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The variability of the clusters generated by clustering techniques in thedomain of latitude and longitude variables of fatal crash data aresignificantly unpredictable. This unpredictability, caused by the randomness offatal crash incidents, reduces the accuracy of crash frequency (i.e., counts offatal crashes per cluster) which is used to measure traffic safety in practice.In this paper, a quantitative measure of traffic safety that is notsignificantly affected by the aforementioned variability is proposed. Itintroduces a fatal point -- a segment with the highest frequency of fatality --concept based on cluster characteristics and detects them by imposing roundingerrors to the hundredth decimal place of the longitude. The frequencies of thecluster and the cluster's fatal point are combined to construct a low-sensitivequantitative measure of traffic safety for the cluster. The performance of theproposed measure of traffic safety is then studied by varying the parameter kof k-means clustering with the expectation that other clustering techniques canbe adopted in a similar fashion. The 2015 North Carolina fatal crash dataset ofFatality Analysis Reporting System (FARS) is used to evaluate the proposedfatal point concept and perform experimental analysis to determine theeffectiveness of the proposed measure. The empirical study shows that theaverage traffic safety, measured by the proposed quantitative measure overseveral clusters, is not significantly affected by the variability, compared tothat of the standard crash frequency.

A fatal point concept and a low-sensitivity quantitative measure for traffic safety analytics