Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics

Shadi Jaradat, Taqwa I. Alhadidi, Huthaifa I. Ashqar, Ahmed Hossain, Mohammed Elhenawy
6/11/2024
cs.CLcs.IR

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

From Narratives to Probabilistic Reasoning: Predicting and Interpreting Drivers' Hazardous Actions in Crashes Using Large Language Model

84% match2025

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques

83% match2025

Data-Driven Analysis of Crash Patterns in SAE Level 2 and Level 4 Automated Vehicles Using K-means Clustering and Association Rule Mining

83% match2025

Advanced Crash Causation Analysis for Freeway Safety: A Large Language Model Approach to Identifying Key Contributing Factors

83% match2025

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

83% match2025

Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach

82% match1 repo22024

Cite this paper

@article{jaradat2024exploring,
  title  = {Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics},
  author = {Shadi Jaradat and Taqwa I. Alhadidi and Huthaifa I. Ashqar and Ahmed Hossain and Mohammed Elhenawy},
  year   = {2024},
  eprint = {2406.09438},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.09438v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2406.09438.svg)](https://codeofpaper.com/paper/2406.09438)

Discussion