GIS-based crash hotspot identification: a comparison among mapping clusters and spatial analysis techniques

Abstract

Knowing the locations of traffic crash hotspots can provide us with valuable insights into the root causes of crash occurrence over the area under study. This knowledge helps decision-makers to better assess the risk associated with road crashes and, as a result, help them to propose more effective countermeasures in order to reduce the annual crash rate. Nonetheless, identifying the areas with the highest potential of crash occurrence is a complicated task. In this regard and within this study, five various types of hotspot identification techniques, consisting of Average Nearest Neighbor, Getis-Ord Gi*, Global Moran’s I, kernel density estimation (KDE) and mean centre, were compared to each other, using three different performance measures, including Predictive Accuracy Index (PAI), Recapture Rate Index (RRI) and hit rate. According to the results, the most accurate model with the highest PAI values (PAI = 1.61 and 1.76), Moran’s I, had the third-highest reliability value (RRI = 1.003). On the other hand, while the Gi* method was the most precise and reliable technique with the highest RRI value (RRI = 1.121), it showed the second-lowest accuracy (PAI= 0.83 and 0.74). Overall, it seems that Moran’s I method is superior to other methods in locating hotspots, which is not only the most accurate technique but also precise enough to rely on.