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Investigating the relationship between driver's ticket frequency and demographic, behavioral, and personal factors: Which drivers commit more offenses?

机译:调查驾驶票频和人口统计,行为和个人因素之间的关系:司机犯下更多罪行?

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The present study investigated the relationship between offenses of drivers in terms of number of tickets and their demographic, behavioral, and personality factors. The researchers had the aim of identifying significant contributing predictors of being ticketed and comparing the relationship between aberrant driving behaviors and tickets for those identified as offending. Ticket frequency models were developed to estimate the drivers' offenses, potential for improvement method was applied to screen out offending drivers and correlation analysis was used to figure out the probability of being ticketed with inclination to commit aberrant driving behavior. A sample including 1,762 drivers was collected. The Iranian drivers responded to a questionnaire aimed at compiling various measures of personality type, aberrant driving behaviors, and demographic and ticket history information. The sample consisted of 78% male and 22% female with a total mean age of 35.6 (SD = 11.987) years. Six models with various independent variables were developed using generalized linear modeling (GLM) approach with a negative binomial error structure. The results indicated that different combinations of variables such as education level, car price, marital status, age, personality type, income level, place of residence, driving experience and exposure and gender in particular influence driving tickets. In the screening procedure, 620 drivers were identified as offending drivers. Comparison analyses between aberrant driving behaviors evidenced that lapses, errors, ordinary and aggressive violations are significantly different for offending drivers at 5% significant level. A correlation comparison between offending and nonoffending drivers revealed that ordinary violations play a more important role than the others on being ticketed.
机译:本研究调查了驾驶员罪行之间的关系,票数及其人口统计,行为和人格因素。研究人员的目的是识别正在票据的重要促使预测因子,并比较了异常驾驶行为和票据的关系,为那些被确定为违规的人。开发票证频率模型以估算驾驶员的违规,应用改进方法的潜力用于筛选冒犯驾驶员和相关性分析来弄清楚倾向于倾向于提出异常驾驶行为的概率。收集了包括1,762个司机的样品。伊朗司机响应了一个调查问卷,旨在编制各种人格类型,异常驾驶行为和人口统计和门票历史信息的措施。该样品由78%的男性和22%女性组成,总平均年龄为35.6(SD = 11.987)岁。使用具有负二项式误差结构的广义线性建模(GLM)方法,开发了具有各种独立变量的六种模型。结果表明,教育水平,汽车价格,婚姻状况,年龄,人格,收入水平,居住地,驾驶经验和暴露和性别,特别是影响驾驶票的不同组合。在筛选程序中,将620个驱动程序确定为违规驱动因素。异常驾驶行为之间的比较分析证明了停滞,错误,普通和攻击性违规,对于违规驾驶员显着差异,在5%显着水平下造成驾驶员。违规和非责任司机之间的相关比较显示普通违规行为比其他人在被票务中发挥更重要的作用。

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