scholarly journals An examination of accident severity differences between male and female drivers, Using Logistic Regression Model

2015 ◽  
Vol 1 (1) ◽  
pp. 31-36
Author(s):  
Alireza Pakgohar ◽  
Mojtaba Kazemi
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Accuracy Rate ◽  
Model Accuracy ◽  
Male And Female ◽  
Accident Severity ◽  
Probability Of Death

One person in every 2539 people gets killed and one in every 253 suffers injuries due to driving crashes each year in Iran. Such that driving incidents are second rank factor of death and the first rank reason for lost lifetimes in this country. 60% of total incidents which lead to deaths or injuries are actually driving incidents in Iran. That is while the same ratio is only 25% worldwide average. In this article, we report a probabilistic relationship between vehicle drivers’ gender and severity of the accidents. The model accuracy rate is more than 91%. Coefficient values show that if an crash happens and all other variables are under control, the probability of suffering injuries for a man is 1.597 times more than for a woman (1.40 – 1.79, 99% CI) in comparison with the case that the person does not get injured at all. Similarly, the probability of death for a man is 1.462 times higher than for a woman (1.13-1.79, 90% CI) again in comparison with case of no injury at all.

scholarly journals Analysis of Traffic Accident Severity at Intersection Using Logistic Regression Model

2020 ◽  
pp. 1-9
Author(s):  
Yao Tzu Hsu ◽  
Shun Chi Chang ◽  
Tzu Hsin Hsu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Traffic Safety ◽  
Logistic Regression Model ◽  
Surface Condition ◽  
Confusion Matrix ◽  
Weather Condition ◽  
Road Surface ◽  
Vehicle Type ◽  
Accident Severity

Accident severity analysis is an important issue in the field of traffic safety study, and intersections are also locations of relatively high accident rates in the roadway network. Therefore, the main purpose of this study is to establish a prediction model of intersection severity based on the binary logistic regression model of data mining technology. The data source of intersection accident is obtained from the Taichung City Police Department in Taiwan in 2018 and there are 27461 valid samples. The dependent variable is the severity of intersection accident. The independent variables include 9 variables such as month, time of accident, weather condition, light conditions, road type, road surface condition, traffic control type, accident type and vehicle type, and are analyzed by the forward selection (Wald). The research results show that time of accident, road surface condition, accident type and vehicle type have significant effects. The confusion matrix is used to verify the reliability of the model, and the results can be used as the references for reducing the degree of accident injury at the intersection in the future.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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