Relationship between Interstate Highway Accidents and Heterogeneous Geometrics by Random Parameter Negative Binomial Model - A case of Interstate Highway in Washington State, USA

Abstract

The objective of this study is finding the relationship between interstate highway accident frequencies and geometrics using Random Parameter Negative Binomial model. Even though it is impossible to take account of the same design criteria to the all segments or corridors on the road in reality, previous research estimated the fixed value of coefficients without considering each segment's characteristic. The drawback of the traditional negative binomial is not to explain the integrated variations in terms of time and the distinct characters specific segment has. This results in underestimation of the standard error which inflates the t-value and finally, affects the modeling estimation. Therefore, this study tries to find the relationship of accident frequencies with the heterogeneous geometrics using 9-years and 7-interstate highway data in Washington State area. 16-types of geometrics are used to derive the model which is compared with the traditional negative binomial Model to understand which Model is more suitable. In addition, by calculating marginal effect and elasticity, heterogeneous variables' effect to the accidents are estimated. Hopefully, this study will help to estiblish the future policy of geometrics.

Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model

“…These distribution values mean that the right shoulder width increases severe injury rates in 7.04% of the main line segments and decreases severe injury rates in 92.96% of the main line segments. These results are consistent with previous studies [15,17], which have shown that accident probability decreases as shoulder width increases.…”

“…Negative binomial models had been shown to be more appropriate than the Poisson model for describing the relationships between accident frequency and geometric elements [5][6][7][8]. Besides, a variety of attempts to analyze accident frequencies were made, and these resulted in a reduction in accidents and improved accident prevention (random effects models [9,10], zero-inflated count models [11][12][13], and random parameters count models [14][15][16][17][18]).…”

Development of Accident Frequency Models with Random Parameters on Interstate Roadway Segments with and without Lighting Systems

“…Since it is difficult to numerically integrate the count models with a random parameter distribution, a simulationbased maximum likelihood method was employed, which maximized the simulated log-likelihood function. In this respect, previous studies have shown that the Halton draw method can be used to estimate empirical parameters [18][19][20].…”

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].…”