A Joint Econometric Framework For Modeling Crash Counts By Severity
Keywords
Count model; crash count by severity; crash prediction model; joint model; negative binomial model; ordered fractional split model
Abstract
This paper proposes an innovative joint econometric framework for examining total crash count and crash proportion by different crash severity. In our proposed approach, irrespective of the number of crash frequency variables the dimensions to be investigated is ‘two’, offering substantial benefits in terms of parameter stability and computational time as opposed to the traditional multivariate approaches. The proposed model is demonstrated by employing a joint negative binomial-ordered logit fractional split model framework. The empirical analysis is conducted using zonal level crash count data for different crash severity levels from Florida for the year 2015. The results clearly highlight the superiority of the joint model in terms of data fit compared to independent model. The applicability of the proposed framework is demonstrated by generating spatial distribution of predicted motor vehicle crash frequency and predicted crash counts by severity levels.
Publication Date
3-16-2018
Publication Title
Transportmetrica A: Transport Science
Volume
14
Issue
3
Number of Pages
230-255
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1080/23249935.2017.1369469
Copyright Status
Unknown
Socpus ID
85028850006 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85028850006
STARS Citation
Yasmin, Shamsunnahar and Eluru, Naveen, "A Joint Econometric Framework For Modeling Crash Counts By Severity" (2018). Scopus Export 2015-2019. 9189.
https://stars.library.ucf.edu/scopus2015/9189