Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics
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Jaradat, Shadi, Taqwa I. Alhadidi, Huthaifa I. Ashqar, Ahmed Hossain, and Mohammed Elhenawy. “Exploring Traffic Crash Narratives in Jordan Using Text Mining Analytics.” 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI), April 2024, 1–6. https://doi.org/10.1109/ICMI60790.2024.10586010.
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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.