A comparative study of the application of the standard kernel density estimation and network kernel density estimation in crash hotspot identification

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Yue Tang
Michael A Knodler
Mi-Hyun Park

Despite a growing number of studies have claimed the network Kernel Density Estimation (network KDE) a more advanced method for crash hotspot identification than the planar Kernel Density Estimation (planar KDE), few conducted comprehensive study to examine their accuracy and practicality on a large-scale basis (i.e. municipal and county). This research attempted to fill the gap by conducting a comparative study of planar KDE and network KDE using the crash data of Hampden County, Massachusetts from 2009 to 2011. A two-tier planar KDE and a network KDE were implemented using the Kernel Density tool in ESRI ArcGIS 10 and SANET 4.1 developed at University of Tokyo respectively. Results showed that (1) Planar KDE is computationally inexpensive and easily accessed. (2) Both methods yielded virtually similar hotspot patterns but with different rankings of the high crash locations. (3) In identifying specific hotspot locations, network KDE could achieve more accurate results and was more timesaving, although multiple runs of planar KDE identified specific locations as well. Accordingly, several suggestions were made for crash hotspot analysis: (1) Since KDE takes the interrelationship among crashes into consideration, it is a more statistically sound approach than traditional methods in crash hotspot identification and can be widely adopted by state and local agencies for initiating safety improvement projects. (2) Planar KDE is recommended to identify general hotspot patterns on large-scale basis for its practicality and efficiency. (3) Network KDE is recommended to identify specific intersections and roadway segments for accuracy.

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