  The variability of the clusters generated by clustering techniques in the
domain of latitude and longitude variables of fatal crash data are
significantly unpredictable. This unpredictability, caused by the randomness of
fatal crash incidents, reduces the accuracy of crash frequency (i.e., counts of
fatal crashes per cluster) which is used to measure traffic safety in practice.
In this paper, a quantitative measure of traffic safety that is not
significantly affected by the aforementioned variability is proposed. It
introduces a fatal point -- a segment with the highest frequency of fatality --
concept based on cluster characteristics and detects them by imposing rounding
errors to the hundredth decimal place of the longitude. The frequencies of the
cluster and the cluster's fatal point are combined to construct a low-sensitive
quantitative measure of traffic safety for the cluster. The performance of the
proposed measure of traffic safety is then studied by varying the parameter k
of k-means clustering with the expectation that other clustering techniques can
be adopted in a similar fashion. The 2015 North Carolina fatal crash dataset of
Fatality Analysis Reporting System (FARS) is used to evaluate the proposed
fatal point concept and perform experimental analysis to determine the
effectiveness of the proposed measure. The empirical study shows that the
average traffic safety, measured by the proposed quantitative measure over
several clusters, is not significantly affected by the variability, compared to
that of the standard crash frequency.
