  In this paper, the authors evaluate the performance of a spatial
multiresolution analysis (SMA) method that behaves like a variable bandwidth
kernel density estimation (KDE) method, for hazardous road segments
identification (HRSI) and crash risk (expected number of crashes) estimation.
The proposed SMA, is similar to the KDE method with the additional benefit of
allowing for the bandwidth to be different at different road segments depending
on how homogenous the segments are. Furthermore, the optimal bandwidth at each
road segment is determined solely based on the data by minimizing an unbiased
estimate of the mean square error. The authors compare the SMA method with the
state of the practice crash analysis method, the empirical Bayes (EB) method,
in terms of their HRSI ability and their ability to predict future crashes. The
results indicate that the SMA may outperform the EB method, at least with the
crash data of the entire Virginia interstate network used in this paper. The
SMA is implemented in an Excel spreadsheet that is freely available for
download.
