This study presents an integrated method for identifying an important segment of traffic accidents in an intercity road network. The spatial analysis method is known as network Kernel Density Estimation (KDE). The importance of critical accident rate is that it takes into account several factors including exposure rates, type of road section, variance of accident data, etc. Also, in this study, we compared the results of the road severity index with the Kernel Density Estimation method. The results of the study were obtained for two almost identical models. We found that the key points determined by the Kernel Density Estimation method reflect the severely problematic component segments, and filter out components that are not vulnerable. This approach can help transportation officials and safety professionals prioritize locations that need more safety attention.