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Advancing Traffic Accident Data Analysis with LLMs

This project explored using large language models to turn traffic crash information from unstructured sources like police reports and narrative records into more organized data that was easier to analyze. The work aimed to assess whether these AI-generated outputs could be comparable to established structured databases while enabling analysis across diverse, underused data sources, and to clarify how AI could support transportation safety research through better data preparation and new perspectives on crash trends.

Faculty Adviser(s)

Yinhai Wang, Civil & Environmental Engineering

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