Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.
Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types.
Center for Transportation Research, The University of Tennessee, 600 Henley Street, Knoxville, TN 37996, USA. Electronic address: CDONG5@UTK.EDU.
Chunjiao
C
Dong
Center for Transportation Research, The University of Tennessee, 600 Henley Street, Knoxville, TN 37996, USA.
David B
DB
Clarke
MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, School of Traffic & Transportation, Beijing Jiaotong University, Beijing 100044, China.
Xuedong
X
Yan
Department of Civil & Environmental Engineering, The University of Tennessee, 319 John D. Tickle Building, Knoxville, TN 37996-2321, USA.
Asad
A
Khattak
Department of Civil & Environmental Engineering, The University of Tennessee, 319 John D. Tickle Building, Knoxville, TN 37996-2321, USA.
Baoshan
B
Huang
D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D002947:Cities; D004779:Environment Design; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D012044:Regression Analysis; D012449:Safety / Q000706:statistics & numerical data*; D013714:Tennessee
Crash frequency; Full Bayesian method; Geometric design; MRZINB model