Original scientific paper
https://doi.org/10.7307/ptt.v36i6.784
Exploring and Comparing Spatial Clusters of Pedestrian Night-Time Crashes Based on Different Street Lighting Conditions at County Spatial Unit
František KEKULA
; Czech Technical University in Prague, Faculty of Transportation Sciences
*
Bernard KOSOVEC
; University of Zagreb, Faculty of Transport and Traffic Sciences
Darko BABIĆ
; University of Zagreb, Faculty of Transport and Traffic Sciences
Pavel HRUBEŠ
; Czech Technical University in Prague, Faculty of Transportation Sciences
* Corresponding author.
Abstract
This paper attempts to determine the role of street lighting in the spatial clustering of night-time crashes involving pedestrians in the Republic of Croatia. Five-year (2018–2022) night-time pedestrian crash data were used in conditions with and without street lighting. First, distance-based statistical methods were used to assess the spatial clustering and deviations from complete spatial randomness (CRS) of the crash patterns. Second, the global Moran’s I analysis was conducted to investigate a degree of spatial autocorrelation of the annual crash counts aggregated in 21 counties of Croatia. Finally, the local indicators of spatial association (LISA) were used to identify the locations of the crash count hotspots. The results of the ANND analysis confirm a significant clustering of crashes for both street lighting conditions. However, different global Moran’s I values for both conditions were obtained with a high and statistically significant positive value for the crash counts without street lighting. Local Moran's I analysis reveals that the High-High (H-H) county clusters are located in coastal regions of Croatia, while the Low-Low (L-L) county clusters appear in the East continental part, next to Slavonia. The results suggest that inadequate lighting conditions have an impact on the clustering of pedestrian crashes at night.
Keywords
night-time pedestrian crashes; street lighting; distance-based statistical methods; spatial autocorrelation; global Moran’s I; local Moran’s I
Hrčak ID:
324613
URI
Publication date:
20.12.2024.
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