article · Accident Analysis & Prevention
Road safety research frequently examines crash locations, but crash severity patterns and spatial relationships are often overlooked. Comparing three years of collision records from Addis Ababa and Berlin reveals how crash severity clusters across different urban environments. In Addis Ababa, spatial autocorrelation is strong and statistically significant, with severe crashes clustering predominantly on the outskirts, while the central business district and residential neighbourhoods show lower severity groupings. In Berlin, high- and low-severity clusters are more intermingled around the periphery, and fatal crashes do not show significant spatial clustering. Despite these differences, driven by roadway infrastructure, user behaviour, and socio-economic conditions, both cities share a notable tendency for high-severity crashes to concentrate along peripheral borders.
Understanding where severe road crashes concentrate helps urban planners and transport authorities target safety interventions effectively. Rather than treating all road incidents equally, distinguishing between minor and severe incidents highlights specific high-risk zones, such as city outskirts. This geographical insight enables municipal bodies to allocate emergency services, adjust speed enforcement, and redesign roadways according to the severity of local risk.
The research demonstrates an applied analytical method using spatial statistics on municipal crash records. While currently early-stage academic analysis, the approach could be integrated into spatial planning tools or traffic management software used by transport authorities, urban planning consultancies, and municipal road safety agencies. Practical deployment would require translation into automated decision-support systems to assist infrastructure planning and resource allocation.
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Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries. The study used three-year crash data from each city. It employed the average nearest neighbor distance (ANND) method to determine the significance of spatial clustering of crash data by severity, Global Moran's I to examine the statistical significance of spatial autocorrelation, and Local Moran's I to identify significant cluster locations with High-High (HH) and Low-Low (LL) crash severity values. The ANND analysis reveals a significant clustering of crashes by severity in both cities, except in Berlin's fatal crashes. However, different Global Moran's I results were obtained for the two cities, with a strong and statistically significant value for Addis Ababa compared to Berlin. The Local Moran's I result indicates that the central business district and residential areas have LL values, while the city's outskirts exhibit HH values in Addis Ababa. With some persistent HH value locations, Berlin's HH and LL grid clusters are intermingled on the city's periphery. Socio-economic factors, road user behavior and roadway factors contribute to the difference in the result. Nevertheless, it is interesting to note the similarity of significant HH value locations on the outskirts of both cities. Finally, the results are consistent with previous studies and indicate the need for further investigation in other locations.
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DOI: 10.1016/j.aap.2024.107535
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