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The Importance of Exercise and General Mental Health on Prediction of Property-Damage-Only Accidents among Taxi Drivers in Tehran: A Study Using ANFIS-PSO and Regression Models

机译:运动和一般心理健康的重要性对德黑兰的出租车司机唯一的财产损失事故的预测:使用ANFIS-PSO和回归模型的研究

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摘要

The rate of traffic accidents in Iran is high, and the majority of the causes that must be investigated are human factors. The present study examined the effects of exercise and general health as human factors on the prediction of crash likelihood with the data collected from taxi drivers of Tehran. The data were collected using the general health questionnaire and a form entailing some items regarding the duration of daily exercise and sociodemographic information. The adaptive neurofuzzy inference system and particle swarm optimization (ANFIS-PSO) was used for tuning the parameters of membership function of the fuzzy model applied for this prediction. Thus system was compared with the more conventional methods, such as multiple regression and Poisson regression. To avoid the overfitting issue, the data were divided into 70% for training and 30% for validation. The root-mean-square error (RMSE) was also utilized as a determinant of goodness of fit between ANFIS-PSO and regression methods. The findings indicated that the number of minutes of daily exercise and mental health significantly influence property-damage-only (PDO) accidents of taxi drivers in Tehran, Iran. Furthermore, the results revealed that the hybrid model (ANFIS-PSO) not only had a better fit but also produced different results from those of the traditional regression models, which may be used in policymaking regarding the reduction of PDO accidents. Based on the results, performing daily exercise for more than 10 minutes would substantially reduce the PDO accidents among the taxi drivers in Tehran. The findings showed that ANFIS-PSO could be effectively implemented in the studies addressing accident frequency. Consequently, the policy makers should simply adopt some interventions to encourage the taxi drivers to perform daily exercise that not only improves their wellbeing but also reduces the risk of PDO accidents.
机译:伊朗的交通事故率很高,而且必须调查的大部分原因是人为因素。本研究审查了运动和一般健康作为人类因素对德黑兰的出租车司机收集的数据预测的人为因素的影响。使用一般健康调查问卷和旨在有些关于日常运动和社会渗目信息的持续时间的表格收集数据。自适应神经纤维义推理系统和粒子群优化(ANFIS-PSO)用于调整应用于该预测的模糊模型的隶属函数参数。因此,将系统与更常规的方法进行比较,例如多元回归和泊松回归。为避免过度装备问题,将数据分为70%以进行培训,验证30%。根均方误差(RMSE)也被用作符合ANFIS-PSO和回归方法的良好的决定因素。这些研究结果表明,日常运动和心理健康的分钟数大大影响了伊朗德黑兰的出租车司机的股权(PDO)事故。此外,结果表明,混合模型(ANFIS-PSO)不仅具有更好的拟合,而且还产生了传统回归模型的不同结果,这可以用于对PDO事故的减少的决策制作。根据结果​​,执行每日运动超过10分钟将大大减少德黑兰的出租车司机之间的PDO事故。研究结果表明,在解决事故频率的研究中可以有效地实施ANFIS-PSO。因此,政策制定者应该只是采取一些干预措施来鼓励出租车司机进行日常行使,这不仅可以提高他们的福祉,而且还降低了PDO事故的风险。

著录项

  • 来源
    《Journal of Advanced Transportation》 |2019年第3期|3852194.1-3852194.10|共10页
  • 作者单位

    KN Toosi Univ Technol Dept Civil Engn Tehran Iran;

    KN Toosi Univ Technol Dept Civil Engn Tehran Iran;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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