| Summary: | Traffic accidents are very serious problems for human life and the environment. In road safety, it is crucial to identify the high risk locations to apply proper counter measures. This paper aims at introducing outcomes of a pilot project whose main goal is to develop a GIS based crash analysis system integrated with the quantitative methods for identification of high risk locations on road networks in Turkey. In this concept, traditional hotspot detection methods used in Turkey(crash frequency, rate, and severity) are compared with the spatial statistical methods including Moran's I, GetisOrd G and planar and network kernel density estimation in terms of their sensitivity to spatial characteristics of crash clusters. Many countries use traditional hotspot detection approaches such as crash frequency, crash rate, and crash severity as well as Turkey. In this project, we aimed at obtaining a model including different hotspot identification methods for the safety program of Turkey. In order to obtain the model, many hotspot detection methods will be used and compare stage by stage. In the first stage, the seven methods mentioned above are used and examined. Although some of these methods are compared in couple, there is no study using all these methods together extensively in the literature. Methods validated with a different spatial vantage points. Repetitiveness of hotspots in a seven years period are used to compare the methods. Meanwhile advantages and disadvantages of the methods according to location of hotspots are examined additionally. Results show that using planar KDE with Gi in the junction locations and using planar KDE with Moran's I in the straight road locations could improve the model while determining hotspots.
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