Preprint Article Version 1 NOT YET PEER-REVIEWED

Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables

  1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China
  2. Guangzhou Expressway Company Limited, Guangzhou 510288, China
Version 1 : Received: 14 August 2016 / Approved: 15 August 2016 / Online: 15 August 2016 (09:47:37 CEST)

How to cite: Zeng, Y.; Wang, X.; Liu, L.; Li, X.; Jiang, C. Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables. Preprints 2016, 2016080144 (doi: 10.20944/preprints201608.0144.v1). Zeng, Y.; Wang, X.; Liu, L.; Li, X.; Jiang, C. Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables. Preprints 2016, 2016080144 (doi: 10.20944/preprints201608.0144.v1).

Abstract

Crash prediction of the sharp horizontal curve segment (SHCS) of a freeway is an important tool in analyzing safety of SHCSs and in building a crash prediction model (CPM). The design and crash report data of 88 SHCSs from different institutions were surveyed and three negative binomial (NB) regression models and three generalized negative binomial (GNB) regression models were built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The study demonstrates the effective use of the GNB model in analyzing the interactive influence of explanatory variables and in predicting freeway basic segments. Traffic volume, highway horizontal radius, and curve length have been formulated as explanatory variables. Subsequently, we performed statistical analysis to determine the model parameters and conducted sensitivity analysis. Among the six models, the result of model 6, which considered interactive influence, is much better than those of the other models by fitting rules. We also compared the actual results from crashes of 88 SHCSs with those predicted by models 1, 3, and 6. Results demonstrate that model 6 is much more reasonable than models 1 and 3.

Subject Areas

freeway; crash prediction model (CPM); sharp horizontal curve segment (SHCS); interactive influence among three explanatory variables; generalized negative binomial (GNB)

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