用可解释方法分析路口事故严重程度，找出高风险组合条件。
A Dimensionality-Reduced XAI Framework for Roundabout Crash Severity Insights
- 先聚类找事故共现模式，再用树模型结合SHAP解释伤害原因。
- 黑暗+湿滑路面+高速度时事故更严重，低速清晰时较轻。
- 揭示入口、环道、减速区不同场景下的具体致因机制。
圆环路口可降低严重事故，但风险模式受条件影响。本研究基于2017-2021年俄亥俄州圆环路口事故数据，采用两步可解释分析流程。首先通过聚类对应分析（CCA）识别共现因素，得到四种事故模式；随后使用基于树的严重性模型，结合SHAP值量化各类模式下伤害的驱动因素。结果表明：当黑暗、湿滑路面与高限速同时出现，且伴随固定物碰撞或角度碰撞时，事故严重性更高；在晴朗、低速环境下则较轻。模式特异性解释揭示了入口处（未让行、间隙判断失误）、多车道环道内（操作不当）及减速过程中（追尾）的具体机制。该流程将模式发现与个案解释结合，支持选址筛查、对策选择与审计合规报告。对信息系统领域贡献在于提供公共安全分析中可用的可解释人工智能实用模板。
原文摘要 · Abstract (English)
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.
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