A Real-Time System to Populate FRA Form 57 from News
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911340757516288 |
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| author | Lim, Chansong Shahgir, Haz Sameen Dong, Yue Chen, Jia Papalexakis, Evangelos E. |
| author_facet | Lim, Chansong Shahgir, Haz Sameen Dong, Yue Chen, Jia Papalexakis, Evangelos E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22457 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Real-Time System to Populate FRA Form 57 from News Lim, Chansong Shahgir, Haz Sameen Dong, Yue Chen, Jia Papalexakis, Evangelos E. Information Retrieval 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. |
| title | A Real-Time System to Populate FRA Form 57 from News |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2512.22457 |