Abstract
Crash scene reconstruction is essential for reverse-engineering the factors of a crash scene to determine the cause of a crash. It requires automated information extraction (IE) from the textual crash report narratives and their formalization in a computer-processable representation. Natural language processing (NLP) is a powerful computational tool to process texts. This paper presents a dependency parsing (DP)-based NLP system for automated IE of crash events information from crash report narratives. DP-based rules and patterns were leveraged for defining relations between subjects and objects to support the IE algorithm. The proposed IE system was tested on 50 reports collected from the Southeast Michigan Council of Governments (SEMCOG) traffic crash database, which achieved an overall 94.9% precision, 90.2% recall, and 92.5% F1-score. A parallel experiment was conducted with ChatGPT to extract information from crash narratives, where an 88.0% precision and 88.0% recall were obtained.
| Original language | English |
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| Title of host publication | Computing in Civil Engineering 2023 |
| Subtitle of host publication | Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023 |
| Editors | Yelda Turkan, Joseph Louis, Fernanda Leite, Semiha Ergan |
| Publisher | American Society of Civil Engineers (ASCE) |
| Pages | 249-256 |
| Number of pages | 8 |
| ISBN (Electronic) | 9780784485224 |
| DOIs | |
| State | Published - 2024 |
| Event | ASCE International Conference on Computing in Civil Engineering 2023: Data, Sensing, and Analytics, i3CE 2023 - Corvallis, United States Duration: Jun 25 2023 → Jun 28 2023 |
Publication series
| Name | Computing in Civil Engineering 2023: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2023 |
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Conference
| Conference | ASCE International Conference on Computing in Civil Engineering 2023: Data, Sensing, and Analytics, i3CE 2023 |
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| Country/Territory | United States |
| City | Corvallis |
| Period | 06/25/23 → 06/28/23 |
Funding
The authors would like to thank the National Science Foundation (NSF). This material is based on work supported by the NSF under Grant No. 2121967. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.