Random Parameter Negative Binomial Models of Interstate Accident Frequencies on Interchange Segment by Interchange Type/Region

Abstract

PURPOSES :The objective was to develop the advanced method which could not explain each observation s specific characteristic in the present negative binomial method that results in under-estimation of the standard error(t-value inflation) and affects the confidence of whole derived results. METHODS :This study dealt with traffic accidents occurring within interchange segment on highway main line with RPNB(Random Parameter Negative Binomial) method that enables to take account of heterogeneity. RESULTS :As a result, AADT and lighting installation type on the road were revealed to have random parameter and in terms of other geometric variables, all were derived as fixed parameter(same effect on every segment). Also, marginal effects were adapted to analyze the relative effects on traffic accidents. CONCLUSIONS :This study proves that RPNB method which considers each observation's specific characteristics is better fitted to the accident data with geometrics. Thus, it is recommended that RPNB model or other methods which could consider the heterogeneity needs to be adapted in accident analysis. Keywords accident model, random parameter negative binomial model, heterogeneity, interchange type, marginal effect

Random Parameter Negative Binomial Model of Signalized Intersections

“…To perform this process, a Halton draw has been imposed. Previous studies [15,16,[24][25][26][27] found that the Halton draw provides a more efficient distribution of draws for numerical integration than random draws.…”

“…The data used in this analysis were from major intersections in the Gyeonggi province, where about 25 percent of the entire Korean population (13 million people) live. Although there have been numerous studies that have attempted to understand factors that influence accident frequencies at intersections in Korea using various statistical modeling methods (linear regression [8], Poisson model [9,10], negative binomial model [11][12][13], and logistic regression [14]), few researchers have used a random parameter count models as another methodological alternative in accident frequencies analysis [15,16].…”