预测印度的道路事故死亡人数:ARIMA和指数式光滑方法之间的明确比较
Prafulla Kumar Swain1, Manas Ranjan Tripathy2, Khushi Agrawal1
1Department of Statistics, Utkal University, Bhubaneswar, Odisha, India.
概括
印度的道路事故死亡人数正在上升. 这项研究使用自行回归集成移动平均线 (ARIMA) 和指数级平滑预测了从2022-2031年的上升趋势,ARIMA (2,2,2) 被确定为最佳模型.
科学领域:
- 公共卫生 公共卫生
- 运输安全运输安全
- 数据科学数据科学数据科学
背景情况:
- 道路交通事故死亡人数代表着日益增长的全球关注,由于越来越多的机动化,这对印度产生了重大影响.
- 现有的文献强调了印度道路交通伤害和死亡的严重程度.
研究的目的:
- 预测2022-2031年期间在印度发生的道路交通事故死亡人数.
- 为了比较自行回归集成移动平均线 (ARIMA) 和指数级平滑方法的有效性,用于对道路交通事故数据的时间序列预测.
- 确定最适合的时间序列模型来预测印度未来的道路交通事故死亡人数.
主要方法:
- 使用的时间序列分析技术:自回归集成移动平均 (ARIMA) 和指数平滑.
- 收集的数据来自印度道路运输和公路部 (2020) 和印度意外死亡和自杀 (ADSI) 报告 (2021).
- 评估和比较ARIMA (2,2,2) 和指数平滑 (M,A,N) 模型,根据AIC和BIC值选择最好的模型.
主要成果:
- 无论是ARIMA还是指数平滑模型都显示出一致的结果,与现有研究保持一致.
- 在ARIMA (2,2,2) 模型被认为是优越的,因为 Akaike信息标准 (AIC) 和贝叶斯信息标准 (BIC) 值较低.
- 预计在未来十年,印度的道路交通事故死亡人数将大幅上升.
结论:
- ARIMA (2,2,2) 模型提供了一种可靠的方法来预测印度的道路交通事故死亡人数.
- 该研究预测,从2022年到2031年,印度的道路交通事故死亡人数将继续增加.
- 时间序列模型的比较分析为道路安全政策和干预策略提供了宝贵的见解.
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