AI for Data Quality Auditing: Detecting Mislabeled Work Zone Crashes Using Large Language Models

AI for Data Quality Auditing: Detecting Mislabeled Work Zone Crashes Using Large Language Models

Ensuring high data quality in traffic crash datasets is critical for effective safety analysis and policymaking. This study presents an AI-assisted framework for auditing crash data integrity by detecting potentially mislabeled records related to construction zone (czone) involvement. A GPT-3.5 mode...

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Bibliographic Details
Main Authors: Shadi Jaradat, Nirmal Acharya, Smitha Shivshankar, Taqwa I. Alhadidi, Mohammad Elhenawy
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Algorithms
Subjects:
misclassification detection
large language models (LLMs)
data quality assurance
work zone crashes
Online Access:https://www.mdpi.com/1999-4893/18/6/317
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https://www.mdpi.com/1999-4893/18/6/317

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