01518800

Component

This study estimates a multivariate Poisson-lognormal (MVPLN) model using the New York City pedestrian-vehicle crash data collected from 2002 to 2006. The data is aggregated to census tract level. The MVPLN model overcomes the limitations of the ordinary univariate count models that analyze crashes of different severity level separately and ignores the correlations among different crashes severity levels. In addition, the MVPLN model can capture the general correlation structure in crashes frequency data, and takes account of the over-dispersion in the data, which provides a superior fitting result. A MATLAB code implementing parallel computing is developed to estimate the MVPLN model via a Markov Chain Monte Carlo (MCMC) approach. A comparison study is conducted to compare the model fit of MVPLN, univariate Poisson-lognormal, univariate Poisson and Negative Binomial model, and the estimation results show a better fit of the pedestrian-vehicle crash data.

This paper was sponsored by TRB committee ANB20 Safety Data, Analysis and Evaluation.

01503729

14-2464

English

21p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

References; Tables

Binomial distributions; Crash rates; Crash severity; Markov chains; Monte Carlo method; Pedestrian vehicle crashes; Poisson distributions

New York (New York)

Highways; Pedestrians and Bicyclists; Safety and Human Factors; I81: Accident Statistics

Transportation Research Board Annual Meeting 2014 Paper #14-2464

TRIS, TRB, ATRI

Jan 27 2014 2:52PM