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

► Mixed logit models capture population heterogeneity in injury severity. ► Age and gender lead to important population heterogeneity effects. ► Transportation policy must account for increased aging-related population variance. ► Population move towards newer safer vehicles can have a significant safety benefit. ► Key safety benefits from reduced drunk driving, improved street lighting. 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. The other half of the 65+ population had a lower probability of fatal injury. Heterogeneity was also noted in vehicle age, but related to the gender of the driver, with males linked to, on average, a higher probability of fatal injury in a newer vehicle compared with females, all other factors kept constant. These effects lend support to the use of mixed logit models in injury severity research and show age and gender based population heterogeneity. Several other factors were found to significantly increase the probability of fatal injury for drivers in single-vehicle crashes, most notably: male driver, drunk driving, unsafe speed, older driver (65+) driving an older vehicle, and darkness without streetlights.