Hotspot identification considering daily variability of traffic flow and crash record: A case study

Abstract

Hotspots identification (HSID), a reactive crash prediction based on the historical accident counts, is crucial to transport authorities for evaluating the risk level of the object road sites. The objective of the research is to identify unidentified hotspots that should have been treated. Numerous conventional HSID approaches have been developed and applied for decades, none of which takes daily variability of traffic flow and crash record into account. In this regard, we categorize the time of day into four groups: (1) morning peak hours, (2) afternoon peak hours, (3) daytime, and (4) night off-peak hours. The authors further apply this proposed methodology to Pacific Motorway Southeast Queensland section linking Brisbane to Gold Coast based on an Empirical Bayesian (EB) approach. Finally, the applications of these proposed EB-based methods and the conventional EB method are discussed through an aggregated view.