  
Shengyang Kang ◽  
Xuecai Xu
Keyword(s):  
Random Effects ◽  
Traffic Safety ◽  
Injury Severity ◽  
Probit Model ◽  
Probit Analysis ◽  
Novice Drivers ◽  
Error Terms ◽  
Time Specific ◽  
Crash Types ◽  
Crash Injury Severity

This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers.

Crash Data-Based Investigation into How Injury Severity Is Affected by Driver Errors

2020 ◽  
Vol 2674 (5) ◽  
pp. 452-464 ◽  
Author(s):  
Mohammad Razaur Rahman Shaon ◽  
Xiao Qin
Keyword(s):  
Injury Severity ◽  
Model Performance ◽  
Severe Injuries ◽  
Crash Data ◽  
Severity Prediction ◽  
Safety Interventions ◽  
Driving Task ◽  
Driver Error ◽  
Crash Characteristics ◽  
Crash Injury Severity

Unsafe driving behaviors, driver limitations, and conditions that lead to a crash are usually referred to as driver errors. Even though driver errors are widely cited as a critical reason for crash occurrence in crash reports and safety literature, the discussion on their consequences is limited. This study aims to quantify the effect of driver errors on crash injury severity. To assist this investigation, driver errors were categorized as sequential events in a driving task. Possible combinations of driver error categories were created and ranked based on statistical dependences between error combinations and injury severity levels. Binary logit models were then developed to show that typical variables used to model injury severity such as driver characteristics, roadway characteristics, environmental factors, and crash characteristics are inadequate to explain driver errors, especially the complicated ones. Next, ordinal probit models were applied to quantify the effect of driver errors on injury severity for rural crashes. Superior model performance is observed when driver error combinations were modeled along with typical crash variables to predict the injury outcome. Modeling results also illustrate that more severe crashes tend to occur when the driver makes multiple mistakes. Therefore, incorporating driver errors in crash injury severity prediction not only improves prediction accuracy but also enhances our understanding of what error(s) may lead to more severe injuries so that safety interventions can be recommended accordingly.

Analysis of crash injury severity on two trans-European transport network corridors in Spain using discrete-choice models and random forests

2020 ◽  
Vol 21 (3) ◽  
pp. 228-233 ◽  
Author(s):  
Bahar Dadashova ◽  
Blanca Arenas-Ramires ◽  
Jose Mira-McWillaims ◽  
Karen Dixon ◽  
Dominique Lord
Keyword(s):  
Discrete Choice ◽  
Random Forests ◽  
Injury Severity ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Transport Network ◽  
Crash Injury Severity

scholarly journals Study on Crash Injury Severity Prediction of Autonomous Vehicles for Different Emergency Decisions Based on Support Vector Machine Model

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 381 ◽  
Author(s):  
Yaping Liao ◽  
Junyou Zhang ◽  
Shufeng Wang ◽  
Sixian Li ◽  
Jian Han
Keyword(s):  
Support Vector Machine ◽  
Autonomous Vehicles ◽  
Injury Severity ◽  
Prediction Models ◽  
Motor Vehicle ◽  
Support Vector ◽  
Severity Prediction ◽  
Emergency Decision ◽  
The Impact ◽  
Crash Injury Severity

Motor vehicle crashes remain a leading cause of life and property loss to society. Autonomous vehicles can mitigate the losses by making appropriate emergency decision, and the crash injury severity prediction model is the basis for autonomous vehicles to make decisions in emergency situations. In this paper, based on the support vector machine (SVM) model and NASS/GES crash data, three SVM crash injury severity prediction models (B-SVM, T-SVM, and BT-SVM) corresponding to braking, turning, and braking + turning respectively are established. The vehicle relative speed (REL_SPEED) and the gross vehicle weight rating (GVWR) are introduced into the impact indicators of the prediction models. Secondly, the ordered logit (OL) and back propagation neural network (BPNN) models are established to validate the accuracy of the SVM models. The results show that the SVM models have the best performance than the other two. Next, the impact of REL_SPEED and GVWR on injury severity is analyzed quantitatively by the sensitivity analysis, the results demonstrate that the increase of REL_SPEED and GVWR will make vehicle crash more serious. Finally, the same crash samples under normal road and environmental conditions are input into B-SVM, T-SVM, and BT-SVM respectively, the output results are compared and analyzed. The results show that with other conditions being the same, as the REL_SPEED increased from the low (0–20 mph) to middle (20–45 mph) and then to the high range (45–75 mph), the best emergency decision with the minimum crash injury severity will gradually transition from braking to turning and then to braking + turning.

scholarly journals Association between Insurance Status and Hospital Length of Stay following Trauma

2016 ◽  
Vol 82 (3) ◽  
pp. 281-288 ◽  
Author(s):  
Brian R. Englum ◽  
Xuan Hui ◽  
Cheryl K. Zogg ◽  
Muhammad Ali Chaudhary ◽  
Cassandra Villegas ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Injury Severity ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Insurance Status ◽  
Trauma Patients ◽  
Uninsured Patients ◽  
National Trauma ◽  
Insured Patients ◽  
The Relationship

Previous research has demonstrated that nonclinical factors are associated with differences in clinical care, with uninsured patients receiving decreased resource use. Studies on trauma populations have also shown unclear relationships between insurance status and hospital length of stay (LOS), a commonly used metric for evaluating quality of care. The objective of this study is to define the relationship between insurance status and LOS after trauma using the largest available national trauma dataset and controlling for significant confounders. Data from 2007 to 2010 National Trauma Data Bank were used to compare differences in LOS among three insurance groups: privately insured, publically insured, and uninsured trauma patients. Multivariable regression models adjusted for potential confounding due to baseline differences in injury severity and demographic and clinical factors. A total of 884,493 patients met the inclusion criteria. After adjusting for the influence of covariates, uninsured patients had significantly shorter hospital stays (0.3 days) relative to privately insured patients. Publicly insured patients had longer risk-adjusted LOS (0.9 days). Stratified differences in discharge disposition and injury severity significantly altered the relationship between insurance status and LOS. In conclusion, this study elucidates the association between insurance status and hospital LOS, demonstrating that a patient's ability to pay could alter LOS in acute trauma patients. Additional research is needed to examine causes and outcomes from these differences to increase efficiency in the health care system, decrease costs, and shrink disparities in health outcomes.

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