波哥大市中心道路交通事故的贝叶斯节点-边建模
Bayesian Node Edge Modeling of Road Crashes in Central Bogotá
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中文总结 AI 辅助
本研究针对波哥大市中心的道路事故数据，采用贝叶斯负二项节点-边模型分析路段与交叉口的事故影响因素，为城市事故分析提供了可解释基线，同时指出路段结果需谨慎解读。
中文摘要 AI 辅助
引言：道路路段和交叉口的事故计数呈现出不同的暴露度和连通性模式，常规分析可能会掩盖这些模式。方法：本研究针对波哥大六个中心区的8169条道路路段和8398个交叉口，拟合了贝叶斯负二项节点-边模型。不同的预测变量分别代表道路等级、路面状况、车速、信号设置、交叉口构型和土地利用处理。通过预测摘要和空间诊断检验模型性能，计算细节见附录。结果：至少有四条关联路段且最高关联车速更高的交叉口，预期事故计数更高；土地利用处理和信号接入强度也呈现出后验关联。道路等级和信号设置是最明确的路段层面因素，但该组成部分的原始尺度预测性能较弱，部分路面类别存在数值不确定性。结论：将交叉口和路段视为不同的网络元素，为城市事故分析提供了可解释的基线，但路段层面的结果需要谨慎解读，并为空间结构化扩展提供了动力。
英文摘要
Introduction: Crash counts on road segments and intersections exhibit differ- ent exposure and connectivity patterns that conventional analyses may obscure. Methodology: A Bayesian negative binomial node edge model was fitted to 8,169 road segments and 8,398 intersections in six central districts of Bogotá. Separate predictors represented road hierarchy, pavement, speed, signalization, intersection configuration, and land-use treatment. Model performance was examined through pre- dictive summaries and spatial diagnostics, while computational details are reported in the appendix. Results: Intersections with at least four incident segments and higher maximum incident speeds had higher expected crash counts. Land use treatment and signalized access intensity also showed posterior associations. Road hierarchy and signalization were the clearest segment-level factors; however, this component had weak raw scale predictive performance and numerical uncertainty for some pavement categories. Conclusion: Treating intersections and segments as distinct network elements provides an interpretable baseline for urban crash analysis, but the segment results require cautious interpretation and motivate spatially structured extensions.
