title: Predicting expressway crash frequency using a random effect negative binomial model: A case study in China.
abstract: To investigate the relationship between crash frequency and potential influence factors, the accident data for events occurring on a 50km long expressway in China, including 567 crash records (2006-2008), were collected and analyzed. Both the fixed-length and the homogeneous longitudinal grade methods were applied to divide the study expressway section into segments. A negative binomial (NB) model and a random effect negative binomial (RENB) model were developed to predict crash frequency. The parameters of both models were determined using the maximum likelihood (ML) method, and the mixed stepwise procedure was applied to examine the significance of explanatory variables. Three explanatory variables, including longitudinal grade, road width, and ratio of longitudinal grade and curve radius (RGR), were found as significantly affecting crash frequency. The marginal effects of significant explanatory variables to the crash frequency were analyzed. The model performance was determined by the relative prediction error and the cumulative standardized residual. The results show that the RENB model outperforms the NB model. It was also found that the model performance with the fixed-length segment method is superior to that with the homogeneous longitudinal grade segment method.
authors.affiliation: School of Automobile, Chang'an University, Xi'an, Shaanxi, China. Electronic address: zhuanglinma@chd.edu.cn.
authors.forename: Zhuanglin
authors.initials: Z
authors.lastname: Ma
authors.affiliation: Deppon Logistics Corporation, Shanghai, China.
authors.forename: Honglu
authors.initials: H
authors.lastname: Zhang
authors.affiliation: School of Automobile, Chang'an University, Xi'an, Shaanxi, China; John A. Reif, Jr. Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ, USA.
authors.forename: Steven I-Jy
authors.initials: SI
authors.lastname: Chien
authors.affiliation: Yunnan Transport Research Institute, Kunming, Yunnan, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Wang
authors.affiliation: Center of Transportation Research, The University of Tennessee, Knoxville, TN, USA.
authors.forename: Chunjiao
authors.initials: C
authors.lastname: Dong
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D002681:China; D004779:Environment Design / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D018986:Motor Vehicles / Q000706:statistics & numerical data; D011336:Probability; D012449:Safety
keywords: Crash frequency; Goodness-of-fit; Negative binomial model; Prediction; Random effects negative binomial model