基于时间序列的道路交通事故预测,通过SARIMA和Facebook Prophet模型预测潜在的变化点
Edmund F Agyemang1,2,3, Joseph A Mensah3, Eric Ocran1
1Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Ghana.
Heliyon
|December 11, 2023
概括
这项研究比较了SARIMA和Facebook Prophet模型,用于预测加纳的道路交通事故. 萨里马模型在预测事故趋势方面表现出卓越的准确性,表现优于先知模型.
科学领域:
- 公共卫生 公共卫生
- 运输科学 运输科学
- 数据科学数据科学数据科学
背景情况:
- 道路交通事故 (RTA) 构成了全球重大公共卫生挑战,特别是在发展中国家.
- 准确分析和预测RTA趋势对于制定有效的道路安全政策至关重要.
- 像加纳的MTTU这样的机构使用的现有统计方法可能无法完全捕捉RTA数据中的季节性模式.
研究的目的:
- 评估单变季节自回归集成移动平均 (SARIMA) 和Facebook (FB) Prophet模型对时间序列RTA数据的有效性.
- 将SARIMA和FB Prophet模型的预测准确度与传统的统计方法进行比较.
- 确定一个可靠的模型,用于预测加纳季节性模式的RTA.
主要方法:
- 训练SARIMA和FB Prophet模型,使用2013-2018年每月的RTA数据.
- 在2019年测试数据集上评估了模型的预测准确性.
- 在SARIMA模型开发和验证中采用Box-Jenkins方法,包括Ljung-Box和Box-Pierce测试.
主要成果:
- 萨里马模型实现了93.1025%的预测准确度 (MAPE:6.8975%,泰尔U1:0.0376).
- 该模型实现了84.3569%的预测准确率 (Theil U1: 0.1071).
- 剩余分析证实SARIMA模型的剩余值是独立的,没有自动/序列相关性,表明模型的充分性.
结论:
- 在预测加纳的每月RTA方面,SARIMA模型显著优于FB Prophet模型.
- 拟议的SARIMA模型在分析和预测RTA趋势方面表现出高准确度和可靠性.
- 该研究建议采用开发的SARIMA模型用于加纳的RTA分析,以加强道路安全战略.
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