Analysis of Railway Accidents' Narratives Using Deep Learning Article Swipe
Mojtaba Heidarysafa
,
Kamran Kowsari
,
Laura E. Barnes
,
Donald E. Brown
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/icmla.2018.00235
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/icmla.2018.00235
Automatic understanding of domain specific texts in order to extract useful\nrelationships for later use is a non-trivial task. One such relationship would\nbe between railroad accidents' causes and their correspondent descriptions in\nreports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B.\nRailroads involved in accidents are required to submit an accident report to\nthe Federal Railroad Administration (FRA). These reports contain a variety of\nfixed field entries including primary cause of the accidents (a coded variable\nwith 389 values) as well as a narrative field which is a short text description\nof the accident. Although these narratives provide more information than a\nfixed field entry, the terminologies used in these reports are not easy to\nunderstand by a non-expert reader. Therefore, providing an assisting method to\nfill in the primary cause from such domain specific texts(narratives) would\nhelp to label the accidents with more accuracy. Another important question for\ntransportation safety is whether the reported accident cause is consistent with\nnarrative description. To address these questions, we applied deep learning\nmethods together with powerful word embeddings such as Word2Vec and GloVe to\nclassify accident cause values for the primary cause field using the text in\nthe narratives. The results show that such approaches can both accurately\nclassify accident causes based on report narratives and find important\ninconsistencies in accident reporting.\n
Related Topics
Concepts
Narrative
Word2vec
Accident (philosophy)
Computer science
Variety (cybernetics)
Task (project management)
Field (mathematics)
Domain (mathematical analysis)
Computer security
Artificial intelligence
Natural language processing
Engineering
Linguistics
Systems engineering
Mathematical analysis
Pure mathematics
Embedding
Philosophy
Mathematics
Epistemology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icmla.2018.00235
- OA Status
- green
- Cited By
- 58
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2896967004
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2896967004Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icmla.2018.00235Digital Object Identifier
- Title
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Analysis of Railway Accidents' Narratives Using Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-12-01Full publication date if available
- Authors
-
Mojtaba Heidarysafa, Kamran Kowsari, Laura E. Barnes, Donald E. BrownList of authors in order
- Landing page
-
https://doi.org/10.1109/icmla.2018.00235Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1810.07382Direct OA link when available
- Concepts
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Narrative, Word2vec, Accident (philosophy), Computer science, Variety (cybernetics), Task (project management), Field (mathematics), Domain (mathematical analysis), Computer security, Artificial intelligence, Natural language processing, Engineering, Linguistics, Systems engineering, Mathematical analysis, Pure mathematics, Embedding, Philosophy, Mathematics, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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58Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 7, 2023: 5, 2022: 13, 2021: 12Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2158728425, https://openalex.org/W2287139952, https://openalex.org/W2919115771, https://openalex.org/W6685053522, https://openalex.org/W2120615054, https://openalex.org/W2144012961, https://openalex.org/W2250966211, https://openalex.org/W6693505360, https://openalex.org/W2470673105, https://openalex.org/W2144211451, https://openalex.org/W2107878631, https://openalex.org/W1977562426, https://openalex.org/W2084085693, https://openalex.org/W6638545294, https://openalex.org/W1967967614, https://openalex.org/W1933479155, https://openalex.org/W571782140, https://openalex.org/W6910387809, https://openalex.org/W2104555393, https://openalex.org/W2759474451, https://openalex.org/W6605424934, https://openalex.org/W2250539671, https://openalex.org/W2112796928, https://openalex.org/W6637242042, https://openalex.org/W1523493493, https://openalex.org/W2170240176, https://openalex.org/W1614298861, https://openalex.org/W2157331557, https://openalex.org/W1841724727, https://openalex.org/W3102058420, https://openalex.org/W2888503998, https://openalex.org/W2963921497, https://openalex.org/W1665214252, https://openalex.org/W2963012544, https://openalex.org/W1815076433, https://openalex.org/W2265846598, https://openalex.org/W1832693441, https://openalex.org/W2949541494, https://openalex.org/W2802022891, https://openalex.org/W132962688, https://openalex.org/W2950577311 |
| referenced_works_count | 41 |
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