Sayed Amin Nemati; Reza Amin; Ali Khodaii
Abstract
The present study focuses on the binary logit model for predicting the probability of driver fatalities in road accidents. The study utilizes data from road traffic accidents in Canada. These data were collected by the highway police in 2019 and recorded in the National Collison Database (NCBD). The ...
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The present study focuses on the binary logit model for predicting the probability of driver fatalities in road accidents. The study utilizes data from road traffic accidents in Canada. These data were collected by the highway police in 2019 and recorded in the National Collison Database (NCBD). The dependent variable in this model is the severity of accidents, which is a binary variable representing driver fatalities and injuries. The independent variables include vehicle types, vehicle age, days of the week, time intervals, same-direction and opposite-direction collisions, intersections, weather conditions, driver age, and gender. By analyzing the data and estimating the parameters, the model can predict up to 41% of the variations in the dependent variable. In the model validation stage, the data were divided into two parts, with 70% used for modeling and 30% for validation. McFadden's pseudo were used to evaluate the model's performance. The models were constructed using SPSS and Nlogit6.0 software. The results demonstrate that the model fits well with the data and has the capability to predict changes in accident severity. Consequently, the study indicates that variables such as road dryness, midnight time interval, and vehicle age contribute to an increase in driver fatalities, while variables such as light duty vehicles, school buses, and same-direction collisions contribute to a reduction in fatalities.
seyed reza shafaii amlashi; Reza Amin; Ali Khodaii
Abstract
Accidents in Iran pose a significant public health burden, requiring effective prevention and management strategies. Latent variable modelling provides a promising approach to understanding the complex factors that contribute to accidents in Iran. This paper reviews the current state of knowledge of ...
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Accidents in Iran pose a significant public health burden, requiring effective prevention and management strategies. Latent variable modelling provides a promising approach to understanding the complex factors that contribute to accidents in Iran. This paper reviews the current state of knowledge of latent variable modelling in Iran's accidents. The paper provides a synthesis of the literature on the measurement and modelling of latent variables in accidents, including human factors, environmental factors, and organizational factors, and also reviews the common statistical techniques used in the analysis of latent variables, such as structural equation modelling, latent class analysis, and factor analysis. The strengths and limitations of these approaches are discussed. The review shows that although some studies have applied latent variable modelling to accidents in Iran, the use of these techniques is still relatively limited in comparison to other countries. Overall, It has been argued that latent variable modelling can offer valuable insights into the underlying mechanisms of accidents in Iran and guide more effective strategies for prevention and management. This study used data from accidents resulting in the death or injury of passengers gathered on Iranian roadways in 2015, and the latent class model was fitted to this data using the NLOGIT6 software. And the findings have been reported in this article. The findings suggest that elements such as the season and day of the accident, as well as weather conditions, have an impact on the severity of accidents.
