Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 0 of its 34 references that are in the map. First posted on arXiv 11 Jun 2024.
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Citing papers per year: 2024: 3; 2025: 2; 2026: 1.
6 papers.
- 2024Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challengescited by 21
- 2024Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testingcited by 10
- 2024Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarioscited by 6
- 2026Benchmarking Frontier Large Language Models Against Official Crash Database Coding Using Police Crash Narrativescited by 2
- 2025Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)cited by 1
- 2025Domain-Adapted Pre-trained Language Models for Implicit Information Extraction in Crash Narrativescited by 1
- 2026Benchmarking Frontier Large Language Models Against Official Crash Database Coding Using Police Crash Narratives31 Jul 2026
- 2025Domain-Adapted Pre-trained Language Models for Implicit Information Extraction in Crash Narratives10 Oct 2025
- 2025Identification of Potentially Misclassified Crash Narratives using Machine Learning (ML) and Deep Learning (DL)3 Jul 2025
- 2024Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing26 Sep 2024
- 2024Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios1 Aug 2024
- 2024Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges26 Jun 2024