Street-view imagery (SVI) offers a fine-grained lens on traffic risk, yet two fundamental challenges persist: (1) how to construct street-level indicators that capture accident-related features, and (2) how to quantify their causal impacts across different accident types. To address these challenges, we propose Semantic4Safety, a framework that applies zero-shot semantic segmentation to SVIs to derive 11 interpretable streetscape indicators, and integrates road type as contextual information to analyze approximately 30,000 accident records in Austin. Specifically, we train an eXtreme Gradient Boosting (XGBoost) multi-class classifier and use Shapley Additive Explanations (SHAP) to interpret both global and local feature contributions, and then apply Generalized Propensity Score (GPS) weighting and Average Treatment Effect (ATE) estimation to control confounding and quantify causal effects. Results uncover heterogeneous, accident-type-specific causal patterns: features capturing scene complexity, exposure, and roadway geometry dominate predictive power; larger drivable area and emergency space reduce risk, whereas excessive visual openness can increase it. By bridging predictive modeling with causal inference, Semantic4Safety supports targeted interventions and high-risk corridor diagnosis, offering a scalable, data-informed tool for urban road safety planning.

翻译：街景图像为交通风险提供了细粒度视角，但两大基础挑战依然存在：(1)如何构建能捕捉事故相关特征的街道级指标，(2)如何量化这些指标对不同事故类型的因果影响。为应对这些挑战，我们提出Semantic4Safety框架，该框架对街景图像应用零样本语义分割以提取11个可解释的街景指标，并整合道路类型作为上下文信息，用以分析奥斯汀市约30,000条事故记录。具体而言，我们训练了一个极限梯度提升多分类器，并采用沙普利加性解释方法解析全局与局部特征贡献，随后应用广义倾向得分加权与平均处理效应估计来控制混杂因素并量化因果效应。研究结果揭示了异质性、事故类型特异性的因果模式：表征场景复杂性、暴露度与道路几何形态的特征主导预测能力；更大的可行驶区域与应急空间可降低风险，而过度的视觉开放性则可能增加风险。通过将预测建模与因果推断相结合，Semantic4Safety支持针对性干预措施与高风险路段诊断，为城市道路安全规划提供了一个可扩展的数据驱动工具。