Detecting high-risk road segments using kriging and inverse distance weighted methods Available to Purchase

The detection of high-risk road segments is essential to set priorities in road rehabilitation projects. The ArcGIS tool has been used in previous studies as an alternative tool to identify high-risk segments. However, most tools use crash frequency as a safety performance measure. In this study, the aim is to investigate how ArcGIS tools, specifically kriging and inverse distance weighted (IDW) tools, can be used to find high-risk road segments using crash rate and equivalent property damage only (EPDO) metrics. A road section with a length of 85 km was selected for applying the investigated tools. Crash data with their details were collected from the official police station for 3 years. Kriging and IDW tools were processed by selecting five clusters, from the high-risk to the no-risk levels. The ranks of the segments have been compared with those resulting from the K-mean method using the t-paired test. The test results indicate that kriging and IDW yield uncorrelated segment ranks compared to the K-mean rank. In addition, using these tools produced false positives and false negative cases. According to the K-mean results, 37.8 km is diagnosed as the top highest risk segment, subjected to further crash data analysis to identify the risk factors and recommend suitable countermeasures.