Comparison of Sichel and Negative Binomial Models in Hotspot Identification

The identification of crash hotspots is the critical component of the highway safety management process. Errors in hotspot identification (HSID) may result in the inefficient use of resources for safety improvements. One HSID method that is based on the empirical Bayesian (EB) method has been widely used as an effective approach for identifying crash-prone sites. For the EB method, the negative binomial (NB) model is usually needed for obtaining the EB estimates. Recently, some studies have shown that the Sichel (SI) model can be easily used within the EB modeling framework and potentially yield better EB estimates. The objective of this study is to compare the performance of the two crash prediction models (SI and NB models) in identifying hotspots using the EB method. To accomplish the objective of this study, empirical crash data collected at highway segments in Texas were used to generate simulated crash counts. Three commonly used HSID methods (simple ranking, confidence interval and EB) were applied using simulated data. False positives, false negatives and false identifications were calculated and compared across the methods. The simulation results in this study suggest that the SI-based EB method can consistently provide a better HSID result than the NB-based EB method. Moreover, EB methods yield lowest error percentage among the three HSID methods. This study confirms that the EB technique is an effective method for identifying hazardous sites. Based on the findings in this study, transportation safety researchers are recommended to consider the SI model as an alternative crash prediction model when using the EB approach.