# Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)

arXiv: https://arxiv.org/abs/2507.03066 · HTML: https://mlsanity.com/paper/2507.03066/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 92 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques](https://mlsanity.com/paper/1707.02919/) (2017)
- [Sarcasm Detection in Tweets with BERT and GloVe Embeddings](https://mlsanity.com/paper/2006.11512/) (2020)
- [Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics](https://mlsanity.com/paper/2406.09438/) (2024)

## Built on by (most cited first, 1 of 1)

- [Beyond Accuracy: A Unified Random Matrix Theory Diagnostic Framework for Crash Classification Models](https://mlsanity.com/paper/2602.19528/) (2026)

## Abstract (arXiv)

This research investigates the efficacy of machine learning (ML) and deep learning (DL) methods in detecting misclassified intersection-related crashes in police-reported narratives. Using 2019 crash data from the Iowa Department of Transportation, we implemented and compared a comprehensive set of models, including Support Vector Machine (SVM), XGBoost, BERT Sentence Embeddings, BERT Word Embeddings, and Albert Model. Model performance was systematically validated against expert reviews of potentially misclassified narratives, providing a rigorous assessment of classification accuracy. Results demonstrated that while traditional ML methods exhibited superior overall performance compared to some DL approaches, the Albert Model achieved the highest agreement with expert classifications (73% with Expert 1) and original tabular data (58%). Statistical analysis revealed that the Albert Model maintained performance levels similar to inter-expert consistency rates, significantly outperforming other approaches, particularly on ambiguous narratives. This work addresses a critical gap in transportation safety research through multi-modal integration analysis, which achieved a 54.2% reduction in error rates by combining narrative text with structured crash data. We conclude that hybrid approaches combining automated classification with targeted expert review offer a practical methodology for improving crash data quality, with substantial implications for transportation safety management and policy development.