Fuzzy Cluster-Based Method of Hotspot Detection with Limited Information

In the absence of geometric design and traffic data, hot-spot identification (HSID) is done primarily with crash data only, based on techniques such as crash frequency (CF), fatal crash frequency (FCF), or equivalent property damage only (EPDO), despite the known limitations of these techniques. In this article, the authors propose an improved HSID technique that may be used with crash data only. Using disaggregate crash history information, this method estimates probabilities of crash severities by the major contributing factors using severity models. These probabilities are used to compute expected numbers of severe and fatal crashes at various locations which are then used to classify the locations into two fuzzy cluster, namely hotspot and non-hotspot using Fuzzy C-Means (FCM) algorithm. The identified hotspots are ranked based on their mean departure from the core of the hotspot cluster. These rankings are compared with rankings done using existing techniques namely CF, FCF, EPDO, and Empirical Bayes' (EB). The proposed method is found to be a robust method for hotspot detection with performance better than existing methods that use crash data only and comparable to the EB method.