Title: A Dimensionality-Reduced XAI Framework for Roundabout Crash Severity Insights Show Chinese title

Title: 一种用于环形交叉口碰撞严重性洞察的降维XAI框架

Abstract: Roundabouts reduce severe crashes, yet risk patterns vary by conditions. This study analyzes 2017-2021 Ohio roundabout crashes using a two-step, explainable workflow. Cluster Correspondence Analysis (CCA) identifies co-occurring factors and yields four crash patterns. A tree-based severity model is then interpreted with SHAP to quantify drivers of injury within and across patterns. Results show higher severity when darkness, wet surfaces, and higher posted speeds coincide with fixed-object or angle events, and lower severity in clear, low-speed settings. Pattern-specific explanations highlight mechanisms at entries (fail-to-yield, gap acceptance), within multi-lane circulation (improper maneuvers), and during slow-downs (rear-end). The workflow links pattern discovery with case-level explanations, supporting site screening, countermeasure selection, and audit-ready reporting. The contribution to Information Systems is a practical template for usable XAI in public safety analytics. Show Chinese abstract

Abstract: 环岛减少严重碰撞，但风险模式因条件而异。 本研究使用一种两步可解释的工作流程分析2017-2021年俄亥俄州环岛碰撞事件。 聚类对应分析（CCA）识别共同因素并得出四种碰撞模式。 然后使用SHAP对基于树的严重性模型进行解释，以量化不同模式内和跨模式的伤害驱动因素。 结果表明，当黑暗、湿滑表面和较高的限速与固定物体或角度事件同时发生时，严重性较高，而在清晰、低速环境下严重性较低。 模式特定的解释突出了入口处的机制（未让行、间隙接受）、多车道循环内部的不当操作以及减速时的追尾情况。 该工作流程将模式发现与案例级解释联系起来，支持地点筛选、应对措施选择和审计就绪的报告。 对信息系统领域的贡献是为公共安全分析中的可用XAI提供了一个实用模板。