Analyzing injury crashes using random-parameter bivariate regression models
This paper proposes a random-parameter bivariate zero-inflated negative binomial (RBZINB) regression model for analyzing the effects of investigated variables on crash frequencies. A Bayesian approach is employed as the estimation method, which has the strength of accounting for the uncertainties related to models and parameter values. The modeling framework has been applied to the bivariate injury crash counts obtained from 1000 intersections in Tennessee over a five-year period. The results reveal that the proposed RBZINB model outperforms other investigated models and provides a superior fit. The proposed RBZINB model is useful in gaining new insights into how crash occurrences are influenced by the risk factors. In addition, the empirical studies show that the proposed RBZINB model has a smaller prediction bias and variance, as well as more accurate coverage probability in estimating model parameters and crash-free probabilities.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23249935
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Supplemental Notes:
- © 2016 Hong Kong Society for Transportation Studies Limited 2016. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Dong, Chunjiao
- Clarke, David B
- Nambisan, Shashi S
- Huang, Baoshan
- Publication Date: 2016-10
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 794-810
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Serial:
- Transportmetrica A: Transport Science
- Volume: 12
- Issue Number: 9
- Publisher: Taylor & Francis
- ISSN: 2324-9935
- EISSN: 2324-9943
- Serial URL: http://www.tandfonline.com/loi/ttra21
Subject/Index Terms
- TRT Terms: Bayes' theorem; Crash injuries; Crash rates; Geometric design; Highway safety; Intersections; Regression analysis
- Uncontrolled Terms: Bivariate analysis
- Geographic Terms: Tennessee
- Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;
Filing Info
- Accession Number: 01608891
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 29 2016 11:12AM