Analysis of Severity of Young Driver Crashes: Sequential Binary Logistic Regression Modeling

2002 ◽  
Vol 1784 (1) ◽  
pp. 108-114 ◽  
Author(s):  
Sunanda Dissanayake ◽  
John Lu
Keyword(s):  
Logistic Regression ◽  
Injury Severity ◽  
Selection Process ◽  
Model Development ◽  
Weather Condition ◽  
Binary Logistic Regression ◽  
The United States ◽  
Crash Severity ◽  
Young Drivers ◽  
Single Vehicle

Young drivers have the highest fatality involvement rates of any driver age group within the United States driving population. They also experience a higher percentage of single-vehicle crashes compared with others. When looking at the methods of improving this alarming death rate of young drivers, it is important to identify the determinants of higher crash and injury severity. With that intention, the study developed, using the Florida Traffic Crash Database, a set of sequential binary logistic regression models to predict the crash severity outcome of single-vehicle fixed-object crashes involving young drivers. Models were organized from the lowest severity level to the highest and vice versa to examine the reliability of the selection process, but it was found that there was no considerable impact based on this selection. The developed models were validated and the accuracy was tested by using crash data that were not utilized in the model development, and the results were found to be satisfactory. Factors influential in making a crash severity difference to young drivers were then identified through the models. Factors such as influence of alcohol or drugs, ejection in the crash, point of impact, rural crash locations, existence of curve or grade at the crash location, and speed of the vehicle significantly increased the probability of having a more severe crash. Restraint device usage and being a male clearly reduced the tendency of high severity, and some other variables, such as weather condition, residence location, and physical condition, were not important at all.

scholarly journals Exploring Driver Injury Severity in Single-Vehicle Crashes under Foggy Weather and Clear Weather

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fulu Wei ◽  
Zhenggan Cai ◽  
Pan Liu ◽  
Yongqing Guo ◽  
Xin Li ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Injury Severity ◽  
Mixed Logit ◽  
Weather Conditions ◽  
Crash Severity ◽  
Young Drivers ◽  
Self Employment ◽  
Road Surfaces ◽  
Single Vehicle ◽  
Clear Weather

The purpose of this study is to investigate and compare the significant influencing factors of driver injury severity in single-vehicle (SV) crashes under foggy and clear weather conditions. Based on data for SV crashes in Shandong Province, China, the mixed logit model (MLM) was employed to interpret driver injury severity for SV crashes in clear and foggy weather. The results showed that there are significant differences in the influencing factors of the severity of SV crashes in foggy and clear weather. Specifically, 15 factors are significantly associated with the severity of SV crashes in clear weather, and 18 factors are significantly associated with the severity of SV crashes in foggy weather. In addition, young drivers (age < 30), non-dry road surfaces, and signal control significantly influence the severity of foggy weather crashes but not clear weather crashes. Self-employment and weekends have significant effects on the severity of crashes only in clear weather. Interestingly, drivers whose occupation is farming showed opposite trends in the effect of crash severity in foggy and clear weather. Based on the findings of this research, some potential countermeasures can be adopted to reduce crash severity in foggy and clear weather.

Modeling head-on crash severity on NCDOT freeways: a mixed logit model approach

2019 ◽  
Vol 46 (4) ◽  
pp. 322-328 ◽  
Author(s):  
Pengfei Liu ◽  
Wei (David) Fan
Keyword(s):  
Logit Model ◽  
Injury Severity ◽  
Safety Information ◽  
Mixed Logit ◽  
Weather Condition ◽  
Mixed Logit Model ◽  
Young Drivers ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
Model Approach

This study employs a mixed logit model approach to evaluate contributing factors that significantly affect the severity of head-on crashes. The head-on crash data are collected from Highway Safety Information System (HSIS) from 2005 to 2013 in North Carolina. The effects that vehicle, driver, roadway, and environmental characteristics have on the injury severity of head-on crashes are examined. The results of this research demonstrate that adverse weather, young drivers, rural roadways, and pickups are found to be better modeled as random-parameters at specific injury severity levels, while others should remain fixed. Also, the model results indicate that driving under the influence of alcohol or drugs, grade or curve roadway configuration, old drivers, high speed limit, motorcycles will increase the injury severity of head-on crashes. Adverse weather condition, two-way divided road, traffic control, young drivers, and pickups will decrease the injury severity of head-on crashes.

scholarly journals Lead Exposure and Cardiovascular Disease among Young and Middle-Aged Adults

2019 ◽  
Vol 7 (11) ◽  
pp. 103 ◽  
Author(s):  
Emmanuel Obeng-Gyasi
Keyword(s):  
Blood Pressure ◽  
Logistic Regression ◽  
Lead Exposure ◽  
Binary Logistic Regression ◽  
The United States ◽  
Gamma Glutamyl Transferase ◽  
Middle Aged ◽  
The Mean ◽  
Glutamyl Transferase ◽  
Middle Aged Adults

Lead and its effects on cardiovascular-related markers were explored in this cross-sectional study of young adults (18–44 years) and middle-aged adults (45–65 years) from the United States using the National Health and Nutrition Examination Survey (NHANES), 2009–2016. Degrees of exposure were created using blood lead level (BLL) as the biomarker of exposure based on the epidemiologically relevant threshold of BLL > 5 μg/dL. The mean values, in addition to the percentages of people represented for the markers of interest (systolic blood pressure [SBP], diastolic blood pressure [DBP], gamma-glutamyl transferase [GGT], non-high-density lipoprotein cholesterol [non-HDL-C]) were explored. Among those exposed to lead, the likelihood of elevated clinical markers (as defined by clinically relevant thresholds of above normal) were examined using binary logistic regression. In exploring exposure at the 5 μg/dL levels, there were significant differences in all the mean variables of interest between young and middle-aged adults. The binary logistic regression showed young and middle-aged adults exposed to lead were significantly more likely to have elevated markers (apart from DBP). In all, lead affects cardiovascular-related markers in young and middle-aged U.S. adults and thus we must continue to monitor lead exposure to promote health.

Correlates of Perceived HIV Prevalence and Associations With HIV Testing Behavior Among Men Who Have Sex With Men in the United States

2014 ◽  
Vol 10 (2) ◽  
pp. 90-99 ◽  
Author(s):  
Darcy White ◽  
Rob Stephenson
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Hiv Testing ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
The United States ◽  
Hiv Prevalence ◽  
Logistic Regression Models ◽  
Sex With Men ◽  
Hiv Testing Behavior

As the rate of HIV infection continues to rise among men who have sex with men (MSM) in the United States, a focus of current prevention efforts is to encourage frequent HIV testing. Although levels of lifetime testing are high, low levels of routine testing among MSM are concerning. Using data from an online sample of 768 MSM, this article explores how perceptions of HIV prevalence are associated with HIV testing behavior. Ordinal logistic regression models were fitted to examine correlates of perceived prevalence, and binary logistic regression models were fitted to assess associations between perceived prevalence and HIV testing. The results indicate that perceptions of higher prevalence among more proximal reference groups such as friends and sex partners are associated with greater odds of HIV testing. Perceptions of HIV prevalence were nonuniform across the sample; these variations point to groups to target with strategic messaging and interventions to increase HIV testing among MSM.

scholarly journals Factors Predicting outcome of patients with polytrauma in tertiary center of western Nepal

2021 ◽  
Vol 2 (1) ◽  
pp. 13-18
Author(s):  
Pradeep Ghimire ◽  
Nikunja Yogi ◽  
Balgopal Karmacharya ◽  
Abhishek Poudel ◽  
Sushil Mishra
Keyword(s):  
Logistic Regression ◽  
Injury Severity Score ◽  
Severity Score ◽  
Injury Severity ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
Public Health Issue ◽  
P Value ◽  
Trauma Population ◽  
Tertiary Center

 Introduction: Trauma is a public health issue associated with substantial socioeconomic impacts and major adverse clinical outcomes. No single study has previously investigated the predictors of mortality in a general trauma population. In this study, we assessed different clinico-biochemical parameters to investigate the associations between those parameters and their effects in outcome of a polytrauma patient. Methods: An analytical study was done in between January 2020 to December 2020 in patients with polytrauma admitted to intensive care unit Department of Surgery in Manipal Teaching Hospital to assess the effect of various socio-demographic and clinic-radiologic variables in outcome (Glasgow outcome scale) of polytrauma patients. All the categorical data were tested using chi square test or Fischer Exact test and continuous variables were tested using student’s “t” test. P value <0.05 was determined significant. Those independent variables significant on univariate analysis were then subjected to binary logistic regression and the data was presented as level of significance, odds ratio and 95% confidence interval. Analysis was done using SPSS 23.0. Results: Out of 67 patients, 34 had favorable GOS and 33 had unfavorable GOS. Injury Severity Score (ISS) (P<0.01), abnormal pupils (P<0.01), RBS (0.04), low GCS during presentation (<0.01), higher CT Marshal Grade (0.01) had strong associations with unfavorable outcome in polytraumatic patient. ISS was the only significant parameter when all the other significant variables were kept constant in binary logistic regression model (OR=1.18, 95% CI=1.08-1.28). Conclusion: Injury Severity Score, abnormal pupils during presentation, high level of blood sugar after polytrauma, low GCS during presentation, higher CT Marshal Grade are strong predictors in outcomes of polytraumatic patient.

scholarly journals Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel

2020 ◽  
Vol 17 (9) ◽  
pp. 3155 ◽  
Author(s):  
Shengdi Chen ◽  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu
Keyword(s):  
Environmental Factor ◽  
High Speed ◽  
Injury Severity ◽  
Control Measures ◽  
Crash Severity ◽  
Contributing Factors ◽  
Safety Control ◽  
Late Night ◽  
Single Vehicle ◽  
The Impact

The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00–06:59) and afternoon rushing hours (16:30–18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures.

Examining the Characteristics That Differentiate Jihadi-Associated Cyberattacks Using Routine Activities Theory

2021 ◽  
pp. 089443932110233
Author(s):  
Thomas J. Holt ◽  
Noah D. Turner ◽  
Joshua D. Freilich ◽  
Steven M. Chermak
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
The United States ◽  
Routine Activities ◽  
Routine Activities Theory ◽  
Future Research ◽  
Binary Logistic Regression Model

This study applies routine activities theory to determine whether the characteristics of jihadi-inspired web defacements in the United States vary from all other defacements performed against IP addresses hosted within the United States from 2012 to 2016. We focus on target suitability variables and use a sample of over 2.2 million defacements reported by the independent website Zone-H. We estimated a binary logistic regression model and found that jihadi cyberattacks were rare among all the defacements performed in this 5-year period. Additionally, these findings demonstrated jihadists were more likely to target organizational websites and utilized specific attack methods compared to all other defacers. We contextualize our findings and outline a number of avenues for future research.

scholarly journals Analysis of Intersection Traffic Safety in the City of San Antonio, 2013–2017

2021 ◽  
Vol 13 (9) ◽  
pp. 5296
Author(s):  
Khondoker Billah ◽  
Qasim Adegbite ◽  
Hatim O. Sharif ◽  
Samer Dessouky ◽  
Lauren Simcic
Keyword(s):  
High Risk ◽  
Traffic Safety ◽  
Traffic Control ◽  
Injury Severity ◽  
San Antonio ◽  
Binary Logistic Regression ◽  
Bivariate Analysis ◽  
Crash Severity ◽  
Contributing Factors ◽  
Late Night

An understanding of the contributing factors to severe intersection crashes is crucial for developing countermeasures to reduce crash numbers and severity at high-risk crash locations. This study examined the variables affecting crash incidence and crash severity at intersections in San Antonio over a five-year period (2013–2017) and identified high-risk locations based on crash frequency and injury severity using data from the Texas Crash Record and Information System database. Bivariate analysis and binary logistic regression, along with respective odds ratios, were used to identify the most significant variables contributing to severe intersection crashes by quantifying their association with crash severity. Intersection crashes were predominantly clustered in the downtown area with relatively less severe crashes. Males and older drivers, weekend driving, nighttime driving, dark lighting conditions, grade and hillcrest road alignment, and crosswalk, divider and marked lanes used as traffic control significantly increased crash severity risk at intersections. Prioritizing resource allocation to high-risk intersections, separating bicycle lanes and sidewalks from the roadway, improving lighting facilities, increasing law enforcement activity during the late night hours of weekend, and introducing roundabouts at intersections with stops and signals as traffic controls are recommended countermeasures.

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