01697891

Component

In safety literature, simulation-based multivariate framework is the most commonly employed approach for analyzing multiple crash frequency dependent variables. The current research effort contributes to literature on crash frequency analysis by suggesting an alternative and mathematically simpler approach for analyzing multiple crash frequency variables for the same study unit. The proposed recasts a multivariate distributional problem as a repeated measure univariate problem. Specifically, the authors employed a simpler panel random parameter based univariate model framework to analyze zonal level crash counts for different crash types. The empirical analysis is based on the traffic analysis zone (TAZ) level crash count data for both motorized and non-motorized crashes from Central Florida for the year 2016. The performance of the proposed framework is compared with the performance of the random parameter multivariate negative binomial model using a host of metrics for estimation sample and hold-out sample. The comparison results show that the proposed model provides better prediction relative to the traditional multivariate approach. In summary, the proposed framework provides improved model results while simplifying the model estimation process.

This paper was sponsored by TRB committee ABJ80 Standing Committee on Statistical Methods.

19-03971

English

9p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

References; Tables

Crash rates; Crash risk forecasting; Data analysis; Highway safety; Traffic analysis zones

Florida

Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2019 Paper #19-03971

TRIS, TRB, ATRI

Dec 7 2018 9:41AM