Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes

Publication date: March 2018 Source:Analytic Methods in Accident Research, Volume 17 Author(s): Chenhui Liu, Mo Zhao, Wei Li, Anuj Sharma Urban midblock crashes are influenced mainly by traffic operation and roadway geometric features. In this paper, 10-year crash data from 1,506 directional urban midblock segments in Nebraska were analyzed using the multivariate random parameters zero-inflated negative binomial model to account for unobserved heterogeneity produced by correlations across segments, correlations across crash collision types, excessive zero crashes, and over dispersion. The multivariate random parameters zero-inflated negative binomial model was superior to many common crash frequency models in terms of both goodness of fit and prediction accuracy. Compared with the multivariate fixed parameters zero-inflated negative binomial model, the multivariate random parameters zero-inflated negative binomial model identified fewer key influencing factors and revealed segment-specific effects of these factors on different crash types. It showed that the number of lanes, annual average daily traffic per lane, and segment length might have non-positive effects on crash frequencies. Segments with a speed limit of 45 mph had fewer crashes than did those with lower speed limits, and there were fewer crashes on the segments in Omaha than on those in Lincoln. It was also found that neither the presence of a shoulder, on-street parking, or one-way traffic, nor lane width...
Source: Analytic Methods in Accident Research - Category: Accident Prevention Source Type: research