Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics

TL;DR

This study applies text mining techniques to analyze traffic crash narratives in Jordan, revealing key themes and factors contributing to crashes, and emphasizing the importance of driver education and multifactorial safety approaches.

Contribution

It introduces the use of text mining analytics on traffic crash narratives in Jordan, uncovering patterns and themes to inform safety policies.

Findings

Text mining reveals multifactorial crash causes.

Driver education and awareness are crucial.

Traffic crash patterns involve human and vehicular factors.

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.