Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation
Identifying and prioritizing hazardous road traffic crash locations is an efficient way to mitigate road traffic crashes, treat point locations, and introduce regulations for area-wide changes. A sound method to identify blackspots (BS) and area-wide hotspots (HS) would help increase the precision o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/12/3/85 |
| _version_ | 1827749477984763904 |
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| author | Anteneh Afework Mekonnen Tibor Sipos Nóra Krizsik |
| author_facet | Anteneh Afework Mekonnen Tibor Sipos Nóra Krizsik |
| author_sort | Anteneh Afework Mekonnen |
| collection | DOAJ |
| description | Identifying and prioritizing hazardous road traffic crash locations is an efficient way to mitigate road traffic crashes, treat point locations, and introduce regulations for area-wide changes. A sound method to identify blackspots (BS) and area-wide hotspots (HS) would help increase the precision of intervention, reduce future crash incidents, and introduce proper measures. In this study, we implemented the operational definitions criterion in the Hungarian design guideline for road planning, reducing the huge number of crashes that occurred over three years for the accuracy and simplicity of the analysis. K-means and hierarchical clustering algorithms were compared for the segmentation process. K-means performed better, and it is selected after comparing the two algorithms with three indexes: Silhouette, Davies–Bouldin, and Calinski–Harabasz. The Empirical Bayes (EB) method was employed for the final process of the BS identification. Three BS were identified in Budapest, based on a three-year crash data set from 2016 to 2018. The optimized hotspot analysis (Getis-Ord Gi*) using the Geographic Information System (GIS) technique was conducted. The spatial autocorrelation analysis separates the hotspots, cold spots, and insignificant areas with 95% and 90% confidence levels. |
| first_indexed | 2024-03-11T06:27:43Z |
| format | Article |
| id | doaj.art-13fbf96761684b46aaf9816fe6ba8b70 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-11T06:27:43Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-13fbf96761684b46aaf9816fe6ba8b702023-11-17T11:27:52ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-02-011238510.3390/ijgi12030085Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial AutocorrelationAnteneh Afework Mekonnen0Tibor Sipos1Nóra Krizsik2Department of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Muegyetem rkp.3, 1111 Budapest, HungaryDepartment of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Muegyetem rkp.3, 1111 Budapest, HungaryDepartment of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Muegyetem rkp.3, 1111 Budapest, HungaryIdentifying and prioritizing hazardous road traffic crash locations is an efficient way to mitigate road traffic crashes, treat point locations, and introduce regulations for area-wide changes. A sound method to identify blackspots (BS) and area-wide hotspots (HS) would help increase the precision of intervention, reduce future crash incidents, and introduce proper measures. In this study, we implemented the operational definitions criterion in the Hungarian design guideline for road planning, reducing the huge number of crashes that occurred over three years for the accuracy and simplicity of the analysis. K-means and hierarchical clustering algorithms were compared for the segmentation process. K-means performed better, and it is selected after comparing the two algorithms with three indexes: Silhouette, Davies–Bouldin, and Calinski–Harabasz. The Empirical Bayes (EB) method was employed for the final process of the BS identification. Three BS were identified in Budapest, based on a three-year crash data set from 2016 to 2018. The optimized hotspot analysis (Getis-Ord Gi*) using the Geographic Information System (GIS) technique was conducted. The spatial autocorrelation analysis separates the hotspots, cold spots, and insignificant areas with 95% and 90% confidence levels.https://www.mdpi.com/2220-9964/12/3/85blackspotscluster analysisgeographic information systemhotspotsroad safetyspatial autocorrelation |
| spellingShingle | Anteneh Afework Mekonnen Tibor Sipos Nóra Krizsik Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation ISPRS International Journal of Geo-Information blackspots cluster analysis geographic information system hotspots road safety spatial autocorrelation |
| title | Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation |
| title_full | Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation |
| title_fullStr | Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation |
| title_full_unstemmed | Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation |
| title_short | Identifying Hazardous Crash Locations Using Empirical Bayes and Spatial Autocorrelation |
| title_sort | identifying hazardous crash locations using empirical bayes and spatial autocorrelation |
| topic | blackspots cluster analysis geographic information system hotspots road safety spatial autocorrelation |
| url | https://www.mdpi.com/2220-9964/12/3/85 |
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