Although existing machine learning-based methods for traffic accident analysis can provide good quality results to downstream tasks, they lack interpretability which is crucial for this critical problem. This paper proposes an interpretable framework based on Bayesian Networks for traffic accident prediction. To enable the ease of interpretability, we design a dataset construction pipeline to feed the traffic data into the framework while retaining the essential traffic data information. With a concrete case study, our framework can derive a Bayesian Network from a dataset based on the causal relationships between weather and traffic events across the United States. Consequently, our framework enables the prediction of traffic accidents with competitive accuracy while examining how the probability of these events changes under different conditions, thus illustrating transparent relationships between traffic and weather events. Additionally, the visualization of the network simplifies the analysis of relationships between different variables, revealing the primary causes of traffic accidents and ultimately providing a valuable reference for reducing traffic accidents.

翻译：尽管现有的基于机器学习的事故分析方法能为下游任务提供优质结果，但缺乏可解释性，而这正是这一关键问题所亟需的。本文提出一个基于贝叶斯网络的可解释框架用于交通事故预测。为便于实现可解释性，我们设计了一个数据集构建流程，在保留关键交通数据信息的同时向框架输入数据。通过具体案例研究，我们的框架能够基于美国天气与交通事件之间的因果关系从数据集中推导出贝叶斯网络。最终，该框架在保持竞争性预测精度的同时，可检查不同条件下事件概率的变化情况，从而揭示交通与天气事件之间透明的关系。此外，网络的可视化简化了不同变量间关系的分析，有助于揭示交通事故的主要成因，最终为减少交通事故提供有价值的参考。