Exploring Road Traffic Accidents Hotspots Using Clustering Algorithms and GIS-Based Spatial Analysis
This study conducts a comprehensive spatial analysis of road traffic accidents (RTAs) in Najran, a city emblematic of rapid urbanization in Saudi Arabia, which is facing significant public safety challenges due to an increase in vehicular traffic. By means of a dataset from 2022, we explore the spat...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10636153/ |
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| author | Hussien Kamh Saleh H. Alyami Afaq Khattak Mana Alyami Hamad Almujibah |
| author_facet | Hussien Kamh Saleh H. Alyami Afaq Khattak Mana Alyami Hamad Almujibah |
| author_sort | Hussien Kamh |
| collection | DOAJ |
| description | This study conducts a comprehensive spatial analysis of road traffic accidents (RTAs) in Najran, a city emblematic of rapid urbanization in Saudi Arabia, which is facing significant public safety challenges due to an increase in vehicular traffic. By means of a dataset from 2022, we explore the spatial distribution of RTAs across the city’s districts by employing advanced clustering algorithms, including Density-based Spatial Clustering of Applications with Noise (DBSCAN) and Hierarchical Agglomerative Clustering (HAC), as well as GIS-based density analysis, proximity analysis, and spatial interpolation, to unveil accident hotspots and disparities in emergency service coverage. Our findings reveal that <xref ref-type="disp-formula" rid="deqn1">(1)</xref> the HAC model, based on the Silhouette and Calinski-Harabasz Scores, performs better in identifying accident hotspots; <xref ref-type="disp-formula" rid="deqn2">(2)</xref> significant concentrations of accidents are observed along major highways and arterial roads, pinpointing critical hotspots within the city’s fabric; <xref ref-type="disp-formula" rid="deqn3">(3)</xref> proximity analysis indicates gaps in the coverage of ambulance services and public hospitals relative to high-incident areas; <xref ref-type="disp-formula" rid="deqn4">(4)</xref> through spatial interpolation, detailed visualizations of RTA distributions are provided, revealing diverse accident patterns across Najran. The study highlights the critical role of spatial analysis in identifying high-risk areas and provides valuable insights for transport planners and public safety officials, supporting the development of targeted strategies to improve road safety and enhance emergency service responses. |
| format | Article |
| id | doaj-art-affc392dff044fc3be5decc4a586ba74 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-affc392dff044fc3be5decc4a586ba742025-08-20T03:09:09ZengIEEEIEEE Access2169-35362025-01-0113609446095410.1109/ACCESS.2024.344324510636153Exploring Road Traffic Accidents Hotspots Using Clustering Algorithms and GIS-Based Spatial AnalysisHussien Kamh0Saleh H. Alyami1Afaq Khattak2https://orcid.org/0000-0002-3219-4562Mana Alyami3Hamad Almujibah4https://orcid.org/0000-0003-1676-6222Information Systems Department, College of Computer Sciences and Information Systems, Najran University, Najran, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Najran University, Najran, Saudi ArabiaCollege of Transportation Engineering, Tongji University, Shanghai, ChinaDepartment of Civil Engineering, College of Engineering, Najran University, Najran, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Taif University, Taif, Saudi ArabiaThis study conducts a comprehensive spatial analysis of road traffic accidents (RTAs) in Najran, a city emblematic of rapid urbanization in Saudi Arabia, which is facing significant public safety challenges due to an increase in vehicular traffic. By means of a dataset from 2022, we explore the spatial distribution of RTAs across the city’s districts by employing advanced clustering algorithms, including Density-based Spatial Clustering of Applications with Noise (DBSCAN) and Hierarchical Agglomerative Clustering (HAC), as well as GIS-based density analysis, proximity analysis, and spatial interpolation, to unveil accident hotspots and disparities in emergency service coverage. Our findings reveal that <xref ref-type="disp-formula" rid="deqn1">(1)</xref> the HAC model, based on the Silhouette and Calinski-Harabasz Scores, performs better in identifying accident hotspots; <xref ref-type="disp-formula" rid="deqn2">(2)</xref> significant concentrations of accidents are observed along major highways and arterial roads, pinpointing critical hotspots within the city’s fabric; <xref ref-type="disp-formula" rid="deqn3">(3)</xref> proximity analysis indicates gaps in the coverage of ambulance services and public hospitals relative to high-incident areas; <xref ref-type="disp-formula" rid="deqn4">(4)</xref> through spatial interpolation, detailed visualizations of RTA distributions are provided, revealing diverse accident patterns across Najran. The study highlights the critical role of spatial analysis in identifying high-risk areas and provides valuable insights for transport planners and public safety officials, supporting the development of targeted strategies to improve road safety and enhance emergency service responses.https://ieeexplore.ieee.org/document/10636153/Road traffic accidentsspatial analysisNajranSaudi Arabia |
| spellingShingle | Hussien Kamh Saleh H. Alyami Afaq Khattak Mana Alyami Hamad Almujibah Exploring Road Traffic Accidents Hotspots Using Clustering Algorithms and GIS-Based Spatial Analysis IEEE Access Road traffic accidents spatial analysis Najran Saudi Arabia |
| title | Exploring Road Traffic Accidents Hotspots Using Clustering Algorithms and GIS-Based Spatial Analysis |
| title_full | Exploring Road Traffic Accidents Hotspots Using Clustering Algorithms and GIS-Based Spatial Analysis |
| title_fullStr | Exploring Road Traffic Accidents Hotspots Using Clustering Algorithms and GIS-Based Spatial Analysis |
| title_full_unstemmed | Exploring Road Traffic Accidents Hotspots Using Clustering Algorithms and GIS-Based Spatial Analysis |
| title_short | Exploring Road Traffic Accidents Hotspots Using Clustering Algorithms and GIS-Based Spatial Analysis |
| title_sort | exploring road traffic accidents hotspots using clustering algorithms and gis based spatial analysis |
| topic | Road traffic accidents spatial analysis Najran Saudi Arabia |
| url | https://ieeexplore.ieee.org/document/10636153/ |
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