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.