在城市道路网络上交通事故发生率的时空空间建模,基于明确的网络三角化
Somnath Chaudhuri1, Pablo Juan1,2, Jorge Mateu3
1Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain.
Journal of applied statistics
|November 16, 2023
概括
这项研究引入了一种新的时空模型,用于预测城市地区的道路交通事故. 开发的风险地图有助于确定更安全的路线,并加强道路安全措施.
科学领域:
- 公共卫生 公共卫生
- 运输科学 运输科学
- 地理空间分析是什么
背景情况:
- 道路交通事故给全球公共卫生带来了重大挑战.
- 了解事故的空间和时间模式对于预防至关重要.
研究的目的:
- 开发一个时空模型,用于预测城市道路段上的交通碰撞.
- 为安全分析生成道路网络的全面风险地图.
主要方法:
- 使用贝叶斯方法与集成嵌套拉普拉斯近似 (INLA).
- 使用随机局部微分方程 (SPDE) 进行空间建模.
- 引入了SPDE网络三角测量,用于线性网络空间自相对应估计.
主要成果:
- 该模型有效地预测了道路段的交通事故发生率.
- 生成了详细的风险地图,突出显示事故热点和安全区.
- 证明了网络限制空间分析的实用性.
结论:
- 拟议的时空模型为城市道路安全提供了一个强大的工具.
- 风险地图可以指导事故预防策略和基础设施改进.
- 该方法提升了道路网络空间依赖性的分析.
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