Comparative analysis of GIS aided methods to identify pedestrian crash hotspots in urban networks

GIS has been a popular tool for visualization of crash data and analysis of hotspotsin highway segments and intersections, hence in this study four GIS aidedmethods as hotspot identification methods have been used: K-means clustering,STAC, Nearest neighbor hierarchical clustering, and Kernel density method. Thisstudy used the data of pedestrian related crashes of district 11 of Tehran over ayear. These crashes resulted in 63 individual crash concentration zones for 276pedestrian crashes in the study area. Furthermore, the results of using above methodsshowed although every of these methods have different assumptions andprocedures their outputs are almost similar and do not have any noticeable difference.In the next step, to determine the significance of identified hotspots and validationof the study, based on minimum required values of crash frequency Poissondistribution in four levels was developed. Intersection of Jomhuri and Valiasrstreets and south side area of Qazvin sq. were obtained the most dangerous hotspotshaving 9 pedestrian crashes in a year and less than 0.5 percent occurrenceprobability in normal conditions.

Keywords:

crash , hotspot , pedestrian , GIS aided methods , Poisson distribution