# Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics

arXiv: https://arxiv.org/abs/2406.09438 · HTML: https://mlsanity.com/paper/2406.09438/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 34 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Built on by (most cited first, 6 of 6)

- [Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges](https://mlsanity.com/paper/2407.00092/) (2024)
- [Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing](https://mlsanity.com/paper/2409.18286/) (2024)
- [Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios](https://mlsanity.com/paper/2408.00948/) (2024)
- [Benchmarking Frontier Large Language Models Against Official Crash Database Coding Using Police Crash Narratives](https://mlsanity.com/paper/2607.29064/) (2026)
- [Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)](https://mlsanity.com/paper/2507.03066/) (2025)
- [Domain-Adapted Pre-trained Language Models for Implicit Information Extraction in Crash Narratives](https://mlsanity.com/paper/2510.09434/) (2025)

## Abstract (arXiv)

This study explores traffic crash narratives in an attempt to inform and enhance effective traffic safety policies using text-mining analytics. Text mining techniques are employed to unravel key themes and trends within the narratives, aiming to provide a deeper understanding of the factors contributing to traffic crashes. This study collected crash data from five major freeways in Jordan that cover narratives of 7,587 records from 2018-2022. An unsupervised learning method was adopted to learn the pattern from crash data. Various text mining techniques, such as topic modeling, keyword extraction, and Word Co-Occurrence Network, were also used to reveal the co-occurrence of crash patterns. Results show that text mining analytics is a promising method and underscore the multifactorial nature of traffic crashes, including intertwining human decisions and vehicular conditions. The recurrent themes across all analyses highlight the need for a balanced approach to road safety, merging both proactive and reactive measures. Emphasis on driver education and awareness around animal-related incidents is paramount.