Dependency Parsing-Based Information Extraction from Car Crash Narratives to Support Crash Scene Reconstruction
Publication: Computing in Civil Engineering 2023
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.
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Published In
Computing in Civil Engineering 2023
Pages: 249 - 256
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Algorithms
- Automation and robotics
- Automobiles
- Business management
- Computing in civil engineering
- Construction engineering
- Construction management
- Databases
- Engineering fundamentals
- Government
- Highway transportation
- Information Technology (IT)
- Infrastructure
- Mathematics
- Organizations
- Practice and Profession
- Systems engineering
- Traffic accidents
- Traffic engineering
- Traffic management
- Transportation engineering
- Vehicles
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