A comparative analysis of intersection hotspot identification: Fixed vs. varying dispersion parameters in negative binomial models

Abstract

Network screening for crash hotspots is the first step in roadway safety management. The empirical Bayes (EB) method has been widely used for ranking sites. In the EB process, the most frequently used model for developing safety performance functions (SPFs) is the negative binomial (NB) regression model, in which the dispersion parameter plays a critical role. There are primarily two forms of the dispersion parameter: fixed and varying. Previous studies showed that SPFs with varying dispersion parameters had better performance in modeling crash estimations, and the Highway Safety Manual has adopted the varying form for segment SPFs. However, a comparative analysis of hotspot identification with fixed and varying dispersion parameters for intersections has not yet been well studied. Moreover, this paper includes a thorough exploration of the varying dispersion parameters with five different functional forms at 1,943 unsignalized intersections in Texas. To evaluate the intersection SPFs, the authors implement three hotspot identification tests in addition to model statistical fit. Two EB approaches with proposed varying dispersion parameters were found superior to the EB approach with a fixed dispersion parameter in hotspot identification. Safety analysts and practitioners are encouraged to consider varying forms of dispersion parameter when analyzing intersection crashes.