01123198

Component

https://doi.org/10.3141/2136-10

http://scholar.google.com...anis&publication_year=2009

Traditionally, highway safety analyses have used univariate Poisson or negative binomial distributions to model crash counts for different levels of crash severity. Because unobservables or omitted variables are shared across severity levels, however, crash counts are multivariate in nature. This research uses full Bayes multivariate Poisson lognormal models to estimate the expected crash frequency for different levels of crash severity and then compares those estimates to independent or univariate Poisson lognormal estimates. The multivariate Poisson lognormal model fits better than the univariate model and improves the precision in crash-frequency estimates. The covariances and correlations among crash severities are high (correlations range from 0.47 to 0.97), with the highest values found between contiguous severity levels. Considering this correlation between severity levels improves the precision of the expected number of crashes. The multivariate estimates are used with cost data from the Pennsylvania Department of Transportation to develop the expected crash cost (and excess expected cost) per segment, which is then used to rank sites for safety improvements. The multivariate-based top-ranked segments are found to have consistently higher costs and excess costs than the univariate estimates, which is due to higher multivariate estimates of fatalities and major injuries (due to the random effects parameter). These higher estimated frequencies, in turn, produce different rankings for the multivariate and independent models. The finding of a high correlation between contiguous severity levels is consistent with some of the literature, but additional tests of multivariate models are recommended. The improved precision has important implications for the identification of sites with promise (SWiPs), because one formulation includes the standard deviation of crash frequencies for similar sites as part of the assessment of SWiPs.

01147883

09-1106

English

pp 82-91

2009

Transportation Research Record: Journal of the Transportation Research Board

9780309142656

Print

Maps (1) ; References (26) ; Tables (6)

Crash injuries; Crash rates; Crash severity; Fatalities; Multivariate analysis; Standard deviation; Traffic safety

Accident costs; Accident sites; Bayesian analysis; Poisson lognormal models

Pennsylvania

Data and Information Technology; Highways; Safety and Human Factors; I81: Accident Statistics

TRIS, TRB, ATRI

Jan 30 2009 5:19PM

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• Examining Application of Aggregated and Disaggregated Poisson–Gamma Models Subjected to Low Sample Mean Bias
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• Method of Selection of Poisson-Based Wavelet Shrinkage Sites
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