...
首页> 外文期刊>American Journal of Theoretical and Applied Statistics >Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach
【24h】

Modeling Road Traffic Accident Injuries in Nairobi County: Model Comparison Approach

机译:内罗毕县道路交通事故伤害建模:模型比较法

获取原文
           

摘要

Road Traffic Accident (RTA) injuries, is a neglected cause of death and disability in Nairobi County. Nairobi County has the highest number of injury rates in Kenya, notably in the active age group of (15-29) years that constitutes approximately 40% of its population. This signifies the importance of properly analyzing traffic accident data and predicting injuries, not only to explore the underlying causes of RTA injuries but also to initiate appropriate safety and policy measures in the County. Thus the study modeled RTA injuries that occurred from 2002 to 2014 in Nairobi County using the Artificial Neural Networks (ANN). ANN is a powerful technique that has demonstrated considerable success in analyzing historical data to predict future trends. However the use of ANN in accidents analysis was found to be relatively new and rare and thus the negative binomial regression approach was utilized as the study's baseline model. The empirical study results indicated that the ANN model outperformed the negative binomial model in its overall performance.
机译:道路交通事故(RTA)受伤是内罗毕县被忽略的死亡和残疾原因。内罗毕县受伤率最高,在肯尼亚,尤其是在(15-29)岁年龄段的活跃年龄组中,约占该国人口的40%。这表明正确分析交通事故数据和预测伤害的重要性,不仅是为了探索造成RTA伤害的根本原因,而且要在该县采取适当的安全和政策措施。因此,该研究使用人工神经网络(ANN)对内罗毕县2002年至2014年发生的RTA伤害进行了建模。人工神经网络是一项功能强大的技术,已在分析历史数据以预测未来趋势方面取得了相当大的成功。然而,在事故分析中使用人工神经网络是相对较新且很少见的,因此使用负二项式回归方法作为研究的基准模型。实证研究结果表明,人工神经网络模型的整体性能优于负二项式模型。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号