TY - JOUR
T1 - Predicting expressway crash frequency using a random effect negative binomial model
T2 - A case study in China
AU - Ma, Zhuanglin
AU - Zhang, Honglu
AU - Chien, Steven I.Jy
AU - Wang, Jin
AU - Dong, Chunjiao
N1 - Funding Information:
This paper was supported by the research projects sponsored by the Natural Science Foundation of China (No. 51208052 ), and the State Scholarship Fund of the China Scholarship Council (No. 201406565054 ).
Publisher Copyright:
© 2016
PY - 2017/1/1
Y1 - 2017/1/1
N2 - To investigate the relationship between crash frequency and potential influence factors, the accident data for events occurring on a 50 km 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.
AB - To investigate the relationship between crash frequency and potential influence factors, the accident data for events occurring on a 50 km 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.
KW - Crash frequency
KW - Goodness-of-fit
KW - Negative binomial model
KW - Prediction
KW - Random effects negative binomial model
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U2 - 10.1016/j.aap.2016.10.012
DO - 10.1016/j.aap.2016.10.012
M3 - Article
C2 - 27764690
AN - SCOPUS:84994051939
SN - 0001-4575
VL - 98
SP - 214
EP - 222
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
ER -