Analyzing locations prone to causing road crash injuries and fatalities in Saudi Arabia
Omar ALMUTAIRI
Abstract. This study proposes a systematic approach to analyzing crash data and identifying high-crash areas in Saudi Arabia’s intercity road network. The approach employs a five-stage process involving data preparation, cluster identification using density-based clustering algorithm DBSCAN, predictive modeling for fatalities and injuries, data aggregation, and cluster ranking. This research’s results reveal that nearly a quarter of crashes are concentrated within clusters. The predictive model confirms the expected relationship between vehicle type, time of day, and crash severity. By quantifying excess fatalities and injuries in these clusters, safety analysts can prioritize locations that should receive targeted interventions. This research offers a practical framework for enhancing road safety. By automating the proposed approach and providing clear visualizations, safety analysts can efficiently identify high-risk areas and implement data-driven countermeasures. This study represents a significant step toward reducing crashes and their consequences.
Keywords
Multilevel Modeling, Density-Based Clustering, Countermeasures, Traffic Safety
Published online 2/25/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Omar ALMUTAIRI, Analyzing locations prone to causing road crash injuries and fatalities in Saudi Arabia, Materials Research Proceedings, Vol. 48, pp 1040-1049, 2025
DOI: https://doi.org/10.21741/9781644903414-112
The article was published as article 112 of the book Civil and Environmental Engineering for Resilient, Smart and Sustainable Solutions
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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