Predictive modeling of maximum injury severity and potential economic cost in a car accident based on the General Estimates System data

2021 ◽  
Author(s):  
Gunes Alkan ◽  
Robert Farrow ◽  
Haichen Liu ◽  
Clayton Moore ◽  
Hon Keung Tony Ng ◽  
...  
Keyword(s):  
Predictive Modeling ◽  
Injury Severity ◽  
Economic Cost ◽  
System Data ◽  
Car Accident

scholarly journals A novel feature extraction model for traffic injury severity and its application to Fatality Analysis Reporting System data analysis

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041988647
Author(s):  
Lixin Yan ◽  
Yi He ◽  
Lingqiao Qin ◽  
Chaozhong Wu ◽  
Dunyao Zhu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Injury Severity ◽  
Reporting System ◽  
Drunk Driving ◽  
Impact Factors ◽  
Classification Error ◽  
Severe Injuries ◽  
Markov Blanket ◽  
Traffic Injury ◽  
System Data

The prevention of severe injuries during crashes has become one of the leading issues in traffic management and transportation safety. Identifying the impact factors that affect traffic injury severity is critical for reducing the occurrence of severe injuries. In this study, the Fatality Analysis Reporting System data are selected as the dataset for the analysis. An algorithm named improved Markov Blanket was proposed to extract the significant and common factors that affect crash injury severity from 29 variables related to driver characteristics, vehicle characteristics, accidents types, road condition, and environment characteristics. The Pearson correlation coefficient test is applied to verify the significant correlation between the selected factors and traffic injury severity. Two widely used classification algorithms (Bayesian networks and C4.5 decision tree) were employed to evaluate the performance of the proposed feature selection algorithm. The calculation result of the correlation coefficient, accuracy of classification, and classification error rate indicated that the improved Markov Blanket not only could extract the significant impact factors but could also improve the accuracy of classification. Meanwhile, the relationship between five selected factors (atmospheric condition, time of crash, alcohol test result, crash type, and driver’s distraction) and traffic injury severity was also analyzed in this study. The results indicated that crashes occurred in bad weather condition (e.g. fog or worse), in night time, in drunk driving, in crash type of single driver, and in distracted driving, which are associated with more severe injuries.

Injury Severity Analysis with Variable and Correlated Thresholds: Ordered Mixed Logit Formulation

2002 ◽  
Vol 1784 (1) ◽  
pp. 132-141 ◽  
Author(s):  
Karthik K. Srinivasan
Keyword(s):  
Injury Severity ◽  
Mixed Logit ◽  
Logit Models ◽  
Sampling System ◽  
Chi Square ◽  
Data Set ◽  
Traffic Crash ◽  
System Data ◽  
Safety Countermeasures ◽  
Unobserved Variability

An ordered mixed logit (OML) formulation is proposed to model injury severity, given a crash. The proposed formulation extends the ordered probit/logit models by accommodating variable, random, and correlated injury severity thresholds associated with various severity levels. The proposed model is calibrated using a sample from the 1996 National Automotive Sampling System General Estimates System data set. Chi-square tests indicate that the more general OML formulation provides a statistically superior representation of observed injury severity data than corresponding ordered logit models. Model results indicate that injury severity thresholds vary systematically depending on individual, traffic, crash-related, and vehicle characteristics. Further, significant unobserved variability in thresholds is found, and the thresholds are correlated within a given individual. The results suggest drastically increased chances of fatal injury due to certain factors including tripped rollovers and injuries sustained by moped riders. These findings suggest that targeting these drivers, behaviors, and conditions with suitable countermeasures including education, enforcement, or curfews is likely to result in substantial safety benefits. The model and results have important implications for developing effective safety countermeasures and more accurate assessment of their impacts.

scholarly journals Early posttraumatic stress symptoms and levels of distress in trauma patients treated in the resuscitation room: an exploratory study

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Iris Reiner ◽  
Manfred E. Beutel ◽  
Philipp Winter ◽  
Pol M. Rommens ◽  
Sebastian Kuhn
Keyword(s):  
Risk Factors ◽  
Posttraumatic Stress ◽  
Injury Severity ◽  
Posttraumatic Stress Symptoms ◽  
Trauma Patients ◽  
Resuscitation Room ◽  
Stress Symptoms ◽  
High Distress ◽  
Car Accident ◽  
The Impact

Abstract Background The aim of the present study was to investigate the incidence of psychological distress and posttraumatic stress symptoms in trauma patients who have been recruited from the resuscitation room. Further, we wanted to explore risk factors for posttraumatic stress symptoms, taking different accident types into account. Methods Our sample consisted of 45 patients who have been treated in the resuscitation room and were interviewed within the first ten days after treatment. Type of accident, third party fault, previous mental health problems and pretraumatic stress were examined. Patients were interviewed with respect to their currently felt distress regarding the accident. Posttraumatic stress symptoms were measured with the German version of the Impact of Event Scale. Injury severity was assessed by means of the Injury Severity Score. Results Our exploratory and cross-sectional project reveals that more severe injuries were associated with higher distress. However, posttraumatic stress symptoms were predicted by high distress and being involved in a car accident, but not by injury severity. Conclusions We identified two potential risk factors for the development of posttraumatic stress in trauma patients recruited from the resuscitation room: Being involved in a car accident and high distress. Trial registration The project has been registered at the Study Center of Mental Disorders (SPE) at the University Medical Center Mainz (No: 92072014).

scholarly journals Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers

2020 ◽  
Vol 2 (2) ◽  
pp. 120-132 ◽  
Author(s):  
Stephen A Arhin ◽  
Adam Gatiba
Keyword(s):  
Support Vector Machines ◽  
Injury Severity ◽  
Naive Bayes ◽  
Economic Cost ◽  
Naïve Bayes ◽  
Support Vector ◽  
Washington Dc ◽  
Unsignalized Intersections ◽  
Vector Machines ◽  
The City

Abstract The Washington, DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41 789 crashes at unsignalized intersections, which resulted in 14 168 injuries and 51 fatalities. The economic cost of these fatalities has been estimated to be in the millions of dollars. It is therefore necessary to investigate the predictability of the occurrence of theses crashes, based on pertinent factors, in order to provide mitigating measures. This research focused on the development of models to predict the injury severity of crashes using support vector machines (SVMs) and Gaussian naïve Bayes classifiers (GNBCs). The models were developed based on 3307 crashes that occurred from 2008 to 2015. Eight SVM models and a GNBC model were developed. The most accurate model was the SVM with a radial basis kernel function. This model predicted the severity of an injury sustained in a crash with an accuracy of approximately 83.2%. The GNBC produced the worst-performing model with an accuracy of 48.5%. These models will enable transport officials to identify crash-prone unsignalized intersections to provide the necessary countermeasures beforehand.

scholarly journals Predicting Injury Severity of Angle Crashes Involving Two Vehicles at Unsignalized Intersections Using Artificial Neural Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 3871-3880
Author(s):  
S. A. Arhin ◽  
A. Gatiba
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Injury Severity ◽  
Economic Cost ◽  
The United States ◽  
Sigmoid Function ◽  
Unsignalized Intersections ◽  
Fatal Crashes ◽  
Unit Function ◽  
Artificial Neural

In 2015, about 20% of the 52,231 fatal crashes that occurred in the United States occurred at unsignalized intersections. The economic cost of these fatalities have been estimated to be in the millions of dollars. In order to mitigate the occurrence of theses crashes, it is necessary to investigate their predictability based on the pertinent factors and circumstances that might have contributed to their occurrence. This study focuses on the development of models to predict injury severity of angle crashes at unsignalized intersections using artificial neural networks (ANNs). The models were developed based on 3,307 crashes that occurred from 2008 to 2015. Twenty-five different ANN models were developed. The most accurate model predicted the severity of an injury sustained in a crash with an accuracy of 85.62%. This model has 3 hidden layers with 5, 10, and 5 neurons, respectively. The activation functions in the hidden and output layers are the rectilinear unit function and sigmoid function, respectively.

scholarly journals Identification of Determinant Factors for Car Accident Levels Occurred in Mekelle City, Tigray, Ethiopia: Ordered Logistic Regression Model Approach.

2019 ◽  
Author(s):  
Hagazi Gebre Meles ◽  
Desta Brhanu Gebrehiwot ◽  
Fireweini Gebrearegay Tela ◽  
Gebretsadik Gebru Wubet ◽  
Teodros Gebregergis
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Injury Severity ◽  
Descriptive Analysis ◽  
P Value ◽  
Ordered Logistic Regression ◽  
Mekelle City ◽  
Accident Injury ◽  
Car Accident

Abstract Background : The car accident injury level is known to be a result of a complex interaction of factors to drivers’ behavior, vehicle characteristics and environmental condition. Therefore it is obvious that identifying the contribution of the factors to the accident injury is very critical. The objective of study was to perform descriptive analysis to see the characteristics of car accident, to assess the prevalence and determinants of road safety practices in Mekelle City, Tigray, Ethiopia. Methods : A random sample of data was extracted from traffic police office from September 2014- July 2017. An ordered logistic regression model was used to examine factors that worsen the car accident level. Result : A total sample of 385 car accidents were considered in the study of which 56.7% were fatal, 28.6% serious and 14.7% slight injury. The model estimation result showed that, being experienced drivers (Coef. = 0.686; p-value< = 0.050) were found to increase the level of injury. On the other hand, being private vehicle (Coef. = -1.160; p-value <= 0.010), the type of accident of vehicle with pedestrian (Coef. = -2.852; p-value <= 0.010), being heavy truck (Coef. = -0.656; p-value <= 0.050), being a cross country buss (Coef. = -0.889; p-value <= 0.050) and being owner of vehicle is the driver himself (Coef. = -.690, p-value <= 0.050) were found to decrease the level of car accident injury severity. Therefore, it is better to create continued awareness to those who are experienced drivers, who carelessly follow the traffic rules. Special attention is required to government owned vehicle drivers, as they were found to increase the level of car accident injury through different short term trainings.

Quantifying the importance of lifetime frequency versus number of methods in conceptualizing nonsuicidal self-injury severity.

2020 ◽  
Vol 10 (4) ◽  
pp. 442-451 ◽  
Author(s):  
Brooke A. Ammerman ◽  
Ross Jacobucci ◽  
Brianna J. Turner ◽  
Katherine L. Dixon-Gordon ◽  
Michael S. McCloskey
Keyword(s):  
Injury Severity ◽  
Self Injury ◽  
Nonsuicidal Self Injury

Disseminierte intravasale Gerinnung und Fibrinolyse in der Frühphase nach Polytrauma

1995 ◽  
Vol 15 (02) ◽  
pp. 79-86
Author(s):  
L. Lampl ◽  
M. Helm ◽  
M. Tisch ◽  
K. H. Bock ◽  
E. Seifried
Keyword(s):  
Injury Severity Score ◽  
Severity Score ◽  
Injury Severity ◽  
Tissue Type ◽  
Disseminierte Intravasale Gerinnung

ZusammenfassungGerinnungsstörungen nach einem Polytrauma werden eine große Bedeutung für die weitere Prognose der Patienten beigemessen. In einer prospektiv angelegten Studie wurden bei 20 polytraumatisierten Patienten Gerinnungsund Fibrinolyseparameter analysiert, um deren Veränderungen während der präklinischen Phase zu definieren. Die Blutentnahmen wurden zum frühestmöglichen Zeitpunkt am Unfallort und bei Klinikübergabe durchgeführt. Die gewonnenen Proben wurden mit Hilfe eines speziell konzipierten »Kleinlabors« noch vor Ort verarbeitet, um möglichst native Meßwerte zu erhalten. Die Patienten wurden dem Schweregrad der Verletzung entsprechend kategorisiert und hatten einen Verletzungsschweregrad nach NACA > IV und einen Injury Severity Score (ISS) > 20. Die Ergebnisse zeigen, daß bereits in der sehr frühen Phase nach Eintritt des Traumas schwerwiegende Veränderungen des Gerinnungsund Fibrinolysesystems eintreten. Die frühzeitige Thrombingenerierung führt zu einer Verbrauchskoagulopathie und reaktiven Hyperfibrinolyse. Zusätzlich erzeugt die Freisetzung von endothelständigem Tissue-type-Plasminogenaktivator eine primäre Hyperfibrinolyse. Die Veränderungen des Gerinnungsund Fibrinolysesystems in der frühen präklinischen Phase nach Polytrauma können zu schwerwiegenden klinischen Komplikationen wie Blutungen, thromboembolischen Komplikationen und zur Ausbildung von Schockorganen führen.

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