实时从新闻中自动填入铁路事故报告表，提升应急响应速度
A Real-Time System to Populate FRA Form 57 from News
- 用视觉语言模型将不规则表格转为结构化JSON，再分组问答还原信息
- 在自建数据集上实现92%的信息召回率，覆盖87%的报告字段
- 适合交通应急、公共安全领域快速响应场景，尤其适合灾后决策支持
铁路事故后，地方铁路委员会需及时获取态势信息，但官方联邦铁路管理局（FRA）调查通常需数天至数周。我们提出一个演示系统，可实时从新闻中自动填充高速公路铁路平交道口事故数据表（Form 57）。该方法解决两大挑战：表格布局不规则且语义密集，新闻内容噪声大。首先，利用视觉语言模型结合样本聚合，将Form 57转换为JSON模式；其次，基于表格布局意图进行分组问题回答，降低歧义。我们通过爬取新闻并关联官方FRA记录构建评估数据集，并标注可提取信息。实验对比多种方案，在信息检索准确率和字段覆盖率方面表现优异。
原文摘要 · Abstract (English)
Local railway committees need timely situational awareness after highway-rail grade crossing incidents, yet official Federal Railroad Administration (FRA) investigations can take days to weeks. We present a demo system that populates Highway-Rail Grade Crossing Incident Data (Form 57) from news in real time. Our approach addresses two core challenges: the form is visually irregular and semantically dense, and news is noisy. To solve these problems, we design a pipeline that first converts Form 57 into a JSON schema using a vision language model with sample aggregation, and then performs grouped question answering following the intent of the form layout to reduce ambiguity. In addition, we build an evaluation dataset by aligning scraped news articles with official FRA records and annotating retrievable information. We then assess our system against various alternatives in terms of information retrieval accuracy and coverage.
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