Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics
- URL: http://arxiv.org/abs/2406.09438v1
- Date: Tue, 11 Jun 2024 20:07:39 GMT
- Title: Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics
- Authors: Shadi Jaradat, Taqwa I. Alhadidi, Huthaifa I. Ashqar, Ahmed Hossain, Mohammed Elhenawy,
- Abstract summary: 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.
Results show that text mining analytics is a promising method and underscore the multifactorial nature of traffic crashes.
- Score: 4.465427147188149
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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