PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables
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. Preprints2016, 2016080144. https://doi.org/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. https://doi.org/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. Preprints2016, 2016080144. https://doi.org/10.20944/preprints201608.0144.v1
APA Style
Zeng, Y., Wang, X., Liu, L., Li, X., & Jiang, C. (2016). Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables. Preprints. https://doi.org/10.20944/preprints201608.0144.v1
Chicago/Turabian Style
Zeng, Y., Xinwei Li and Caifeng Jiang. 2016 "Statistical Study on Crash Frequency Model Using GNB Models of Freeway Sharp Horizontal Curve Based on Interactive Influence of 3 Explanatory Variables" Preprints. https://doi.org/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.
Keywords
freeway; crash prediction model (CPM); sharp horizontal curve segment (SHCS); interactive influence among three explanatory variables; generalized negative binomial (GNB)
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
