جستجوی مقالات مرتبط با کلیدواژه « احتمال وقوع تصادف » در نشریات گروه « عمران »

One of the vehicles on the city streets is a taxi, which is a supplement to public transportation and a part of an active transportation. Due to the fact that taxi drivers spend a longer time on the roads compared to other drivers, they have a higher probability of an accident; Therefore, safety and proper driving are important for taxi drivers. in this research, the influence of driver's behavior and characteristics on the probability of being involved in an accident and the rate of taxi accidents and presenting quantitative models for predicting the number of accidents and the probability of being involved in an accident have been investigated. For this purpose and by reviewing the literature on demographic characteristics, driving history and accidents of the last three years, driving behaviors and impulsivity characteristics were considered as independent variables, and to collect them, standard questionnaires were distributed to the target population, which were taxi drivers in Arak metropolis. In this regard, 330 random samples of taxi drivers completed the questionnaires. In the following, the reliability and validity of the questionnaires were verified and the logistic regression model was estimated to predict the probability of involvement in an accident and generalized Poisson models to predict the number of accidents as the best models using SPSS 24 software. The significant independent variables of the accident involvement equation include: age, number of violations, driver's hours of work, errors and slips, and interest in driving were significant. The influential variables in the model for predicting the number of accidents also included: age, error and slip, Disregarding the Regulation, interest in driving and history of driving a taxi. The results of this study can be used in designing training courses, evaluating taxi drivers' qualifications, and making cultural packages for these drivers.

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.

Transportation safety is the most important criterion in the engineering design of roads. Rural roads pedestrian accidents statistics show that this type of accident is tremendously important. From the safety point of view drivers need to have a reasonable time window for hazard recognition, reaction initiation and execution. This means that the hazard has to be in sight from a distance longer than the minimum stopping sight distance. Nevertheless, if the road side configuration make the sight available for a very long distance, it’s the hazards characteristics which defines the visibility along with the drivers’ behavioral characteristics as a human. The hazard color and mobility are some of the most important hazard characteristics. In this research using some variables of hazard characteristics, a linear accident probability model was developed and the effect of these characteristics and their interactions was investigated. The use of driving simulator was in order to prepare a ceteris paribus laboratory condition. Randomly invited, the test takers were 80 over 18-years-old person who had a driver license. The simulated environment was a straight rout in a plain terrain in which the driver’s sight were not obstructed. Drivers encountered averagely 14 hazards in this road including some pedestrian crossing the road with black or white out-fit and some black or white rocks in the road. The rocks were modeled to enable the comparison between the effects of state of being mobile or fixed on accident likelihood. According to the findings of this research, speed has an increasing effect and the number of prior facing with hazards had a decreasing effect on the accident likelihood, although this effect was not so considerable. The hazards including pedestrians and rocks with black color was about 26% more likely to be involved in an accident in night time in comparison with the white ones. This likelihood is estimated to be about 2% less in day time.