论文标题
考虑空间网络晶格的汽车崩溃数据的多元分层分析
Multivariate hierarchical analysis of car crashes data considering a spatial network lattice
论文作者
论文摘要
道路交通伤亡是一种隐藏的全球流行病，要求基于证据的干预措施。本文展示了一种网络晶格方法，该方法是根据一个主要城市（英国利兹）的案例研究来识别特定关注的道路细分市场，其中在八年（2011年至2018年）中记录了5,862例不同严重性的撞车事故。我们考虑一个包括空间结构和非结构化随机效应的贝叶斯分层模型家族，以捕获严重程度之间的依赖性。结果突出了与城市中心西北部和南部相对于估计交通量的估计交通量更容易碰撞的道路。我们分析了可修改的面积单位问题（MAUP），提出了一种新的程序，以研究网络晶格上的MOUP的存在。我们得出的结论是，我们的方法可以对道路安全水平进行可靠的估算，以帮助识别道路网络上的“热点”并为有效的当地干预提供信息。
Road traffic casualties represent a hidden global epidemic, demanding evidence-based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a case study of a major city (Leeds, UK), in which 5,862 crashes of different severities were recorded over an eight-year period (2011-2018). We consider a family of Bayesian hierarchical models that include spatially structured and unstructured random effects, to capture the dependencies between the severity levels. Results highlight roads that are more prone to collisions, relative to estimated traffic volumes, in the northwest and south of city-centre. We analyse the Modifiable Areal Unit Problem (MAUP), proposing a novel procedure to investigate the presence of MAUP on a network lattice. We conclude that our methods enable a reliable estimation of road safety levels to help identify "hotspots" on the road network and to inform effective local interventions.
