Peter T. Savolainen

TL;DR: This paper summarizes the evolution of research and current thinking as it relates to the statistical analysis of motor-vehicle injury severities, and provides a discussion of future methodological directions.

TL;DR: Estimates of probabilistic models of motorcyclists' injury severities in single- and multi-vehicle crashes show that increasing motorcyclist age is associated with more severe injuries and that collision type, roadway characteristics, alcohol consumption, helmet use, unsafe speed and other variables play significant roles in crash-injury outcomes.

TL;DR: Results of likelihood ratio tests reveal that some of the factors affecting bicyclist injury severity at intersection and non-intersection locations are substantively different and using a common model to jointly estimate impacts on severity at both types of locations may result in biased or inconsistent estimates.

TL;DR: In this article, a random parameters bivariate ordered probit model is estimated to examine factors affecting the degree of injury sustained by drivers involved in angle collisions, and the results demonstrate that injury severity outcomes are correlated for those involved in the same crash.

TL;DR: This study involved the development of multinomial logit models to assess driver injury severity resulting from single-vehicle crashes on rural two-lane highways in Texas and found that females were more likely to sustain injury and older drivers to be critically injured, particularly on curves of smaller radius.