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https://doi.org/10.1080/00045600903550337

has been cited by the following article:

• TITLE: Identifying Vehicular Crash High Risk Locations along Highways via Spatial Autocorrelation Indices and Kernel Density Estimation

  AUTHORS: Azad Abdulhafedh

  KEYWORDS: Spatial Autocorrelation, Kernel Density, Moran’s I, Gi* statistic, Hot Spots Analysis

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.5 No.2, May 10, 2017

  ABSTRACT: Identifying vehicular crash high risk locations along highways is important for understanding the causes of vehicle crashes and to determine effective countermeasures based on the analysis. This paper presents a GIS approach to examine the spatial patterns of vehicle crashes and determines if they are spatially clustered, dispersed, or random. Moran’s I and Getis-Ord Gi* statistic are employed to examine spatial patterns, clusters mapping of vehicle crash data, and to generate high risk locations along highways. Kernel Density Estimation (KDE) is used to generate crash concentration maps that show the road density of crashes. The proposed approach is evaluated using the 2013 vehicle crash data in the state of Indiana. Results show that the approach is efficient and reliable in identifying vehicle crash hot spots and unsafe road locations.