Predicting expressway crash frequency using a random effect negative binomial model: A case study in China

Document Type

Article

Publication Date

1-1-2017

Abstract

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.

Identifier

84994051939 (Scopus)

Publication Title

Accident Analysis and Prevention

External Full Text Location

https://doi.org/10.1016/j.aap.2016.10.012

ISSN

00014575

PubMed ID

27764690

First Page

214

Last Page

222

Volume

98

Grant

51208052

Fund Ref

National Natural Science Foundation of China

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