Driver-injury severity in single-vehicle crashes in California: A mixed logit analysis of heterogeneity due to age and gender.

This research develops a mixed logit model of driver-injury severity in single-vehicle crashes in California. The research especially considers the heterogeneous effects of age and gender. Older drivers (65+ years old) were found to have a random parameter with about half the population having a higher probability of a fatal injury given a crash than the comparison group of 25-64 year olds with all other factors than age kept constant.

A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model.

Pedestrian-injury severity has been traditionally modeled with approaches that have assumed that the effect of each variable is fixed across injury observations. This assumption ignores possible unobserved heterogeneity which is likely to be particularly important in pedestrian injuries because unobserved physical health, strength, and behavior may significantly affect the pedestrians' ability to absorb collision forces. To address such unobserved heterogeneity, this research applies a mixed logit model to analyze pedestrian-injury severity in pedestrian-vehicle crashes.

Age and pedestrian injury severity in motor-vehicle crashes: a heteroskedastic logit analysis.

This research explores the injury severity of pedestrians in motor-vehicle crashes. It is hypothesized that the variance of unobserved pedestrian characteristics increases with age. In response, a heteroskedastic generalized extreme value model is used.

Underreporting in traffic accident data, bias in parameters and the structure of injury severity models.

Injury severities in traffic accidents are usually recorded on ordinal scales, and statistical models have been applied to investigate the effects of driver factors, vehicle characteristics, road geometrics and environmental conditions on injury severity. The unknown parameters in the models are in general estimated assuming random sampling from the population. Traffic accident data however suffer from underreporting effects, especially for lower injury severities.

Bivariate ordered-response probit model of driver's and passenger's injury severities in collisions with fixed objects.

A bivariate ordered-response probit model of driver's and most severely injured passenger's severity (IS) in collisions with fixed objects is developed in this study. Exact passenger's IS is not necessarily observed, especially when only most severe injury of the accident and driver's injury are recorded in the police reports. To accommodate passenger IS as well, we explicitly develop a partial observability model of passenger IS in multi-occupant vehicle (HOV).