A Generalized Event Count Model for Crash Data Analysis
The investigation of relationships between traffic accidents and relevant factors is important in traffic safety management. Various methods have been developed for the modeling of crash data. In real world scenarios, crash data often display the characteristics of under-, over- or Poisson dispersion. The commonly used models (such as the Poisson and the NB [negative binomial] regression models) have associated limitations to deal with various degrees of dispersion. In light of this, a generalized event count (GEC) model was proposed in this study. This method can be generally used without considering the degrees of dispersion to simplify the process of crash data analysis. This model was applied to case studies using data from highways in Idaho. The results from the GEC model were compared with those from the Poisson regression and the Negative binomial regression models. The cases studies show that the proposed model has good performance for crash data with various degrees of dispersion.
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Supplemental Notes:
- This paper was sponsored by TRB committee ABJ80 Statistical Methods.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Ye, Zhirui
- Xu, Yueru
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Conference:
- Transportation Research Board 94th Annual Meeting
- Location: Washington DC, United States
- Date: 2015-1-11 to 2015-1-15
- Date: 2015
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 15p
- Monograph Title: TRB 94th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Case studies; Crash causes; Crash data; Data analysis; Mathematical models; Regression analysis
- Geographic Terms: Idaho
- Subject Areas: Data and Information Technology; Highways; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01552863
- Record Type: Publication
- Report/Paper Numbers: 15-1326
- Files: TRIS, TRB, ATRI
- Created Date: Feb 5 2015 1:08PM