Spatial Analysis of Road Traffic Crash Severity in Limassol: A Data-Driven Framework for Urban Road-Safety Management

Road traffic crashes continue to impose significant human and economic costs worldwide, yet traditional approaches to identifying hazardous locations often rely on simple frequency metrics that fail to capture spatial dependence or account for crash severity. This study addresses these limitations by developing a comprehensive spatial analytics framework for assessing crash severity patterns in the greater Limassol area, Cyprus. The methodology integrates severity-weighted crash values and applies a suite of spatial statistical techniques to detect clustering behaviour and identify micro-scale hotspots. Results reveal strong clustering of crash locations based on Average Nearest Neighbour Distance analysis, while Global Moran’s I shows weak or non-significant global autocorrelation of severity. This pattern indicates that severe crashes do not form broad regional trends but instead appear as highly localized clusters. Local Moran’s I analysis confirms the presence of small but meaningful High-High severity hotspots and several spatial outliers (High-Low and Low-High), typically located along major arterial corridors, network transition points, and mixed-use zones. A comparison of pre- and post-COVID periods shows a substantial reduction in crash frequency and cluster intensity, suggesting measurable benefits from altered mobility patterns and the introduction of automated speed enforcement. The findings demonstrate the value of localized, severity-sensitive spatial analysis for modern road-safety management. The proposed framework offers actionable insights for black-spot identification, targeted interventions, dangerous goods routing, and integration into digital-twin and ITS decision-support environments, supporting data-driven strategies aligned with Vision Zero objectives.