Development of New Performance Measures Based on Data Mining Weights for Hotspot Identification

Abstract

In this study, new performance measures are proposed for hotspot identification in urban intersections. These measures reflect severity factor weights, which are determined based on data mining. To estimate the severity factor weights of crashes at urban intersections, the study utilizes tree-based random forest (RF) and extreme gradient boosting (XGB) methods. The importance of variables in the severity classification model is standardized and utilized for calculating the score of each crash, which is aggregated into intersections. The aggregated score is used as a dependent variable for the safety performance functions (SPFs) in the network screening process. To illustrate the under-dispersed severity score aggregation data, SPFs that follow the COM-Poisson distribution as well as the negative binomial (NB) are developed. Independent variables in SPFs set up intersection geometry elements that can be collected from online GIS services. Four additional performance measures are proposed, each reflecting a severity weight. Data about a total of 42,513 intersection crashes from 2017 to 2018 in South Korea were collected for crash injury severity analysis. Hotspot identification was performed on 81 intersections, and three consistency tests were conducted to validate the four measures. Tests show that the RF-based weighted N-pred,(RF) and N-EPDO(,)RF have the best consistency. Since the severity factor weights of each crash are reflected, intersections vulnerable to dangerous crashes can be analyzed in more detail. Using this method, it is expected that effective safety improvement project plans can be established with the input of safety managers in the future.