Citation

Yasmin S, Eluru N. Transportmetrica A: Transp. Sci. 2018; 14(3): 230-255.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/23249935.2017.1369469

PMID

unavailable

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.

Language: en

Keywords

Count model; crash count by severity; crash prediction model; joint model; negative binomial model; ordered fractional split model