  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.
